Switch plotting backend from PythonCall to CairoMakie

Author: GiggleLiu

.ci/Project.toml

@@ -5,7 +5,6 @@ version = "0.1.0"
 
 [deps]
 Comonicon = "863f3e99-da2a-4334-8734-de3dacbe5542"
-CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
 CoverageTools = "c36e975a-824b-4404-a568-ef97ca766997"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"

.ci/src/example.jl

@@ -141,15 +141,13 @@ in parallel.
     example_dir = root_dir("examples")
     script = """
     using Pkg
-    using CondaPkg
     using Literate
     for name in readdir(\"$example_dir\")
         project_dir = joinpath(\"$example_dir\", name)
         isdir(project_dir) || continue
 
         Pkg.activate(project_dir)
         Pkg.instantiate()
-        CondaPkg.resolve()
 
         @info "building" project_dir
         Literate.$target(

CondaPkg.toml

@@ -9,11 +9,11 @@ BloqadeLattices = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe4"
 BloqadeMIS = "bd27d05e-4ce1-5e74-84dd-c5d7d508bbe2"
 BloqadeODE = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe5"
 BloqadeWaveforms = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe7"
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Yao = "5872b779-8223-5990-8dd0-5abbb0748c8c"
 YaoSubspaceArrayReg = "bd27d05e-4ce1-5e79-84dd-c5d7d508ade2"
@@ -29,7 +29,6 @@ ColorSchemes = "3"
 Colors = "0.12"
 ForwardDiff = "0.10"
 Measurements = "2"
-PythonCall = "0.8,0.9"
 Reexport = "1"
 Yao = "0.9"
 YaoSubspaceArrayReg = "0.2"

Project.toml

@@ -16,7 +16,6 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 Yao = "5872b779-8223-5990-8dd0-5abbb0748c8c"
 YaoAPI = "0843a435-28de-4971-9e8b-a9641b2983a8"

docs/Project.toml

@@ -57,8 +57,6 @@ Built on top of Ubuntu Server 20.04 LTS, this image includes
 - [Yao.jl](https://yaoquantum.org/)
 - [Revise.jl](https://github.com/timholy/Revise.jl)
 - [BenchmarkTools.jl](https://juliaci.github.io/BenchmarkTools.jl/stable/)
-- [PythonCall.jl](https://cjdoris.github.io/PythonCall.jl/stable/) 
-- Conda package manager, provided by [Miniconda](https://docs.conda.io/en/latest/miniconda.html) 
 
 #### Bloqade CUDA AMI
 
@@ -227,8 +225,6 @@ Built on top of Ubuntu Server 20.04 LTS, this image includes
 - [Yao.jl](https://yaoquantum.org/)
 - [Revise.jl](https://github.com/timholy/Revise.jl)
 - [BenchmarkTools.jl](https://juliaci.github.io/BenchmarkTools.jl/stable/)
-- [PythonCall.jl](https://cjdoris.github.io/PythonCall.jl/stable/)
-- Conda package manager, provided by [Mamba](https://mamba.readthedocs.io/en/latest/index.html)
 - Jupyter Lab interface with dedicated Julia and Python kernels
 - Integrated Terminal for interactive command-line sessions
 

docs/src/install.md

@@ -17,14 +17,11 @@ which is a created by providing a callable object and a real number `duration`:
 Bloqade gives users the flexibility to specify general waveforms by inputting functions. The following code constructs a sinusoidal waveform with a time duration of 2 μs:
 
 ```@example waveform
-using Bloqade
-using PythonCall
-plt = pyimport("matplotlib.pyplot")
+using Bloqade, Bloqade.CairoMakie
 waveform = Waveform(t->2.2*2π*sin(2π*t), duration = 2);
 Bloqade.plot(waveform)
 ```
-In our documentation, we use the
-python package [`matplotlib`](https://matplotlib.org) for plotting.
+In our documentation, we use the [`CairoMakie`](https://docs.makie.org/) for plotting.
 
 Bloqade supports built-in waveforms for convenience (see References below). 
 For example, the codes below create different waveform shapes with a single line:
@@ -63,7 +60,9 @@ wf1 = piecewise_linear(;clocks, values=values1);
 values2 = 2π*rand(length(clocks)-1)
 wf2 = piecewise_constant(;clocks, values=values2); 
 
-fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), ncols=2)
+fig = Figure(size=(960, 400))
+ax1 = Axis(fig[1, 1])
+ax2 = Axis(fig[1, 2])
 Bloqade.plot!(ax1, wf1)
 Bloqade.plot!(ax2, wf2)
 fig
@@ -104,7 +103,9 @@ waveform:
 wf = piecewise_linear(clocks=[0.0, 2.0, 3.0, 4.0], values=2π*[0.0, 3.0, 1.1, 2.2]);
 swf = smooth(wf;kernel_radius=0.1);
 
-fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), ncols=2)
+fig = Figure(size=(960, 400))
+ax1 = Axis(fig[1, 1])
+ax2 = Axis(fig[1, 2])
 Bloqade.plot!(ax1, wf)
 Bloqade.plot!(ax2, swf)
 fig
@@ -120,7 +121,9 @@ wf2 = sinusoidal(duration = 2.2, amplitude = 2.2*2π);
 wf3 = wf1 + wf2; 
 wf4 = wf1 - wf2;
 
-fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), ncols=2)
+fig = Figure(size=(960, 400))
+ax1 = Axis(fig[1, 1])
+ax2 = Axis(fig[1, 2])
 Bloqade.plot!(ax1, wf3)
 Bloqade.plot!(ax2, wf4)
 fig
@@ -133,7 +136,9 @@ To increase the strength of a waveform by some factors, we can directly use `*`:
 wf = linear_ramp(;duration=2.2, start_value=0.0, stop_value=1.0*2π);
 wf_t = 3 * wf;
 
-fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), ncols=2)
+fig = Figure(size=(960, 400))
+ax1 = Axis(fig[1, 1])
+ax2 = Axis(fig[1, 2])
 Bloqade.plot!(ax1, wf)
 Bloqade.plot!(ax2, wf_t)
 fig
@@ -144,7 +149,9 @@ Such operations can also be broadcasted by using `.*`:
 ```@example waveform
 wf2, wf3 = [2.0, 3.0] .* wf1; 
 
-fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), ncols=2)
+fig = Figure(size=(960, 400))
+ax1 = Axis(fig[1, 1])
+ax2 = Axis(fig[1, 2])
 Bloqade.plot!(ax1, wf2)
 Bloqade.plot!(ax2, wf3)
 fig

docs/src/waveform.md

@@ -7,7 +7,6 @@ BloqadeMIS = "bd27d05e-4ce1-5e74-84dd-c5d7d508bbe2"
 BloqadeODE = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe5"
 BloqadeWaveforms = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe7"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 Yao = "5872b779-8223-5990-8dd0-5abbb0748c8c"
 YaoArrayRegister = "e600142f-9330-5003-8abb-0ebd767abc51"

examples/1.blockade/Project.toml

@@ -345,26 +345,29 @@ for _ in TimeChoiceIterator(integrator2, 0.0:dt:Tmax)
 end
 
 # Plot the data:
-using PythonCall # Use matplotlib to generate plots
-matplotlib = pyimport("matplotlib")
-plt = pyimport("matplotlib.pyplot")
-
-ax = plt.subplot(1, 1, 1)
-plt.plot(times, real(densities), "k", label = "Full space")
-plt.plot(times, real(densities2), "r--", label = "Subspace")
-ax.axis([0, Tmax, 0, 0.45])
-plt.xlabel("Time (us)")
-plt.ylabel("Rydberg density")
-plt.tight_layout()
-plt.legend()
-
-inset_axes = pyimport("mpl_toolkits.axes_grid1.inset_locator")
-ax2 = inset_axes.inset_axes(ax, width = "20%", height = "30%", loc = "lower right", borderpad = 1)
-plt.plot(times, real(densities - densities2))
-plt.axis([0, 0.5, -0.001, 0.003])
-plt.ylabel("Difference", fontsize = 12)
-plt.yticks(LinRange(-0.001, 0.003, 5), fontsize = 12);
-plt.xticks([0, 0.2, 0.4, 0.6], fontsize = 12);
+using Bloqade.CairoMakie # Use CairoMakie to generate plots
+
+fig = Figure()
+ax = Axis(fig[1, 1], xlabel = "Time (us)", ylabel = "Rydberg density", limits=(0.0, Tmax, 0.0, 0.45))
+lines!(ax, times, real(densities), label = "Full space", color=:black)
+lines!(ax, times, real(densities2), label = "Subspace", color=:red, linestyle=:dash)
+axislegend(ax, position = :rt)
+fig
+
+# The inset axis
+ax2 = Axis(fig[1, 1],
+    width=Relative(0.2),
+    height=Relative(0.3),
+    halign=0.9,
+    valign=0.1,
+    limits=(0.0, 0.5, -0.001, 0.003),
+    ylabel="Difference",
+    yticks=LinRange(-0.001, 0.003, 5),
+    xticks=[0, 0.2, 0.4, 0.6],
+    backgroundcolor=:lightgray)
+
+lines!(ax2, times, real(densities - densities2), color = :black)
+fig;
 
 # ![RydbergBlockadeSubspace](../../../assets/RydbergBlockadeSubspace.png)
 

examples/1.blockade/main.jl

@@ -7,7 +7,6 @@ BloqadeMIS = "bd27d05e-4ce1-5e74-84dd-c5d7d508bbe2"
 BloqadeODE = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe5"
 BloqadeWaveforms = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe7"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 YaoSubspaceArrayReg = "bd27d05e-4ce1-5e79-84dd-c5d7d508ade2"
 
 [extras]

examples/2.adiabatic/Project.toml

@@ -19,12 +19,10 @@
 # Let's start by importing the required libraries:
 
 using Bloqade
-using PythonCall
+using Bloqade.CairoMakie
 using KrylovKit
 using SparseArrays
 
-plt = pyimport("matplotlib.pyplot");
-
 # # Ground State Properties
 
 # We start by probing the ground state properties of the Rydberg Hamiltonian in a 1D system. 
@@ -59,21 +57,15 @@ end
 
 # To compare, we first plot the density profile when ``\Delta= -2π \times 10`` MHz: 
 
-fig, ax = plt.subplots(figsize = (10, 4))
-ax.bar(1:nsites, density_g[1, :])
-ax.set_xticks(1:nsites)
-ax.set_xlabel("Sites")
-ax.set_ylabel("Rydberg density")
-ax.set_title("Density Profile: 1D Chain, Δ = -2π * 10 MHz")
+fig = Figure(size=(800, 320))
+ax = Axis(fig[1, 1], xlabel = "Sites", ylabel = "Rydberg density", title = "Density Profile: 1D Chain, Δ = -2π * 10 MHz", xticks = 1:nsites)
+barplot!(ax, 1:nsites, density_g[1, :])
 fig
 
 # We can see that the Rydberg densities in this case are close to 0 for all sites. In contrast, for ``\Delta= 2π \times 10`` MHz, the density shows a clear ``Z_2`` ordered profile:
-fig, ax = plt.subplots(figsize = (10, 4))
-ax.bar(1:nsites, density_g[30, :])
-ax.set_xticks(1:nsites)
-ax.set_xlabel("Sites")
-ax.set_ylabel("Rydberg density")
-ax.set_title("Density Profile: 1D Chain, Δ = 2π * 10 MHz")
+fig = Figure(size=(800, 320))
+ax = Axis(fig[1, 1], xlabel = "Sites", ylabel = "Rydberg density", title = "Density Profile: 1D Chain, Δ = 2π * 10 MHz", xticks = 1:nsites)
+barplot!(ax, 1:nsites, density_g[30, :])
 fig
 
 # More generally, we can plot an order parameter as a function of ``\Delta`` to clearly see the onset of phase transition. 
@@ -83,13 +75,10 @@ order_para = map(1:Δ_step) do ii
     return sum(density_g[ii, 1:2:nsites]) - sum(density_g[ii, 2:2:nsites]) # density on odd sites - density on even sites
 end
 
-fig, ax = plt.subplots(figsize = (10, 4))
-ax.plot(Δ / 2π, order_para)
-ax.set_xlabel("Δ/2π (MHz) ")
-ax.set_ylabel("Order parameter")
+fig = Figure(size=(800, 320))
+ax = Axis(fig[1, 1], xlabel = "Δ/2π (MHz)", ylabel = "Order parameter")
+lines!(ax, Δ / (2π), order_para)
 fig
-
-# From the density profile of ground states and the change in the order parameter, we can observe a phase transition with changing ``\Delta``. 
 # Below, we show that by slowly changing the parameters of the Hamiltonian, we can follow the trajectory of the ground states and adiabatically evolve the atoms from the ground state to the ``Z_2`` 
 # ordered state.
 
@@ -106,13 +95,12 @@ U2 = 2π * 10;
 Δ = piecewise_linear(clocks = [0.0, 0.6, 2.1, total_time], values = [U1, U1, U2, U2]);
 
 # We plot the two waveforms:
-fig, (ax1, ax2) = plt.subplots(ncols = 2, figsize = (12, 4))
-Bloqade.plot!(ax1, Ω)
-ax1.set_ylabel("Ω/2π (MHz)")
-Bloqade.plot!(ax2, Δ)
-ax2.set_ylabel("Δ/2π (MHz)")
+fig = Figure(size=(960, 320))
+ax1 = Axis(fig[1, 1], ylabel = "Ω/2π (MHz)")
+ax2 = Axis(fig[1, 2], ylabel = "Δ/2π (MHz)")
+Bloqade.plot!(ax1, Ω / (2π))
+Bloqade.plot!(ax2, Δ / (2π))
 fig
-
 # We generate the positions for a 1D atomic chain again: 
 
 nsites = 9
@@ -136,19 +124,13 @@ densities = []
 for _ in TimeChoiceIterator(integrator, 0.0:1e-3:total_time)
     push!(densities, rydberg_density(reg))
 end
-D = hcat(densities...);
-
-# and finally plot the time-dependent dynamics of Rydberg density for each site:
-fig, ax = plt.subplots(figsize = (10, 4))
-shw = ax.imshow(real(D), interpolation = "nearest", aspect = "auto", extent = [0, total_time, 0.5, nsites + 0.5])
-ax.set_xlabel("time (μs)")
-ax.set_ylabel("site")
-ax.set_xticks(0:0.2:total_time)
-ax.set_yticks(1:nsites)
-bar = fig.colorbar(shw)
-fig
+D = hcat(densities...)';
 
-# We can clearly see that a ``Z_2`` ordered state has been generated by the specified adiabatic pulse sequence. 
+fig = Figure(size=(800, 320))
+ax = Axis(fig[1, 1], xlabel = "time (μs)", ylabel = "site", xticks=0:0.2:total_time, yticks=(0.5:1:nsites, string.(1:nsites)))
+shw = heatmap!(ax, LinRange(0.0, total_time, size(D, 1)), LinRange(0.5, nsites - 0.5, size(D, 2)), real(D))
+Colorbar(fig[1, 2], shw, label = "Rydberg density")
+fig
 # We can also confirm it by plotting the bitstring distribution at the final time step:
 
 bitstring_hist(reg; nlargest = 20)
@@ -194,11 +176,11 @@ total_time = 2.9
 U = 2π * 15.0
 Δ = piecewise_linear(clocks = [0.0, 0.3, 2.6, total_time], values = [-U, -U, U, U]);
 
-fig, (ax1, ax2) = plt.subplots(ncols = 2, figsize = (10, 4))
+fig = Figure(size=(800, 320))
+ax1 = Axis(fig[1, 1], ylabel = "Ω/2π (MHz)")
+ax2 = Axis(fig[1, 2], ylabel = "Δ/2π (MHz)")
 Bloqade.plot!(ax1, Ω)
-ax1.set_ylabel("Ω/2π (MHz)")
 Bloqade.plot!(ax2, Δ)
-ax2.set_ylabel("Δ/2π (MHz)")
 fig
 
 # Then, we use the waveforms and atom positions to create a Hamiltonian and define a time evolution problem:
@@ -213,13 +195,10 @@ densities = [];
 for _ in TimeChoiceIterator(integrator, 0.0:1e-3:total_time)
     push!(densities, rydberg_density(reg))
 end
-D = hcat(densities...)
-
-fig, ax = plt.subplots(figsize = (10, 4))
-shw = ax.imshow(real(D), interpolation = "nearest", aspect = "auto", extent = [0, total_time, 0.5, nsites + 0.5])
-ax.set_xlabel("time (μs)")
-ax.set_ylabel("site")
-ax.set_xticks(0:0.2:total_time)
-ax.set_yticks(1:nsites)
-bar = fig.colorbar(shw)
+D = hcat(densities...)'
+
+fig = Figure(size=(800, 320))
+ax = Axis(fig[1, 1], xlabel = "time (μs)", ylabel = "site", xticks=0:0.2:total_time, yticks=(0.5:1:nsites, string.(1:nsites)))
+shw = heatmap!(ax, LinRange(0.0, total_time, size(D, 1)), LinRange(0.5, nsites - 0.5, size(D, 2)), real(D))
+Colorbar(fig[1, 2], shw, label = "Rydberg density")
 fig

examples/2.adiabatic/main.jl

@@ -1,14 +1,13 @@
 [deps]
-CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 Bloqade = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe1"
 BloqadeExpr = "bd27d05e-4ce1-5e79-84dd-c5d7d508abe2"
 BloqadeKrylov = "bd27d05e-4cd1-5e79-84dd-c5d7d508ade2"
 BloqadeLattices = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe4"
 BloqadeMIS = "bd27d05e-4ce1-5e74-84dd-c5d7d508bbe2"
 BloqadeODE = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe5"
 BloqadeWaveforms = "bd27d05e-4ce1-5e79-84dd-c5d7d508bbe7"
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 Yao = "5872b779-8223-5990-8dd0-5abbb0748c8c"
 YaoArrayRegister = "e600142f-9330-5003-8abb-0ebd767abc51"