Merge pull request #29 from JuliaDiffEq/7

fix v0.7 indexing

Author: ChrisRackauckas

src/array_partition.jl

@@ -144,6 +144,14 @@ end
 
 ## indexing
 
+# Interface for the linear indexing. This is just a view of the underlying nested structure
+@static if VERSION >= v"0.7-"
+  @inline Base.firstindex(A::ArrayPartition) = 1
+  @inline Base.lastindex(A::ArrayPartition) = length(A)
+else
+  @inline Base.endof(A::ArrayPartition) = length(A)
+end
+
 @inline function Base.getindex(A::ArrayPartition, i::Int)
   @boundscheck checkbounds(A, i)
   @inbounds for j in 1:length(A.x)

src/utils.jl

@@ -113,21 +113,6 @@ function recursive_mean(vecvec::Vector{T}) where T<:AbstractArray
   out/length(vecvec)
 end
 
-function recursive_mean(matarr::Matrix{T},region=0) where T<:AbstractArray
-  if region == 0
-    return recursive_mean(vec(matarr))
-  elseif region == 1
-    out = [zeros(matarr[1,i]) for i in 1:size(matarr,2)]
-    for j in 1:size(matarr,2), i in 1:size(matarr,1)
-      out[j] += matarr[i,j]
-    end
-    return out/size(matarr,1)
-  elseif region == 2
-    return recursive_mean(matarr',1)
-  end
-end
-
-
 # From Iterators.jl. Moved here since Iterators.jl is not precompile safe anymore.
 
 # Concatenate the output of n iterators

src/vector_of_array.jl

@@ -18,7 +18,13 @@ DiffEqArray(vec::AbstractVector,ts::AbstractVector) = DiffEqArray(vec, ts, (size
 
 
 # Interface for the linear indexing. This is just a view of the underlying nested structure
-@inline Base.endof(VA::AbstractVectorOfArray) = endof(VA.u)
+@static if VERSION >= v"0.7-"
+  @inline Base.firstindex(VA::AbstractVectorOfArray) = firstindex(VA.u)
+  @inline Base.lastindex(VA::AbstractVectorOfArray) = lastindex(VA.u)
+else
+  @inline Base.endof(VA::AbstractVectorOfArray) = endof(VA.u)
+end
+
 @inline Base.length(VA::AbstractVectorOfArray) = length(VA.u)
 @inline Base.eachindex(VA::AbstractVectorOfArray) = Base.OneTo(length(VA.u))
 @inline Base.iteratorsize(VA::AbstractVectorOfArray) = Base.HasLength()

test/utils_test.jl

@@ -10,18 +10,6 @@ data = convert(Array,randomized)
 A = [[1 2; 3 4],[1 3;4 6],[5 6;7 8]]
 @test recursive_mean(A) ≈ [2.33333333 3.666666666
            4.6666666666 6.0]
-B = Matrix{Matrix{Int64}}(2,3)
-B[1,:] = [[1 2; 3 4],[1 3;4 6],[5 6;7 8]]
-B[2,:] = [[1 2; 3 4],[1 5;4 3],[5 8;2 1]]
-
-ans = [[1 2; 3 4],[1 4; 4 4.5],[5 7; 4.5 4.5]]
-@test recursive_mean(B,1)[1] ≈ ans[1]
-@test recursive_mean(B,1)[2] ≈ ans[2]
-@test recursive_mean(B,1)[3] ≈ ans[3]
-
-ans = [[2.333333333333 4.666666666666; 3.6666666666666 6.0], [2.3333333 3.0; 5.0 2.6666666]]
-@test recursive_mean(B,2)[1] ≈ ans[1]
-@test recursive_mean(B,2)[2] ≈ ans[2]
 
 A = zeros(5,5)
 recursive_unitless_eltype(A) == Float64