Merge pull request #143 from marius311/MM/vecindex

make vector indexing work with all extrapolations

.travis.yml

@@ -1,7 +1,6 @@
 language: julia
 sudo: false
 julia:
-    - 0.4
     - 0.5
     - nightly
 matrix:

REQUIRE

@@ -1,4 +1,4 @@
-julia 0.4
+julia 0.5
 
 WoodburyMatrices 0.1.5
 Ratios

src/Interpolations.jl

@@ -37,7 +37,7 @@ export
 using Compat
 using WoodburyMatrices, Ratios, AxisAlgorithms
 
-import Base: convert, size, getindex, gradient, promote_rule, ndims, eltype
+import Base: convert, size, getindex, gradient, promote_rule, ndims, eltype, checkbounds
 
 # Julia v0.5 compatibility
 if isdefined(:scaling) import Base.scaling end

src/extrapolation/extrapolation.jl

@@ -54,10 +54,12 @@ function getindex_impl{T,N,ITPT,IT,GT,ET}(etp::Type{Extrapolation{T,N,ITPT,IT,GT
     end
 end
 
-@generated function getindex{T,N,ITPT,IT,GT,ET}(etp::Extrapolation{T,N,ITPT,IT,GT,ET}, xs...)
+@generated function getindex{T,N,ITPT,IT,GT,ET}(etp::Extrapolation{T,N,ITPT,IT,GT,ET}, xs::Number...)
     getindex_impl(etp, xs...)
 end
 
+checkbounds(::AbstractExtrapolation,I...) = nothing
+
 
 function gradient!_impl{T,N,ITPT,IT,GT,ET}(g, etp::Type{Extrapolation{T,N,ITPT,IT,GT,ET}}, xs...)
     coords = [Symbol("xs_", d) for d in 1:N]

test/extrapolation/runtests.jl

@@ -79,6 +79,13 @@ etp100g = extrapolate(interpolate(([10;20],),[100;110], Gridded(Linear())), Flat
 @test @inferred(getindex(etp100g, 5)) == 100
 @test @inferred(getindex(etp100g, 15)) == 105
 @test @inferred(getindex(etp100g, 25)) == 110
+
+
+# check all extrapolations work with vectorized indexing
+for E in [0,Flat(),Linear(),Periodic(),Reflect()]
+    @test (@inferred(getindex(extrapolate(interpolate([0,0],BSpline(Linear()),OnGrid()),E),[1.2, 1.8, 3.1]))) == [0,0,0]
+end
+
 end
 
 include("type-stability.jl")