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Implement inplace GriddedInterpolation (#496)
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* Implement inplace GriddedInterpolation

* Fix io test

* Add inplace gridded example to doc

* Bump version to 0.14.0
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@jaemolihm
jaemolihm authored Jun 23, 2022
1 parent 9dbcb64 commit 4316c04
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2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,6 +1,6 @@
name = "Interpolations"
uuid = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
version = "0.13.7"
version = "0.14.0"

[deps]
AxisAlgorithms = "13072b0f-2c55-5437-9ae7-d433b7a33950"
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9 changes: 9 additions & 0 deletions docs/src/control.md
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Expand Up @@ -123,6 +123,15 @@ Af = [a for a in A if !ismissing(a)]
itp = interpolate((xf, ), Af, Gridded(Linear()))
```

In-place gridded interpolation is also possible:
```julia
x = 1:4
y = view(rand(4), :)
itp = interpolate!((x,), y, Gridded(Linear()))
y .= 0
@show itp(2.5) # 0
```

## Parametric splines

Given a set a knots with coordinates `x(t)` and `y(t)`, a parametric spline `S(t) = (x(t),y(t))` parametrized by `t in [0,1]` can be constructed with the following code adapted from a [post](http://julia-programming-language.2336112.n4.nabble.com/Parametric-splines-td37794.html#a37818) by Tomas Lycken:
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12 changes: 6 additions & 6 deletions src/gridded/gridded.jl
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Expand Up @@ -16,7 +16,7 @@ const GridIndex{T} = Union{AbstractVector{T}, Tuple}

struct GriddedInterpolation{T,N,TCoefs,IT<:DimSpec{Gridded},K<:Tuple{Vararg{AbstractVector}}} <: AbstractInterpolation{T,N,IT}
knots::K
coefs::Array{TCoefs,N}
coefs::TCoefs
it::IT
end

Expand All @@ -29,12 +29,12 @@ end
"""
GriddedInterpolation(typeOfWeights::Type{<:Real},
knots::NTuple{N, Union{ AbstractVector{T}, Tuple } },
array::AbstractArray{TCoefs,N},
array::AbstractArray{Tel,N},
interpolationType::DimSpec{<:Gridded})
Construct a GriddedInterpolation for generic knots from an AbstractArray
"""
function GriddedInterpolation(::Type{TWeights}, knots::NTuple{N,GridIndex}, A::AbstractArray{TCoefs,N}, it::IT) where {N,TCoefs,TWeights<:Real,IT<:DimSpec{Gridded},pad}
function GriddedInterpolation(::Type{TWeights}, knots::NTuple{N,GridIndex}, A::AbstractArray{Tel,N}, it::IT) where {N,Tel,TWeights<:Real,IT<:DimSpec{Gridded},pad}
isconcretetype(IT) || error("The b-spline type must be a leaf type (was $IT)")

check_gridded(it, knots, axes(A))
Expand All @@ -44,7 +44,7 @@ function GriddedInterpolation(::Type{TWeights}, knots::NTuple{N,GridIndex}, A::A
else
T = typeof(c * first(A))
end
GriddedInterpolation{T,N,TCoefs,IT,typeof(knots)}(knots, A, it)
GriddedInterpolation{T,N,typeof(A),IT,typeof(knots)}(knots, A, it)
end

"""
Expand Down Expand Up @@ -146,7 +146,7 @@ axes(A::GriddedInterpolation) = axes(A.coefs)
itpflag(A::GriddedInterpolation) = A.it

function interpolate(::Type{TWeights}, ::Type{TCoefs}, knots::NTuple{N,GridIndex}, A::AbstractArray{Tel,N}, it::IT) where {TWeights,TCoefs,Tel,N,IT<:DimSpec{Gridded}}
GriddedInterpolation(TWeights, knots, A, it)
GriddedInterpolation(TWeights, knots, copy(A), it)
end
"""
itp = interpolate((nodes1, nodes2, ...), A, interpmode)
Expand All @@ -169,7 +169,7 @@ end
interpolate!(::Type{TWeights}, knots::NTuple{N,GridIndex}, A::AbstractArray{Tel,N}, it::IT) where {TWeights,Tel,N,IT<:DimSpec{Gridded}} =
GriddedInterpolation(TWeights, knots, A, it)
function interpolate!(knots::NTuple{N,GridIndex}, A::AbstractArray{Tel,N}, it::IT) where {Tel,N,IT<:DimSpec{Gridded}}
interpolate!(tweight(A), tcoef(A), knots, A, it)
interpolate!(tweight(A), knots, A, it)
end

lbounds(itp::GriddedInterpolation) = first.(itp.knots)
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144 changes: 78 additions & 66 deletions test/gridded/gridded.jl
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Expand Up @@ -5,75 +5,78 @@ using Interpolations, Test
front(r::AbstractRange) = range(first(r), step=step(r), length=length(r)-1)

for D in (Constant, Linear)
## 1D
a = rand(5)
knots = (range(1, stop=length(a), length=length(a)),)
itp = @inferred(interpolate(knots, a, Gridded(D())))
@inferred(itp(2))
@inferred(itp(CartesianIndex(2)))
for i = 1:length(a)
@test itp(i) ≈ a[i]
@test itp(CartesianIndex(i)) ≈ a[i]
end
@inferred(itp(knots...))
@test itp(knots...) ≈ a
# compare scalar indexing and vector indexing
x = front(knots[1] .+ 0.1)
v = itp(x)
for i = 1:length(x)
@test v[i] ≈ itp(x[i])
end
x = [2.3,2.2] # non-increasing order
v = itp(x)
for i = 1:length(x)
@test v[i] ≈ itp(x[i])
end
# compare against BSpline
itpb = @inferred(interpolate(a, BSpline(D())))
for x in range(1.1, stop=4.9, length=101)
@test itp(x) ≈ itpb(x)
end
for constructor in (interpolate, interpolate!)
isinplace = constructor == interpolate!
## 1D
a = rand(5)
knots = (range(1, stop=length(a), length=length(a)),)
itp = @inferred(constructor(knots, a, Gridded(D())))
@inferred(itp(2))
@inferred(itp(CartesianIndex(2)))
for i = 1:length(a)
@test itp(i) ≈ a[i]
@test itp(CartesianIndex(i)) ≈ a[i]
end
@inferred(itp(knots...))
@test itp(knots...) ≈ a
# compare scalar indexing and vector indexing
x = front(knots[1] .+ 0.1)
v = itp(x)
for i = 1:length(x)
@test v[i] ≈ itp(x[i])
end
x = [2.3,2.2] # non-increasing order
v = itp(x)
for i = 1:length(x)
@test v[i] ≈ itp(x[i])
end
# compare against BSpline
itpb = @inferred(constructor(a, BSpline(D())))
for x in range(1.1, stop=4.9, length=101)
@test itp(x) ≈ itpb(x)
end

knots = (range(0.0, stop=1.0, length=length(a)),)
itp = @inferred(interpolate(knots, a, Gridded(D())))
@test itp([0.1, 0.2, 0.3]) == [itp(0.1), itp(0.2), itp(0.3)]
knots = (range(0.0, stop=1.0, length=length(a)),)
itp = @inferred(constructor(knots, a, Gridded(D())))
@test itp([0.1, 0.2, 0.3]) == [itp(0.1), itp(0.2), itp(0.3)]

## 2D
A = rand(6,5)
knots = (range(1, stop=size(A,1), length=size(A,1)), range(1, stop=size(A,2), length=size(A,2)))
itp = @inferred(interpolate(knots, A, Gridded(D())))
@test parent(itp) === A
@inferred(itp(2, 2))
@inferred(itp(CartesianIndex((2,2))))
for j = 2:size(A,2)-1, i = 2:size(A,1)-1
@test itp(i,j) ≈ A[i,j]
@test itp(CartesianIndex((i,j))) ≈ A[i,j]
end
@test itp(knots...) ≈ A
@inferred(itp(knots...))
# compare scalar indexing and vector indexing
x, y = front(knots[1] .+ 0.1), front(knots[2] .+ 0.6)
v = itp(x,y)
for j = 1:length(y), i = 1:length(x)
@test v[i,j] ≈ itp(x[i],y[j])
end
# check the fallback vector indexing
x = [2.3,2.2] # non-increasing order
y = [3.5,2.8]
v = itp(x,y)
for j = 1:length(y), i = 1:length(x)
@test v[i,j] ≈ itp(x[i],y[j])
end
# compare against BSpline
itpb = @inferred(interpolate(A, BSpline(D())))
for x in range(1.1, stop=5.9, length=101), y in range(1.1, stop=4.9, length=101)
@test itp(x,y) ≈ itpb(x,y)
end
## 2D
A = rand(6,5)
knots = (range(1, stop=size(A,1), length=size(A,1)), range(1, stop=size(A,2), length=size(A,2)))
itp = @inferred(constructor(knots, A, Gridded(D())))
isinplace && @test parent(itp) === A
@inferred(itp(2, 2))
@inferred(itp(CartesianIndex((2,2))))
for j = 2:size(A,2)-1, i = 2:size(A,1)-1
@test itp(i,j) ≈ A[i,j]
@test itp(CartesianIndex((i,j))) ≈ A[i,j]
end
@test itp(knots...) ≈ A
@inferred(itp(knots...))
# compare scalar indexing and vector indexing
x, y = front(knots[1] .+ 0.1), front(knots[2] .+ 0.6)
v = itp(x,y)
for j = 1:length(y), i = 1:length(x)
@test v[i,j] ≈ itp(x[i],y[j])
end
# check the fallback vector indexing
x = [2.3,2.2] # non-increasing order
y = [3.5,2.8]
v = itp(x,y)
for j = 1:length(y), i = 1:length(x)
@test v[i,j] ≈ itp(x[i],y[j])
end
# compare against BSpline
itpb = @inferred(constructor(A, BSpline(D())))
for x in range(1.1, stop=5.9, length=101), y in range(1.1, stop=4.9, length=101)
@test itp(x,y) ≈ itpb(x,y)
end

A = rand(8,20)
knots = ([x^2 for x = 1:8], [0.2y for y = 1:20])
itp = interpolate(knots, A, Gridded(D()))
@test itp(4,1.2) ≈ A[2,6]
A = rand(8,20)
knots = ([x^2 for x = 1:8], [0.2y for y = 1:20])
itp = constructor(knots, A, Gridded(D()))
@test itp(4,1.2) ≈ A[2,6]
end
end

# issue #248
Expand Down Expand Up @@ -101,4 +104,13 @@ using Interpolations, Test
successive_knots_warning = "Successive repeated knots detected. Consider using `move_knots` keyword to Interpolations.deduplicate_knots!"
@test_logs (:warn, successive_knots_warning) Interpolations.deduplicate_knots!(duplicated_knots)
@test allunique(duplicated_knots)

# inplace gridded interpolation, issue #495
knots = ([0., 1.],)
y = view([1., 2.], :)
f1 = interpolate(knots, y, Gridded(Linear()))
f2 = interpolate!(knots, y, Gridded(Linear()))
y .= 0
@test f1(0.5) ≈ 1.5
@test f2(0.5) ≈ 0
end
2 changes: 1 addition & 1 deletion test/io.jl
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Expand Up @@ -51,7 +51,7 @@ using Test
summary(itp) == "8×20 interpolate((::Array{Int64,1},::Array{Float64,1}), ::Array{Float64,2}, (Gridded(Linear()), Gridded(Constant{Nearest}()))) with element type Float64"

# issue #260
A = (1:4)/4
A = collect((1:4)/4)
itp = interpolate((range(0.0, stop=0.3, length=4),), A, Gridded(Linear()))
io = IOBuffer()
show(io, MIME("text/plain"), itp)
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2 comments on commit 4316c04

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@mkitti mkitti commented on 4316c04 Jul 11, 2022

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Registration pull request created: JuliaRegistries/General/63991

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.14.0 -m "<description of version>" 4316c044da4594783b287048e0fbaa7c885ca408
git push origin v0.14.0

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