Reactant attempt 2

Project.toml

@@ -6,6 +6,7 @@ version = "0.11.26"
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
+Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
 AstroLib = "c7932e45-9af1-51e7-9da9-f004cd3a462b"
 AstroTime = "c61b5328-d09d-5e37-a9a8-0eb41c39009c"
@@ -30,6 +31,7 @@ PolarizedTypes = "d3c5d4cd-a8ee-40d6-aac7-e34df5a20044"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+ReactantCore = "a3311ec8-5e00-46d5-b541-4f83e724a433"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -53,6 +55,7 @@ Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 Pigeons = "0eb8d820-af6a-4919-95ae-11206f830c31"
 Pyehtim = "3d61700d-6e5b-419a-8e22-9c066cf00468"
+Reactant = "3c362404-f566-11ee-1572-e11a4b42c853"
 VLBIFiles = "c1ebf4c8-f9d4-409a-8daf-7009448f4e6e"
 
 [extensions]
@@ -63,9 +66,11 @@ ComradeMakieExt = "Makie"
 ComradeOptimizationExt = "Optimization"
 ComradePigeonsExt = "Pigeons"
 ComradePyehtimExt = "Pyehtim"
+ComradeReactantExt = "Reactant"
 ComradeVLBIFilesExt = "VLBIFiles"
 
 [compat]
+Adapt = "4"
 AbstractMCMC = "3, 4, 5"
 Accessors = "0.1"
 AdvancedHMC = "0.6, 0.7, 0.8"
@@ -76,7 +81,7 @@ ChainRulesCore = "1"
 ComradeBase = "0.9.13"
 DelimitedFiles = "1"
 DensityInterface = "0.4"
-DimensionalData = "0.29 - 0.29.24, ^0.29.26, 0.30"
+DimensionalData = "0.30"
 Distributions = "0.25"
 DocStringExtensions = "0.8, 0.9"
 Dynesty = "0.4"
@@ -92,9 +97,11 @@ Optimization = "5"
 PaddedViews = "0.5"
 ParameterHandling = "0.4, 0.5"
 Pigeons = "0.4, 0.5"
-PolarizedTypes = "0.1.0 - 0.1.3"
+PolarizedTypes = "0.1"
 PrettyTables = "3"
 Pyehtim = "0.2"
+Reactant = "0.2"
+ReactantCore = "0.1"
 RecipesBase = "1"
 Reexport = "1"
 SpecialFunctions = "0.10, 1, 2"
@@ -105,8 +112,8 @@ StructArrays = "0.5,0.6, 0.7"
 Tables = "1"
 TransformVariables = "^0.8.19"
 VLBIFiles = "0.4.16"
-VLBIImagePriors = "0.10.8"
-VLBILikelihoods = "^0.2.6"
+VLBIImagePriors = "^0.10.9"
+VLBILikelihoods = "0.3.0"
 VLBISkyModels = "^0.6.24"
 julia = "1.10"
 
@@ -119,8 +126,9 @@ Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 Pigeons = "0eb8d820-af6a-4919-95ae-11206f830c31"
 Pyehtim = "3d61700d-6e5b-419a-8e22-9c066cf00468"
+Reactant = "3c362404-f566-11ee-1572-e11a4b42c853"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 VLBIFiles = "c1ebf4c8-f9d4-409a-8daf-7009448f4e6e"
 
 [targets]
-test = ["Test", "AdvancedHMC", "Dynesty", "Enzyme", "Makie", "Optimization", "Pigeons", "Pyehtim", "VLBIFiles"]
+test = ["Test", "AdvancedHMC", "Dynesty", "Enzyme", "Makie", "Optimization", "Pigeons", "Pyehtim", "VLBIFiles", "Reactant"]

docs/src/benchmarks.md

@@ -60,15 +60,15 @@ function model(θ, p)
     return ring + g
 end
 prior = (
-          rad = Uniform(10.0, 30.0),
-          wid = Uniform(1.0, 10.0),
-          a = Uniform(-0.5, 0.5), b = Uniform(-0.5, 0.5),
-          f = Uniform(0.0, 1.0),
-          sig = Uniform((1.0), (60.0)),
-          asy = Uniform(0.0, 0.9),
-          pa = Uniform(0.0, 1π),
-          x = Uniform(-(80.0), (80.0)),
-          y = Uniform(-(80.0), (80.0))
+          rad = VLBIUniform(10.0, 30.0),
+          wid = VLBIUniform(1.0, 10.0),
+          a = VLBIUniform(-0.5, 0.5), b = VLBIUniform(-0.5, 0.5),
+          f = VLBIUniform(0.0, 1.0),
+          sig = VLBIUniform((1.0), (60.0)),
+          asy = VLBIUniform(0.0, 0.9),
+          pa = VLBIUniform(0.0, 1π),
+          x = VLBIUniform(-(80.0), (80.0)),
+          y = VLBIUniform(-(80.0), (80.0))
         )
 # Now form the posterior
 skym = SkyModel(model, prior, imagepixels(μas2rad(150.0), μas2rad(150.0), 128, 128))

docs/src/tutorials/index.md

@@ -6,8 +6,8 @@ const beginner = [
   {
     href: "beginner/LoadingData", 
     src: "../vis.png",
-    caption: "Loading Data with Pyehtim",
-    desc: "How to load data using standard eht-imaging in Julia." 
+    caption: "Loading Data with VLBIFiles",
+    desc: "How to load data into the Comrade format" 
   },
   {
     href: "beginner/GeometricModeling", 

examples/advanced/FitPS/main.jl

@@ -77,9 +77,9 @@ using Distributions
 # arguments and are in scope inside both the prior expressions and the body.
 @sky function sky(grid; mimg, pl, cprior, ρmax)
     c ~ cprior
-    ρs ~ ntuple(Returns(Uniform(0.01, ρmax)), 3)
-    σimg ~ Exponential(2.0)
-    fb ~ Uniform(0.0, 1.0)
+    ρs ~ ntuple(Returns(VLBIUniform(0.01, ρmax)), 3)
+    σimg ~ VLBIExponential(2.0)
+    fb ~ VLBIUniform(0.0, 1.0)
     ## Apply the GMRF fluctuations to the image
     x = genfield(StationaryRandomField(MarkovPS(ρs), pl), c)
     x .= σimg .* x
@@ -123,7 +123,7 @@ skym = sky(grid; mimg, pl, cprior, ρmax = max(size(grid)...))
 # Since we are fitting closures we do not need to include an instrument model, since
 # the closure likelihood is approximately independent of gains in the high SNR limit.
 using Enzyme
-post = VLBIPosterior(skym, dlcamp, dcphase)
+post = VLBIPosterior(skym, dlcamp, dcphase);
 
 # ## Reconstructing the Image
 

examples/advanced/Hibi/main.jl

@@ -77,10 +77,10 @@ using VLBIImagePriors
 using Distributions
 @sky function sky(grid; ftot, cprior)
     c ~ cprior
-    σimg ~ truncated(Normal(0.0, 0.5); lower = 0.0)
-    r ~ Uniform(μas2rad(10.0), μas2rad(40.0))
-    ain ~ Uniform(1.0, 20.0)
-    aout ~ Uniform(1.0, 20.0)
+    σimg ~ VLBITruncated(VLBIGaussian(0.0, 0.5); lower = 0.0)
+    r ~ VLBIUniform(μas2rad(10.0), μas2rad(40.0))
+    ain ~ VLBIUniform(1.0, 20.0)
+    aout ~ VLBIUniform(1.0, 20.0)
     ## Form the image model
     mb = RingTemplate(RadialDblPower(ain, aout), AzimuthalUniform())
     mr = modify(mb, Stretch(r))
@@ -97,11 +97,13 @@ end
 #   - Gain amplitudes which are typically known to 10-20%, except for LMT, which has amplitudes closer to 50-100%.
 #   - Gain phases which are more difficult to constrain and can shift rapidly.
 
-fgain(x) = exp(x.lg + 1im * x.gp)
+using VLBIImagePriors
+using Distributions
+fgain(x) = exp(complex(x.lg, x.gp))
 G = SingleStokesGain(fgain)
 
 intpr = (
-    lg = ArrayPrior(IIDSitePrior(ScanSeg(), Normal(0.0, 0.2)); LM = IIDSitePrior(ScanSeg(), Normal(0.0, 1.0))),
+    lg = ArrayPrior(IIDSitePrior(ScanSeg(), VLBIGaussian(0.0, 0.2)); LM = IIDSitePrior(ScanSeg(), VLBIGaussian(0.0, 1.0))),
     gp = ArrayPrior(IIDSitePrior(ScanSeg(), DiagonalVonMises(0.0, inv(π^2))); refant = SEFDReference(0.0)),
 )
 intmodel = InstrumentModel(G, intpr)
@@ -123,7 +125,7 @@ skym = sky(g; ftot = 1.1, cprior)
 # This is everything we need to specify our posterior distribution, which our is the main
 # object of interest in image reconstructions when using Bayesian inference.
 using Enzyme
-post = VLBIPosterior(skym, intmodel, dvis)
+post = VLBIPosterior(skym, intmodel, dvis);
 
 # We can sample from the prior to see what the model looks like
 using DisplayAs #hide

examples/advanced/NeuralFields/Project.toml

@@ -0,0 +1,32 @@
+[deps]
+AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+Comrade = "99d987ce-9a1e-4df8-bc0b-1ea019aa547b"
+DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+NonuniformFFTs = "cd96f58b-6017-4a02-bb9e-f4d81626177f"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+Pyehtim = "3d61700d-6e5b-419a-8e22-9c066cf00468"
+Reactant = "3c362404-f566-11ee-1572-e11a4b42c853"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+VLBIFiles = "c1ebf4c8-f9d4-409a-8daf-7009448f4e6e"
+VLBIImagePriors = "b1ba175b-8447-452c-b961-7db2d6f7a029"
+VLBILikelihoods = "90db92cd-0007-4c0a-8e51-dbf0782ce592"
+VLBISkyModels = "d6343c73-7174-4e0f-bb64-562643efbeca"
+
+[compat]
+CairoMakie = "0.15"
+DisplayAs = "0.1"
+Distributions = "0.25"
+Optimisers = "0.4"
+Pkg = "1"
+Pyehtim = "0.2"
+Reactant = "0.2"
+StableRNGs = "1"
+VLBIImagePriors = "0.10"