Avoid DrWatson in examples

DrWatson is too opinionated and doesn't really fit our use case very
much.

Makefile

@@ -31,19 +31,15 @@ test/Manifest.toml: test/Project.toml ../scripts/installorg.jl
 	@touch $@
 
 
-docs/Manifest.toml: test/Manifest.toml
-	cp test/*.toml docs/
-
-
 devrepl:  ## Start an interactive REPL for testing and building documentation
 	$(JULIA) --project=test --banner=no --startup-file=yes -i devrepl.jl
 
 
-docs: docs/Manifest.toml  ## Build the documentation
+docs: test/Manifest.toml  ## Build the documentation
 	$(JULIA) --project=test docs/make.jl
 	@echo "Done. Consider using 'make devrepl'"
 
-servedocs: docs/Manifest.toml  ## Build (auto-rebuild) and serve documentation at PORT=8000
+servedocs: test/Manifest.toml  ## Build (auto-rebuild) and serve documentation at PORT=8000
 	$(JULIA) --project=test -e 'include("devrepl.jl"); servedocs(port=$(PORT), verbose=true)'
 
 clean: ## Clean up build/doc/testing artifacts

devrepl.jl

@@ -37,8 +37,6 @@ end
 
 if !isfile(joinpath("test", "Manifest.toml"))
     _instantiate()
-    cp(joinpath("test", "Project.toml"), joinpath("docs", "Project.toml"); force=true)
-    cp(joinpath("test", "Manifest.toml"), joinpath("docs", "Manifest.toml"); force=true)
 end
 include("test/init.jl")
 

docs/make.jl

@@ -8,7 +8,7 @@ using Plots
 gr()
 ENV["GKSwstype"] = "100"
 
-include(joinpath(@__DIR__, "download_dumps.jl"))
+include(joinpath("..", "test", "download_dumps.jl"))
 
 # Generate examples
 include("generate.jl")

examples/perfect_entanglers.jl

@@ -40,8 +40,9 @@
 #nb # \newcommand{Im}[0]{\operatorname{Im}}
 #nb # $
 
-using DrWatson
-@quickactivate "KrotovTests"
+const PROJECTDIR = dirname(Base.active_project());
+projectdir(names...) = joinpath(PROJECTDIR, names...);
+datadir(names...) = projectdir("data", names...);
 #jl using Test; println("")
 
 # This example illustrates the optimization towards a perfectly entangling
@@ -134,8 +135,8 @@ using QuantumControl.Shapes: flattop
 function guess_pulses(; T=400ns, E₀=35MHz, dt=0.1ns, t_rise=15ns)
 
     tlist = collect(range(0, T, step=dt))
-    Ωre = t -> E₀ * flattop(t, T=T, t_rise=t_rise)
-    Ωim = t -> 0.0
+    Ωre(t) = E₀ * flattop(t, T=T, t_rise=t_rise)
+    Ωim(t) = 0.0
 
     return tlist, Ωre, Ωim
 
@@ -225,7 +226,7 @@ objectives = [Objective(; initial_state=Ψ, generator=H) for Ψ ∈ basis];
 # the Weyl chamber. Since the logical subspace defining the qubit is embedded
 # in the larger Hilbert space of the transmon, there may be loss of population
 # from the logical subspace. To counter this possibility in the optimization,
-# we add a unitarity measure  to $D_PE$. The two terms are added with equal
+# we add a unitarity measure  to $D_{PE}$. The two terms are added with equal
 # weight.
 
 using TwoQubitWeylChamber: D_PE, gate_concurrence, unitarity
@@ -391,7 +392,7 @@ gate_concurrence(U_opt)
 # concurrence and (as above) a unitarity measure to penalize loss of population
 # from the logical subspace:
 
-J_T_C = U -> 0.5 * (1 - gate_concurrence(U)) + 0.5 * (1 - unitarity(U));
+J_T_C(U) = 0.5 * (1 - gate_concurrence(U)) + 0.5 * (1 - unitarity(U));
 
 # In the optimization, we will convert this functional to one that takes the
 # propagated states as arguments (via the `gate_functional` routine).

examples/rho_3states.jl

@@ -8,6 +8,7 @@
 #md #     Python package](https://qucontrol.github.io/krotov/v1.2.1/notebooks/06_example_3states.html).
 
 #md # ``\gdef\op#1{\hat{#1}}``
+#md # ``\gdef\ketbra#1#2{\vert#1\rangle\langle#2\vert}``
 #md # ``\gdef\init{\text{init}}``
 #md # ``\gdef\tgt{\text{tgt}}``
 
@@ -24,6 +25,7 @@
 #nb # \newcommand{rwa}[0]{\text{rwa}}
 #nb # \newcommand{bra}[1]{\langle#1\vert}
 #nb # \newcommand{ket}[1]{\vert#1\rangle}
+#nb # \newcommand{ketbra}[2]{\vert#1\rangle\langle#2\vert}
 #nb # \newcommand{Bra}[1]{\left\langle#1\right\vert}
 #nb # \newcommand{Ket}[1]{\left\vert#1\right\rangle}
 #nb # \newcommand{Braket}[2]{\left\langle #1\vphantom{#2}\mid{#2}\vphantom{#1}\right\rangle}
@@ -47,9 +49,10 @@
 # quantum system, where the dynamics is governed by the Liouville-von Neumann
 # equation.
 
-using DrWatson
-@quickactivate "KrotovTests"
-#-
+const PROJECTDIR = dirname(Base.active_project())
+projectdir(names...) = joinpath(PROJECTDIR, names...)
+datadir(names...) = projectdir("data", names...)
+
 using QuantumControl
 using LinearAlgebra
 using Serialization
@@ -134,8 +137,8 @@ end
 
 const T = 400ns;
 
-Ωre = t -> 35MHz * QuantumControl.Shapes.flattop(t; T=T, t_rise=20ns);
-Ωim = t -> 0.0;
+Ωre(t) = 35MHz * QuantumControl.Shapes.flattop(t; T=T, t_rise=20ns);
+Ωim(t) = 0.0;
 
 L = transmon_liouvillian(Ωre, Ωim);
 
@@ -147,15 +150,15 @@ function plot_control(pulse::Vector, tlist)
     plot(tlist, pulse, xlabel="time", ylabel="amplitude", legend=false)
 end
 
-plot_control(ϵ::T, tlist) where {T<:Function} = plot_control([ϵ(t) for t in tlist], tlist);
+plot_control(ϵ::Function, tlist) = plot_control([ϵ(t) for t in tlist], tlist);
 #-
 fig = plot_control(Ωre, tlist)
 #jl display(fig)
 
 
 # ## Optimization objectives
 
-# Our target gate is $\Op{O} = \sqrt{\text{iSWAP}}$:
+# Our target gate is $\op{O} = \sqrt{\text{iSWAP}}$:
 
 SQRTISWAP = [
     1  0    0   0
@@ -166,16 +169,16 @@ SQRTISWAP = [
 
 # The key idea explored in the paper is that a set of three density matrices is sufficient to track the optimization
 #
-# $$
+# ```math
 # \begin{align}
-# \Op{\rho}_1
+# \op{\rho}_1
 #     &= \sum_{i=1}^{d} \frac{2 (d-i+1)}{d (d+1)} \ketbra{i}{i} \\
-# \Op{\rho}_2
+# \op{\rho}_2
 #     &= \sum_{i,j=1}^{d} \frac{1}{d} \ketbra{i}{j} \\
-# \Op{\rho}_3
+# \op{\rho}_3
 #     &= \sum_{i=1}^{d} \frac{1}{d} \ketbra{i}{i}
 # \end{align}
-# $$
+# ```
 
 # In our case, $d=4$ for a two qubit-gate, and the $\ket{i}$, $\ket{j}$ are the canonical basis states $\ket{00}$, $\ket{01}$, $\ket{10}$, $\ket{11}$
 
@@ -212,12 +215,12 @@ const ρ̂₃_tgt = sum([(1 / d) * basis_tgt[i] * adjoint(basis_tgt[i]) for i 
 # The three density matrices play different roles in the optimization, and, as
 # shown in the paper, convergence may improve significantly by weighing the
 # states relatively to each other. For this example, we place a strong emphasis
-# on the optimization $\Op{\rho}_1 \rightarrow \Op{O}^\dagger \Op{\rho}_1
-# \Op{O}$, by a factor of 20. This reflects that the hardest part of the
+# on the optimization $\op{\rho}_1 \rightarrow \op{O}^\dagger \op{\rho}_1
+# \op{O}$, by a factor of 20. This reflects that the hardest part of the
 # optimization is identifying the basis in which the gate is diagonal. We will
 # be using the real-part functional ($J_{T,\text{re}}$) to evaluate the success
-# of $\Op{\rho}_i \rightarrow \Op{O}\Op{\rho}_i\Op{O}^\dagger$. Because
-# $\Op{\rho}_1$ and $\Op{\rho}_3$ are mixed states, the Hilbert-Schmidt overlap
+# of $\op{\rho}_i \rightarrow \op{O}\op{\rho}_i\op{O}^\dagger$. Because
+# $\op{\rho}_1$ and $\op{\rho}_3$ are mixed states, the Hilbert-Schmidt overlap
 # will take values smaller than one in the optimal case. To compensate, we
 # divide the weights by the purity of the respective states.
 #
@@ -259,10 +262,10 @@ function as_matrix(ρ⃗)
     return reshape(ρ⃗, N, N)
 end;
 
-pop00 = ρ⃗ -> real(tr(as_matrix(ρ⃗) * ρ̂₀₀));
-pop01 = ρ⃗ -> real(tr(as_matrix(ρ⃗) * ρ̂₀₁));
-pop10 = ρ⃗ -> real(tr(as_matrix(ρ⃗) * ρ̂₁₀));
-pop11 = ρ⃗ -> real(tr(as_matrix(ρ⃗) * ρ̂₁₁));
+pop00(ρ⃗) = real(tr(as_matrix(ρ⃗) * ρ̂₀₀));
+pop01(ρ⃗) = real(tr(as_matrix(ρ⃗) * ρ̂₀₁));
+pop10(ρ⃗) = real(tr(as_matrix(ρ⃗) * ρ̂₁₀));
+pop11(ρ⃗) = real(tr(as_matrix(ρ⃗) * ρ̂₁₁));
 
 
 rho_00_expvals = propagate_objective(
@@ -298,6 +301,3 @@ opt_result = @optimize_or_load(
     method = :krotov
 )
 #-
-opt_result
-
-# ## Optimization result

examples/simple_state_to_state.jl

@@ -47,9 +47,10 @@
 # simple canonical optimization problem: the transfer of population in a two
 # level system.
 
-using DrWatson
-@quickactivate "KrotovTests"
-#-
+const PROJECTDIR = dirname(Base.active_project())
+projectdir(names...) = joinpath(PROJECTDIR, names...)
+datadir(names...) = projectdir("data", names...)
+
 using QuantumControl
 using QuantumPropagators.Controls: substitute
 using LinearAlgebra
@@ -112,7 +113,7 @@ function plot_control(pulse::Vector, tlist)
     plot(tlist, pulse, xlabel="time", ylabel="amplitude", legend=false)
 end
 
-plot_control(ϵ::T, tlist) where {T<:Function} = plot_control([ϵ(t) for t in tlist], tlist);
+plot_control(ϵ::Function, tlist) = plot_control([ϵ(t) for t in tlist], tlist);
 #-
 fig = plot_control(ϵ, tlist)
 #jl display(fig)

examples/state_to_state_parametrizations.jl

@@ -43,9 +43,10 @@
 # This example illustrates the parametrization of control pulses as a
 # form of amplitude constraint.
 
-using DrWatson
-@quickactivate "KrotovTests"
-#-
+const PROJECTDIR = dirname(Base.active_project())
+projectdir(names...) = joinpath(PROJECTDIR, names...)
+datadir(names...) = projectdir("data", names...)
+
 using QuantumControl
 using QuantumControl.Shapes: flattop
 using QuantumControl.Generators

test/Project.toml

@@ -1,5 +1,3 @@
-name = "KrotovTests"
-
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
@@ -8,7 +6,6 @@ Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
 Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
-DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1"
 GRAPE = "6b52fcaf-80fe-489a-93e9-9f92080510be"
 GRAPELinesearchAnalysis = "290eba36-e2d8-4488-81b6-f66cc44f2186"
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"

test/download_dumps.jl

@@ -1,22 +1,25 @@
-using DrWatson
-@quickactivate "KrotovTests"
 import Downloads
 
+const PROJECTDIR = dirname(Base.active_project())
+projectdir(names...) = joinpath(PROJECTDIR, names...)
+datadir(names...) = projectdir("data", names...)
+
+
 DOWNLOADS = Dict(
     "https://github.com/JuliaQuantumControl/Krotov.jl/raw/data-dump/DissGateOCT%23J_T%3DJ_T_re%23iter_stop%3D3000%23method%3Dkrotov.jld2" =>
-        joinpath(datadir(), "DissGateOCT#J_T=J_T_re#iter_stop=3000#method=krotov.jld2"),
+        datadir("DissGateOCT#J_T=J_T_re#iter_stop=3000#method=krotov.jld2"),
     "https://github.com/JuliaQuantumControl/Krotov.jl/raw/data-dump/parametrization%23opt_result_logisticsq.jld2" =>
-        joinpath(datadir(), "parametrization#opt_result_logisticsq.jld2"),
+        datadir("parametrization#opt_result_logisticsq.jld2"),
     "https://github.com/JuliaQuantumControl/Krotov.jl/raw/data-dump/parametrization%23opt_result_positive.jld2" =>
-        joinpath(datadir(), "parametrization#opt_result_positive.jld2"),
+        datadir("parametrization#opt_result_positive.jld2"),
     "https://github.com/JuliaQuantumControl/Krotov.jl/raw/data-dump/parametrization%23opt_result_tanh.jld2" =>
-        joinpath(datadir(), "parametrization#opt_result_tanh.jld2"),
+        datadir("parametrization#opt_result_tanh.jld2"),
     "https://github.com/JuliaQuantumControl/Krotov.jl/raw/data-dump/parametrization%23opt_result_tanhsq.jld2" =>
-        joinpath(datadir(), "parametrization#opt_result_tanhsq.jld2"),
+        datadir("parametrization#opt_result_tanhsq.jld2"),
     "https://github.com/JuliaQuantumControl/Krotov.jl/raw/data-dump/PE_OCT.jld2" =>
-        joinpath(datadir(), "PE_OCT.jld2"),
+        datadir("PE_OCT.jld2"),
     "https://github.com/JuliaQuantumControl/Krotov.jl/raw/data-dump/PE_OCT_direct.jld2" =>
-        joinpath(datadir(), "PE_OCT_direct.jld2"),
+        datadir("PE_OCT_direct.jld2"),
 )
 
 function download_dump(url, destination; force=false, verbose=true)

test/init.jl

@@ -8,7 +8,10 @@ using LiveServer: LiveServer, serve, servedocs as _servedocs
 using Term
 include(joinpath(@__DIR__, "clean.jl"))
 
-servedocs(; kwargs...) = _servedocs(; skip_dirs=["docs/src/examples"], kwargs...)
+function servedocs(; kwargs...)
+    clean()
+    _servedocs(; skip_dirs=["docs/src/examples"], kwargs...)
+end
 
 REPL_MESSAGE = """
 *******************************************************************************

test/runtests.jl

@@ -4,9 +4,9 @@ using Plots
 
 unicodeplots()
 
-include(joinpath(@__DIR__, "generate_example_tests.jl"))
+include("generate_example_tests.jl")
 
-include(joinpath(@__DIR__, "download_dumps.jl"))
+include("download_dumps.jl")
 
 # Note: comment outer @testset to stop after first @safetestset failure
 @time @testset verbose = true "Krotov.jl Package" begin