JuliaFormatter.jl

src/checks.jl

@@ -14,8 +14,6 @@ end
 function check_densesmooth(integ)
     if integ.opts.dense && !integ.alg.smooth
         error("To use `dense=true` you need to set `smooth=true`!")
-    # elseif !integ.opts.dense && integ.alg.smooth
-    #     @warn "If you set dense=false for efficiency, you might also want to set smooth=false."
     end
     if !integ.opts.save_everystep && integ.alg.smooth
         error("If you do not save all values, you do not need to smooth!")

src/covariance_structure.jl

@@ -45,8 +45,6 @@ to_factorized_matrix(::DenseCovariance, M::AbstractMatrix) = Matrix(M)
 to_factorized_matrix(::IsometricKroneckerCovariance, M::AbstractMatrix) =
     error("Cannot factorize Matrix")
 to_factorized_matrix(::IsometricKroneckerCovariance, M::IsometricKroneckerProduct) = M
-to_factorized_matrix(FAC::IsometricKroneckerCovariance, M::Diagonal{T, <:Fill{T, 1}}) where {T} =
-	  IsometricKroneckerProduct(FAC.d, M.diag.value*Eye(FAC.q+1))
 
 for FT in [:DenseCovariance, :IsometricKroneckerCovariance]
     @eval to_factorized_matrix(FAC::$FT, M::PSDMatrix) =

src/data_likelihoods/dalton.jl

@@ -26,19 +26,20 @@ function dalton_data_loglik(
     args...;
     # observation model
     observation_matrix=I,
-    observation_noise_cov::Union{Number, AbstractMatrix},
+    observation_noise_cov::Union{Number,AbstractMatrix},
     # data
     data::NamedTuple{(:t, :u)},
-    kwargs...
+    kwargs...,
 )
-
     if alg.smooth
-        str = "The passed algorithm performs smoothing, but `dalton_nll` can be used without. " *
+        str =
+            "The passed algorithm performs smoothing, but `dalton_nll` can be used without. " *
             "You might want to set `smooth=false` to imprpove performance."
         @warn str
     end
     if !(:adaptive in keys(kwargs))
-        throw(ArgumentError("`dalton_nll` only works with fixed step sizes. Set `adaptive=false`."))
+        str = "`dalton_nll` only works with fixed step sizes. Set `adaptive=false`."
+        throw(ArgumentError(str))
     end
 
     if :tstops in keys(kwargs)
@@ -68,10 +69,8 @@ function dalton_data_loglik(
         tstops,
     )
 
-    dalton_ll = (data_ll.ll
-                 + sol_with_data.pnstats.log_likelihood
-                 - sol_without_data.pnstats.log_likelihood)
-
+    sol_with_data_pn_ll = sol_with_data.pnstats.log_likelihood
+    sol_without_data_pn_ll = sol_without_data.pnstats.log_likelihood
+    dalton_ll = data_ll.ll + sol_with_data_pn_ll - sol_without_data_pn_ll
     return dalton_ll
 end
-

src/data_likelihoods/fenrir.jl

@@ -36,7 +36,7 @@ function fenrir_data_loglik(
     observation_noise_cov::Union{Number,AbstractMatrix},
     # data
     data::NamedTuple{(:t, :u)},
-    kwargs...
+    kwargs...,
 )
     if !alg.smooth
         throw(ArgumentError("fenrir only works with smoothing. Set `smooth=true`."))
@@ -64,7 +64,7 @@ end
 
 function fit_pnsolution_to_data!(
     sol::AbstractProbODESolution,
-    observation_noise_var::Union{Real, AbstractMatrix},
+    observation_noise_var::Union{Real,AbstractMatrix},
     data::NamedTuple{(:t, :u)};
     proj=I,
 )

src/data_likelihoods/filtering.jl

@@ -4,13 +4,14 @@ function filtering_data_loglik(
     args...;
     # observation model
     observation_matrix=I,
-    observation_noise_cov::Union{Number, AbstractMatrix},
+    observation_noise_cov::Union{Number,AbstractMatrix},
     # data
     data::NamedTuple{(:t, :u)},
-    kwargs...
+    kwargs...,
 )
     if alg.smooth
-        str = "The passed algorithm performs smoothing, but `dalton_nll` can be used without. " *
+        str =
+            "The passed algorithm performs smoothing, but `dalton_nll` can be used without. " *
             "You might want to set `smooth=false` to imprpove performance."
         @warn str
     end