Switch to WGLMakie

docs/make.jl

@@ -7,18 +7,6 @@ Pkg.activate(script_dir)
 parent_dir = dirname(script_dir)
 Pkg.develop(PackageSpec(path=parent_dir))
 
-
-using Documenter
-using Pyehtim
-using Zygote
-using Comrade
-using ComradeBase
-using VLBISkyModels
-using InteractiveUtils
-
-using Literate
-using Pkg
-
 function dev_subpkg(subpkg)
     subpkg_path = joinpath(dirname(@__DIR__), "lib", subpkg)
     Pkg.develop(PackageSpec(path=subpkg_path))
@@ -32,6 +20,19 @@ dev_subpkg("ComradeNested")
 dev_subpkg("ComradeDynesty")
 dev_subpkg("ComradeAdaptMCMC")
 
+
+using Documenter
+using Pyehtim
+using Zygote
+using Comrade
+using ComradeBase
+using VLBISkyModels
+using InteractiveUtils
+
+using Literate
+using Pkg
+
+
 using ComradeAHMC
 using ComradeOptimization
 using ComradeNested

examples/geometric_modeling.jl

@@ -169,7 +169,7 @@ xopt = transform(fpost, sol)
 
 # Given this we can now plot the optimal image or the *maximum a posteriori* (MAP) image.
 
-import CairoMakie as CM
+import WGLMakie as CM
 g = imagepixels(μas2rad(200.0), μas2rad(200.0), 256, 256)
 fig, ax, plt = CM.image(g, model(xopt); axis=(xreversed=true, aspect=1, xlabel="RA (μas)", ylabel="Dec (μas)"), figure=(;resolution=(650,500),) ,colormap=:afmhot)
 

examples/hybrid_imaging.jl

@@ -216,7 +216,7 @@ post = Posterior(lklhd, prior)
 # To sample from our prior we can do
 xrand = prior_sample(rng, post)
 # and then plot the results
-import CairoMakie as CM
+import WGLMakie as CM
 g = imagepixels(μas2rad(150.0), μas2rad(150.0), 128, 128)
 imageviz(intensitymap(skymodel(post, xrand), g))
 
@@ -256,7 +256,7 @@ residual(vlbimodel(post, xopt), dvis, ylabel="Correlated Flux Residual")
 # Now these residuals look a bit high. However, it turns out this is because the MAP is typically
 # not a great estimator and will not provide very predictive measurements of the data. We
 # will show this below after sampling from the posterior.
-CM.image(g, skymodel(post, xopt), axis=(aspect=1, xreversed=true, title="MAP"), colormap=:afmhot)
+CM.image(g, skymodel(post, xopt), axis=(aspect=1, xreversed=true, title="MAP"), colormap=:afmhot, figure=(;resolution=(400, 400),))
 
 
 # We will now move directly to sampling at this point.
@@ -295,7 +295,7 @@ rast_imgs = intensitymap.(rast_samples, fovxy, fovxy, 128, 128)
 ring_mean, ring_std = mean_and_std(ring_imgs)
 rast_mean, rast_std = mean_and_std(rast_imgs)
 
-fig = CM.Figure(; resolution=(800, 800))
+fig = CM.Figure(; resolution=(400, 400))
 axes = [CM.Axis(fig[i, j], xreversed=true, aspect=CM.DataAspect()) for i in 1:2, j in 1:2]
 CM.image!(axes[1,1], ring_mean, colormap=:afmhot); axes[1,1].title = "Ring Mean"
 CM.image!(axes[1,2], ring_std, colormap=:afmhot); axes[1,2].title = "Ring Std. Dev."
@@ -304,7 +304,7 @@ CM.image!(axes[2,2], rast_std, colormap=:afmhot); axes[2,2].title = "Rast Std. D
 fig
 
 # Finally, let's take a look at some of the ring parameters
-figd = CM.Figure(;resolution=(900, 600))
+figd = CM.Figure(;resolution=(600, 400))
 p1 = CM.density(figd[1,1], rad2μas(chain.r)*2, axis=(xlabel="Ring Diameter (μas)",))
 p2 = CM.density(figd[1,2], rad2μas(chain.σ)*2*sqrt(2*log(2)), axis=(xlabel="Ring FWHM (μas)",))
 p3 = CM.density(figd[1,3], -rad2deg.(chain.mp1) .+ 360.0, axis=(xlabel = "Ring PA (deg) E of N",))

examples/imaging_closures.jl

@@ -164,9 +164,9 @@ residual(skymodel(post, xopt), dlcamp, ylabel="Log Closure Amplitude Res.")
 residual(skymodel(post, xopt), dcphase, ylabel="|Closure Phase Res.|")
 
 # Now let's plot the MAP estimate.
-import CairoMakie as CM
+import WGLMakie as CM
 img = intensitymap(skymodel(post, xopt), μas2rad(150.0), μas2rad(150.0), 100, 100)
-CM.image(img, axis=(xreversed=true, aspect=1, title="MAP Image"), colormap=:afmhot)
+CM.image(img, axis=(xreversed=true, aspect=1, title="MAP Image"), colormap=:afmhot, figure=(;resolution=(400, 400),))
 
 # To sample from the posterior we will use HMC and more specifically the NUTS algorithm. For information about NUTS
 # see Michael Betancourt's [notes](https://arxiv.org/abs/1701.02434).
@@ -194,7 +194,7 @@ using StatsBase
 imgs = intensitymap.(msamples, μas2rad(150.0), μas2rad(150.0), 128, 128)
 mimg = mean(imgs)
 simg = std(imgs)
-fig = CM.Figure(;resolution=(800, 800))
+fig = CM.Figure(;resolution=(400, 400));
 CM.image(fig[1,1], mimg,
                    axis=(xreversed=true, aspect=1, title="Mean Image"),
                    colormap=:afmhot)

examples/imaging_pol.jl

@@ -323,7 +323,7 @@ imgtrue = Comrade.load(joinpath(dirname(pathof(Comrade)), "..", "examples", "Pol
 imgtruesub = imgtrue(Interval(-fovx/2, fovx/2), Interval(-fovy/2, fovy/2))
 img = intensitymap!(copy(imgtruesub), skymodel(post, xopt))
 import CairoMakie as CM
-fig = CM.Figure(;resolution=(900, 400))
+fig = CM.Figure(;resolution=(450, 200));
 polimage(fig[1,1], imgtruesub,
                    axis=(xreversed=true, aspect=1, title="Truth", limits=((-20.0,20.0), (-20.0, 20.0))),
                    length_norm=1, plot_total=true,

examples/imaging_vis.jl

@@ -269,7 +269,7 @@ residual(vlbimodel(post, xopt), dvis)
 # improved in a few ways, but that is beyond the goal of this quick tutorial.
 # Plotting the image, we see that we have a much cleaner version of the closure-only image from
 # [Imaging a Black Hole using only Closure Quantities](@ref).
-import CairoMakie as CM
+import WGLMakie as CM
 img = intensitymap(skymodel(post, xopt), fovx, fovy, 128, 128)
 CM.image(img, axis=(xreversed=true, aspect=1, title="MAP Image"), colormap=:afmhot)