Backports for 0.6.1

NEWS.md

@@ -258,6 +258,10 @@ This section lists changes that do not have deprecation warnings.
   * `homedir` now determines the user's home directory via `libuv`'s `uv_os_homedir`,
     rather than from environment variables ([#19636]).
 
+  * Workers now listen on an ephemeral port assigned by the OS. Previously workers would
+    listen on the first free port available from 9009 ([#21818]).
+
+
 Library improvements
 --------------------
 

base/abstractarray.jl

@@ -801,7 +801,7 @@ A[iter] = 0
 
 If you supply more than one `AbstractArray` argument, `eachindex` will create an
 iterable object that is fast for all arguments (a `UnitRange`
-if all inputs have fast linear indexing, a `CartesianRange`
+if all inputs have fast linear indexing, a [`CartesianRange`](@ref)
 otherwise).
 If the arrays have different sizes and/or dimensionalities, `eachindex` returns an
 iterable that spans the largest range along each dimension.
@@ -1718,6 +1718,7 @@ For multiple iterable arguments, `f` is called elementwise.
 `foreach` should be used instead of `map` when the results of `f` are not
 needed, for example in `foreach(println, array)`.
 
+# Example
 ```jldoctest
 julia> a = 1:3:7;
 
@@ -1745,6 +1746,7 @@ colons go in this expression. The results are concatenated along the remaining d
 For example, if `dims` is `[1,2]` and `A` is 4-dimensional, `f` is called on `A[:,:,i,j]`
 for all `i` and `j`.
 
+# Examples
 ```jldoctest
 julia> a = reshape(collect(1:16),(2,2,2,2))
 2×2×2×2 Array{Int64,4}:
@@ -1871,6 +1873,7 @@ map(f, A::Union{AbstractArray,AbstractSet,Associative}) = collect_similar(A, Gen
 Transform collection `c` by applying `f` to each element. For multiple collection arguments,
 apply `f` elementwise.
 
+# Examples
 ```jldoctest
 julia> map(x -> x * 2, [1, 2, 3])
 3-element Array{Int64,1}:
@@ -1913,6 +1916,7 @@ end
 Like [`map`](@ref), but stores the result in `destination` rather than a new
 collection. `destination` must be at least as large as the first collection.
 
+# Example
 ```jldoctest
 julia> x = zeros(3);
 

base/abstractarraymath.jl

@@ -16,6 +16,7 @@ Reshape the array `a` as a one-dimensional column vector. The resulting array
 shares the same underlying data as `a`, so modifying one will also modify the
 other.
 
+# Example
 ```jldoctest
 julia> a = [1 2 3; 4 5 6]
 2×3 Array{Int64,2}:
@@ -48,6 +49,7 @@ Remove the dimensions specified by `dims` from array `A`.
 Elements of `dims` must be unique and within the range `1:ndims(A)`.
 `size(A,i)` must equal 1 for all `i` in `dims`.
 
+# Example
 ```jldoctest
 julia> a = reshape(collect(1:4),(2,2,1,1))
 2×2×1×1 Array{Int64,4}:
@@ -101,6 +103,7 @@ imag(x::AbstractArray{<:Real}) = zero(x)
 Return all the data of `A` where the index for dimension `d` equals `i`. Equivalent to
 `A[:,:,...,i,:,:,...]` where `i` is in position `d`.
 
+# Example
 ```jldoctest
 julia> A = [1 2 3 4; 5 6 7 8]
 2×4 Array{Int64,2}:
@@ -125,6 +128,7 @@ end
 
 Reverse `A` in dimension `d`.
 
+# Example
 ```jldoctest
 julia> b = [1 2; 3 4]
 2×2 Array{Int64,2}:
@@ -177,6 +181,7 @@ circshift(a::AbstractArray, shiftamt::DimsInteger) = circshift!(similar(a), a, s
 Circularly shift the data in an array. The second argument is a vector giving the amount to
 shift in each dimension.
 
+# Example
 ```jldoctest
 julia> b = reshape(collect(1:16), (4,4))
 4×4 Array{Int64,2}:
@@ -281,6 +286,7 @@ end
 Construct a matrix by repeating the given matrix (or vector) `m` times in dimension 1 and `n` times in
 dimension 2.
 
+# Examples
 ```jldoctest
 julia> repmat([1, 2, 3], 2)
 6-element Array{Int64,1}:
@@ -337,6 +343,7 @@ repeated. The i-th element of `outer` specifies the number of times that a slice
 i-th dimension of `A` should be repeated. If `inner` or `outer` are omitted, no repetition
 is performed.
 
+# Examples
 ```jldoctest
 julia> repeat(1:2, inner=2)
 4-element Array{Int64,1}:
@@ -408,7 +415,7 @@ _reperr(s, n, N) = throw(ArgumentError("number of " * s * " repetitions " *
                 n = inner[i]
                 inner_indices[i] = (1:n) + ((c[i] - 1) * n)
             end
-            R[inner_indices...] = A[c]
+            fill!(view(R, inner_indices...), A[c])
         end
     end