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Apply JuliaFormatter to fix code formatting #451

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Apply JuliaFormatter to fix code formatting #451

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3 changes: 2 additions & 1 deletion ext/DataInterpolationsMakieExt.jl
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Expand Up @@ -35,7 +35,8 @@ function Makie.convert_arguments(
plotdensity = 10_000,
denseplot = true
)
densex, densey = convert_arguments(
densex,
densey = convert_arguments(
Makie.PointBased(), A; plotdensity = plotdensity, denseplot = denseplot)
return [
Makie.SpecApi.Lines(densex, densey),
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8 changes: 2 additions & 6 deletions ext/DataInterpolationsSparseConnectivityTracerExt.jl
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Expand Up @@ -18,11 +18,9 @@ using DataInterpolations:
CubicHermiteSpline,
# PCHIPInterpolation,
QuinticHermiteSpline,
output_size
output_size#===========##===========#

#===========#
# Utilities #
#===========#

# Limit support to `u` begin an AbstractVector{<:Number} or AbstractMatrix{<:Number},
# to avoid any cases where the output size is dependent on the input value.
Expand Down Expand Up @@ -67,11 +65,9 @@ function _sct_interpolate(
t = hessian_tracer_1_to_1(t, is_der_1_zero, is_der_2_zero)
N = only(output_size(interp))
return Fill(t, N)
end
end#===========##===========#

#===========#
# Overloads #
#===========#

# We assume that with the exception of ConstantInterpolation and LinearInterpolation,
# all interpolations have a non-zero second derivative at some point in the input domain.
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4 changes: 2 additions & 2 deletions src/interpolation_caches.jl
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Expand Up @@ -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)
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3 changes: 2 additions & 1 deletion src/interpolation_utils.jl
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Expand Up @@ -191,7 +191,8 @@ 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)
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2 changes: 2 additions & 0 deletions test/interpolation_tests.jl
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Expand Up @@ -1027,6 +1027,7 @@ 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))
Expand Down Expand Up @@ -1109,6 +1110,7 @@ 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))
Expand Down
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