Apply JuliaFormatter to fix code formatting (#450)

- Applied JuliaFormatter with SciML style guide
- Formatted 7 files

🤖 Generated by OrgMaintenanceScripts.jl

Co-authored-by: SciML Bot <sciml-bot@julialang.org>

Author: ChrisRackauckas

ext/DataInterpolationsChainRulesCoreExt.jl

@@ -1,10 +1,10 @@
 module DataInterpolationsChainRulesCoreExt
 
 using DataInterpolations: _interpolate, derivative, AbstractInterpolation,
-                            LinearInterpolation, QuadraticInterpolation,
-                            LagrangeInterpolation, AkimaInterpolation,
-                            BSplineInterpolation, BSplineApprox, get_idx, get_parameters,
-                            munge_data
+                          LinearInterpolation, QuadraticInterpolation,
+                          LagrangeInterpolation, AkimaInterpolation,
+                          BSplineInterpolation, BSplineApprox, get_idx, get_parameters,
+                          munge_data
 using ChainRulesCore
 
 function ChainRulesCore.rrule(::typeof(munge_data), u, t)

ext/DataInterpolationsMakieExt.jl

@@ -8,15 +8,19 @@ using Makie
 Makie.plottype(::AbstractInterpolation) = Makie.ScatterLines
 
 # Define the attributes that you want to use
-Makie.used_attributes(::Makie.PointBased, ::AbstractInterpolation) = (:plotdensity, :denseplot)
-Makie.used_attributes(::Type{<:Makie.ScatterLines}, ::AbstractInterpolation) = (:plotdensity, :denseplot)
+function Makie.used_attributes(::Makie.PointBased, ::AbstractInterpolation)
+    (:plotdensity, :denseplot)
+end
+function Makie.used_attributes(::Type{<:Makie.ScatterLines}, ::AbstractInterpolation)
+    (:plotdensity, :denseplot)
+end
 
 # Define the conversion of the data to the plot
 function Makie.convert_arguments(
-    ::Makie.PointBased,
-    A::AbstractInterpolation;
-    plotdensity = 10_000,
-    denseplot = true,
+        ::Makie.PointBased,
+        A::AbstractInterpolation;
+        plotdensity = 10_000,
+        denseplot = true
 )
     DataInterpolations.to_plottable(A; plotdensity = plotdensity, denseplot = denseplot)
 end
@@ -26,16 +30,17 @@ end
 # and should actually be handled by a plot! method,
 # except that doesn't work anymore or does it?
 function Makie.convert_arguments(
-    ::Type{<:Makie.ScatterLines},
-    A::AbstractInterpolation;
-    plotdensity = 10_000,
-    denseplot = true,
+        ::Type{<:Makie.ScatterLines},
+        A::AbstractInterpolation;
+        plotdensity = 10_000,
+        denseplot = true
 )
-    densex, densey = convert_arguments(Makie.PointBased(), A; plotdensity = plotdensity, denseplot = denseplot)
+    densex, densey = convert_arguments(
+        Makie.PointBased(), A; plotdensity = plotdensity, denseplot = denseplot)
     return [
         Makie.SpecApi.Lines(densex, densey),
         Makie.SpecApi.Scatter(A.t, A.u)
     ]
 end
 
-end # module
+end # module

ext/DataInterpolationsSparseConnectivityTracerExt.jl

@@ -5,20 +5,20 @@ using SparseConnectivityTracer: GradientTracer, gradient_tracer_1_to_1
 using SparseConnectivityTracer: HessianTracer, hessian_tracer_1_to_1
 using FillArrays: Fill # from FillArrays.jl
 using DataInterpolations:
-    AbstractInterpolation,
-    LinearInterpolation,
-    QuadraticInterpolation,
-    LagrangeInterpolation,
-    AkimaInterpolation,
-    ConstantInterpolation,
-    QuadraticSpline,
-    CubicSpline,
-    BSplineInterpolation,
-    BSplineApprox,
-    CubicHermiteSpline,
-    # PCHIPInterpolation,
-    QuinticHermiteSpline,
-    output_size
+                          AbstractInterpolation,
+                          LinearInterpolation,
+                          QuadraticInterpolation,
+                          LagrangeInterpolation,
+                          AkimaInterpolation,
+                          ConstantInterpolation,
+                          QuadraticSpline,
+                          CubicSpline,
+                          BSplineInterpolation,
+                          BSplineApprox,
+                          CubicHermiteSpline,
+# PCHIPInterpolation,
+                          QuinticHermiteSpline,
+                          output_size
 
 #===========#
 # Utilities #
@@ -33,26 +33,26 @@ function _sct_interpolate(
         uType::Type{<:AbstractVector{<:Number}},
         t::GradientTracer,
         is_der_1_zero,
-        is_der_2_zero,
-    )
+        is_der_2_zero
+)
     return gradient_tracer_1_to_1(t, is_der_1_zero)
 end
 function _sct_interpolate(
         ::AbstractInterpolation,
         uType::Type{<:AbstractVector{<:Number}},
         t::HessianTracer,
         is_der_1_zero,
-        is_der_2_zero,
-    )
+        is_der_2_zero
+)
     return hessian_tracer_1_to_1(t, is_der_1_zero, is_der_2_zero)
 end
 function _sct_interpolate(
         interp::AbstractInterpolation,
         uType::Type{<:AbstractMatrix{<:Number}},
         t::GradientTracer,
         is_der_1_zero,
-        is_der_2_zero,
-    )
+        is_der_2_zero
+)
     t = gradient_tracer_1_to_1(t, is_der_1_zero)
     N = only(output_size(interp))
     return Fill(t, N)
@@ -62,8 +62,8 @@ function _sct_interpolate(
         uType::Type{<:AbstractMatrix{<:Number}},
         t::HessianTracer,
         is_der_1_zero,
-        is_der_2_zero,
-    )
+        is_der_2_zero
+)
     t = hessian_tracer_1_to_1(t, is_der_1_zero, is_der_2_zero)
     N = only(output_size(interp))
     return Fill(t, N)
@@ -77,33 +77,33 @@ end
 # all interpolations have a non-zero second derivative at some point in the input domain.
 
 for (I, is_der1_zero, is_der2_zero) in (
-        (:ConstantInterpolation, true, true),
-        (:LinearInterpolation, false, true),
-        (:QuadraticInterpolation, false, false),
-        (:LagrangeInterpolation, false, false),
-        (:AkimaInterpolation, false, false),
-        (:QuadraticSpline, false, false),
-        (:CubicSpline, false, false),
-        (:BSplineInterpolation, false, false),
-        (:BSplineApprox, false, false),
-        (:CubicHermiteSpline, false, false),
-        (:QuinticHermiteSpline, false, false),
-    )
+    (:ConstantInterpolation, true, true),
+    (:LinearInterpolation, false, true),
+    (:QuadraticInterpolation, false, false),
+    (:LagrangeInterpolation, false, false),
+    (:AkimaInterpolation, false, false),
+    (:QuadraticSpline, false, false),
+    (:CubicSpline, false, false),
+    (:BSplineInterpolation, false, false),
+    (:BSplineApprox, false, false),
+    (:CubicHermiteSpline, false, false),
+    (:QuinticHermiteSpline, false, false)
+)
     @eval function (interp::$(I){uType})(
             t::AbstractTracer
-        ) where {uType <: AbstractArray{<:Number}}
+    ) where {uType <: AbstractArray{<:Number}}
         return _sct_interpolate(interp, uType, t, $is_der1_zero, $is_der2_zero)
     end
 end
 
 # Some Interpolations require custom overloads on `Dual` due to mutation of caches.
 for I in (
-        :LagrangeInterpolation,
-        :BSplineInterpolation,
-        :BSplineApprox,
-        :CubicHermiteSpline,
-        :QuinticHermiteSpline,
-    )
+    :LagrangeInterpolation,
+    :BSplineInterpolation,
+    :BSplineApprox,
+    :CubicHermiteSpline,
+    :QuinticHermiteSpline
+)
     @eval function (interp::$(I){uType})(d::Dual) where {uType <: AbstractVector}
         p = interp(primal(d))
         t = interp(tracer(d))

src/interpolation_caches.jl

@@ -1220,8 +1220,8 @@ function BSplineApprox(
     end
     for k in 2:(n - 1)
         q[ax_u...,
-            k] = u[ax_u..., k] - sc[k, 1] * u[ax_u..., 1] -
-                 sc[k, h] * u[ax_u..., end]
+        k] = u[ax_u..., k] - sc[k, 1] * u[ax_u..., 1] -
+             sc[k, h] * u[ax_u..., end]
     end
     Q = Array{T, N}(undef, size(u)[1:(end - 1)]..., h - 2)
     for i in 2:(h - 1)

src/interpolation_utils.jl

@@ -191,8 +191,7 @@ function cumulative_integral(A::AbstractInterpolation{<:Number}, cache_parameter
     Base.require_one_based_indexing(A.u)
     idxs = cache_parameters ? (1:(length(A.t) - 1)) : (1:0)
     return cumsum(_integral(A, idx, t1, t2)
-    for (idx, t1, t2) in
-        zip(idxs, @view(A.t[begin:(end - 1)]), @view(A.t[(begin + 1):end])))
+    for (idx, t1, t2) in zip(idxs, @view(A.t[begin:(end - 1)]), @view(A.t[(begin + 1):end])))
 end
 
 function get_parameters(A::LinearInterpolation, idx)

test/derivative_tests.jl

@@ -35,7 +35,8 @@ function test_derivatives(method; args = [], kwargs = [], name::String)
 
         # Interpolation transition points
         for _t in t[2:(end - 1)]
-            if func isa Union{SmoothedConstantInterpolation, BSplineInterpolation, BSplineApprox}
+            if func isa
+               Union{SmoothedConstantInterpolation, BSplineInterpolation, BSplineApprox}
                 # TODO fix interpolations
                 continue
             else

test/interpolation_tests.jl

@@ -1027,7 +1027,6 @@ end
     ut1 = Float32[0.1, 0.2, 0.3, 0.4, 0.5]
     ut2 = Float64[0.1, 0.2, 0.3, 0.4, 0.5]
     for u in (ut1, ut2), t in (ut1, ut2)
-
         interp = @inferred(LinearInterpolation(ut1, ut2))
         for xs in (u, t)
             ys = @inferred(interp(xs))
@@ -1110,7 +1109,6 @@ f_cubic_spline = c -> square(CubicSpline, c)
     iszero_allocations(u, t) = iszero(@allocated(DataInterpolations.munge_data(u, t)))
 
     for T in (String, Union{String, Missing}), dims in 1:3
-
         _u0 = convert(Array{T}, reshape(u0, ntuple(i -> i == dims ? 3 : 1, dims)))
 
         u, t = @inferred(DataInterpolations.munge_data(_u0, t0))