JuliaFormatter.jl

nathanaelbosch · Feb 9, 2024 · 4060652 · 4060652
1 parent 9402f93
commit 4060652
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Showing 3 changed files with 25 additions and 15 deletions.
diff --git a/src/algorithms.jl b/src/algorithms.jl
@@ -5,15 +5,23 @@ abstract type AbstractEK <: OrdinaryDiffEq.OrdinaryDiffEqAdaptiveAlgorithm end
 
 function ekargcheck(alg; diffusionmodel, pn_observation_noise, kwargs...)
     if (isstatic(diffusionmodel) && diffusionmodel.calibrate) &&
-        (!isnothing(pn_observation_noise) && !iszero(pn_observation_noise))
-        throw(ArgumentError("Automatic calibration of global diffusion models is not possible when using observation noise. If you want to calibrate a global diffusion parameter, do so setting `calibrate=false` and optimizing `sol.pnstats.log_likelihood` manually."))
+       (!isnothing(pn_observation_noise) && !iszero(pn_observation_noise))
+        throw(
+            ArgumentError(
+                "Automatic calibration of global diffusion models is not possible when using observation noise. If you want to calibrate a global diffusion parameter, do so setting `calibrate=false` and optimizing `sol.pnstats.log_likelihood` manually.",
+            ),
+        )
     end
-    if (diffusionmodel isa FixedMVDiffusion || diffusionmodel isa DynamicMVDiffusion) && alg == EK1
-        throw(ArgumentError("The `EK1` algorithm does not support multivariate diffusion models. Use `EK0` instead, or use a scalar diffusion model."))
+    if (diffusionmodel isa FixedMVDiffusion || diffusionmodel isa DynamicMVDiffusion) &&
+       alg == EK1
+        throw(
+            ArgumentError(
+                "The `EK1` algorithm does not support multivariate diffusion models. Use `EK0` instead, or use a scalar diffusion model.",
+            ),
+        )
     end
 end
 
-
 """
     EK0(; order=3,
           smooth=true,
@@ -56,11 +64,11 @@ struct EK0{PT,DT,IT,RT} <: AbstractEK
     initialization::IT
     pn_observation_noise::RT
     EK0(; order=3,
-         prior::PT=IWP(order),
-         diffusionmodel::DT=DynamicDiffusion(),
-         smooth=true,
-         initialization::IT=TaylorModeInit(num_derivatives(prior)),
-         pn_observation_noise::RT=nothing,
+        prior::PT=IWP(order),
+        diffusionmodel::DT=DynamicDiffusion(),
+        smooth=true,
+        initialization::IT=TaylorModeInit(num_derivatives(prior)),
+        pn_observation_noise::RT=nothing,
     ) where {PT,DT,IT,RT} = begin
         ekargcheck(EK0; diffusionmodel, pn_observation_noise)
         new{PT,DT,IT,RT}(

diff --git a/src/caches.jl b/src/caches.jl
@@ -168,7 +168,8 @@ function OrdinaryDiffEq.alg_cache(
     copy!(x0.Σ, apply_diffusion(x0.Σ, initdiff))
 
     # Measurement model related things
-    R = isnothing(alg.pn_observation_noise) ? nothing :
+    R =
+        isnothing(alg.pn_observation_noise) ? nothing :
         to_factorized_matrix(FAC, cov2psdmatrix(alg.pn_observation_noise; d))
     H = factorized_similar(FAC, d, D)
     v = similar(Array{uElType}, d)

diff --git a/src/filtering/update.jl b/src/filtering/update.jl
@@ -151,9 +151,10 @@ end
 function update!(
     x_out::SRGaussian{T,<:IsometricKroneckerProduct},
     x_pred::SRGaussian{T,<:IsometricKroneckerProduct},
-    measurement::Gaussian{<:AbstractVector,
-                          <:Union{<:PSDMatrix{T,<:IsometricKroneckerProduct},
-                                  <:IsometricKroneckerProduct}},
+    measurement::Gaussian{
+        <:AbstractVector,
+        <:Union{<:PSDMatrix{T,<:IsometricKroneckerProduct},<:IsometricKroneckerProduct},
+    },
     H::IsometricKroneckerProduct,
     K1_cache::IsometricKroneckerProduct,
     K2_cache::IsometricKroneckerProduct,
@@ -169,7 +170,7 @@ function update!(
     _x_pred = Gaussian(reshape_no_alloc(x_pred.μ, Q, d), PSDMatrix(x_pred.Σ.R.B))
     _measurement = Gaussian(
         reshape_no_alloc(measurement.μ, 1, d),
-        measurement.Σ isa PSDMatrix ? PSDMatrix(measurement.Σ.R.B) : measurement.Σ.B
+        measurement.Σ isa PSDMatrix ? PSDMatrix(measurement.Σ.R.B) : measurement.Σ.B,
     )
     _H = H.B
     _K1_cache = K1_cache.B