Merge 78b95f4 into 2d81794

README.md

@@ -1,6 +1,7 @@
 # FiniteDiff
 
 [![Build Status](https://travis-ci.org/JuliaDiff/FiniteDiff.jl.svg?branch=master)](https://travis-ci.org/JuliaDiff/FiniteDiff.jl)
+[![codecov](https://codecov.io/gh/JuliaDiff/FiniteDiff.jl/branch/master/graph/badge.svg?token=PnBAscoOiU)](https://codecov.io/gh/JuliaDiff/FiniteDiff.jl)
 
 This package is for calculating derivatives, gradients, Jacobians, Hessians,
 etc. numerically. This library is for maximizing speed while giving a usable

src/FiniteDiff.jl

@@ -9,8 +9,8 @@ _vec(x) = vec(x)
 _vec(x::Number) = x
 
 _mat(x::AbstractMatrix) = x
-_mat(x::StaticVector)   = reshape(x, (axes(x, 1), SOneTo(1)))
-_mat(x::AbstractVector) = reshape(x, (axes(x, 1),  OneTo(1)))
+_mat(x::StaticVector)   = reshape(x, (axes(x, 1),     SOneTo(1)))
+_mat(x::AbstractVector) = reshape(x, (axes(x, 1), Base.OneTo(1)))
 
 include("iteration_utils.jl")
 include("epsilons.jl")

test/out_of_place_tests.jl

@@ -34,18 +34,15 @@ J = FiniteDiff.finite_difference_jacobian(f, x, Val{:forward}, eltype(x),
 @test J ≈ second_derivative_stencil(30)
 @test typeof(J) == typeof(spJ)
 
-#1x1 SVector test
-x = SVector{1}([1.])
-f(x) = x
-J = FiniteDiff.finite_difference_jacobian(f, x, Val{:forward}, eltype(x))
-@test J[1, 1] ≈ 1.0
-@test J isa SMatrix{1,1}
-J = FiniteDiff.finite_difference_jacobian(f, x, Val{:central}, eltype(x))
-@test J[1, 1] ≈ 1.0
-@test J isa SMatrix{1,1}
-J = FiniteDiff.finite_difference_jacobian(f, x, Val{:complex}, eltype(x))
-@test J[1, 1] ≈ 1.0
-@test J isa SMatrix{1,1}
+f = x -> x
+@testset "1x1 test of $x" for
+  (x, y) in ((SVector{1}([1.]), SMatrix{1,1}), ([1.0], Matrix)),
+    difftype in (:forward, :central, :complex)
+
+  J = FiniteDiff.finite_difference_jacobian(f, x, Val{difftype}, eltype(x))
+  @test J[1, 1] ≈ 1.0
+  @test J isa y
+end
 
 x = SVector{1}([1.])
 f(x) = vcat(x, x)