fix doctests

doc/src/devdocs/subarrays.md

@@ -19,14 +19,14 @@ julia> A = rand(2,3,4);
 
 julia> S1 = view(A, :, 1, 2:3)
 2×2 view(::Array{Float64, 3}, :, 1, 2:3) with eltype Float64:
- 0.200586  0.066423
- 0.298614  0.956753
+ 0.762657  0.545403
+ 0.66338   0.866199
 
 julia> S2 = view(A, 1, :, 2:3)
 3×2 view(::Array{Float64, 3}, 1, :, 2:3) with eltype Float64:
- 0.200586  0.066423
- 0.246837  0.646691
- 0.648882  0.276021
+ 0.762657  0.545403
+ 0.704125  0.0802701
+ 0.886174  0.535537
 ```
 ```@meta
 DocTestSetup = nothing

doc/src/manual/performance-tips.md

@@ -77,12 +77,12 @@ julia> function sum_global()
        end;
 
 julia> @time sum_global()
-  0.009639 seconds (7.36 k allocations: 300.310 KiB, 98.32% compilation time)
-496.84883432553846
+  0.011001 seconds (9.30 k allocations: 417.044 KiB, 98.77% compilation time)
+503.4466694614652
 
 julia> @time sum_global()
-  0.000140 seconds (3.49 k allocations: 70.313 KiB)
-496.84883432553846
+  0.000075 seconds (3.49 k allocations: 70.156 KiB)
+503.4466694614652
 ```
 
 On the first call (`@time sum_global()`) the function gets compiled. (If you've not yet used [`@time`](@ref)
@@ -113,12 +113,12 @@ julia> function sum_arg(x)
        end;
 
 julia> @time sum_arg(x)
-  0.006202 seconds (4.18 k allocations: 217.860 KiB, 99.72% compilation time)
-496.84883432553846
+  0.007258 seconds (4.24 k allocations: 226.052 KiB, 99.74% compilation time)
+503.4466694614652
 
 julia> @time sum_arg(x)
   0.000005 seconds (1 allocation: 16 bytes)
-496.84883432553846
+503.4466694614652
 ```
 
 The 1 allocation seen is from running the `@time` macro itself in global scope. If we instead run
@@ -128,8 +128,8 @@ the timing in a function, we can see that indeed no allocations are performed:
 julia> time_sum(x) = @time sum_arg(x);
 
 julia> time_sum(x)
-  0.000001 seconds
-496.84883432553846
+  0.000002 seconds
+503.4466694614652
 ```
 
 In some situations, your function may need to allocate memory as part of its operation, and this

stdlib/Random/docs/src/index.md

@@ -147,20 +147,20 @@ Scalar and array methods for `Die` now work as expected:
 
 ```jldoctest Die; setup = :(Random.seed!(1))
 julia> rand(Die)
-Die(15)
+Die(6)
 
 julia> rand(MersenneTwister(0), Die)
 Die(11)
 
 julia> rand(Die, 3)
 3-element Vector{Die}:
- Die(18)
- Die(5)
+ Die(15)
+ Die(19)
  Die(4)
 
 julia> a = Vector{Die}(undef, 3); rand!(a)
 3-element Vector{Die}:
- Die(5)
+ Die(17)
  Die(20)
  Die(15)
 ```
@@ -175,13 +175,13 @@ In order to define random generation out of objects of type `S`, the following m
 julia> Random.rand(rng::AbstractRNG, d::Random.SamplerTrivial{Die}) = rand(rng, 1:d[].nsides);
 
 julia> rand(Die(4))
-3
+1
 
 julia> rand(Die(4), 3)
 3-element Vector{Any}:
+ 3
  4
  1
- 1
 ```
 
 Given a collection type `S`, it's currently assumed that if `rand(::S)` is defined, an object of type `eltype(S)` will be produced. In the last example, a `Vector{Any}` is produced; the reason is that `eltype(Die) == Any`. The remedy is to define `Base.eltype(::Type{Die}) = Int`.

stdlib/Random/src/misc.jl

@@ -53,13 +53,13 @@ number generator, see [Random Numbers](@ref).
 # Examples
 ```jldoctest
 julia> Random.seed!(3); randstring()
-"Y7m62wOj"
+"vZmAMp3z"
 
 julia> randstring(MersenneTwister(3), 'a':'z', 6)
 "ocucay"
 
 julia> randstring("ACGT")
-"ATTTGCGT"
+"CAAACACC"
 ```
 
 !!! note

stdlib/SparseArrays/src/sparsematrix.jl

@@ -1585,13 +1585,13 @@ argument specifies a random number generator, see [Random Numbers](@ref).
 # Examples
 ```jldoctest; setup = :(using Random; Random.seed!(1234))
 julia> sprand(Bool, 2, 2, 0.5)
-2×2 SparseMatrixCSC{Bool, Int64} with 1 stored entry:
- ⋅  ⋅
- ⋅  1
+2×2 SparseMatrixCSC{Bool, Int64} with 4 stored entries:
+ 1  1
+ 1  1
 
 julia> sprand(Float64, 3, 0.75)
 3-element SparseVector{Float64, Int64} with 1 stored entry:
-  [3]  =  0.298614
+  [1]  =  0.66338
 ```
 """
 function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T}=eltype(rfn(r, 1))) where T
@@ -1632,9 +1632,9 @@ argument specifies a random number generator, see [Random Numbers](@ref).
 # Examples
 ```jldoctest; setup = :(using Random; Random.seed!(0))
 julia> sprandn(2, 2, 0.75)
-2×2 SparseMatrixCSC{Float64, Int64} with 2 stored entries:
-  ⋅   0.586617
-  ⋅   0.297336
+2×2 SparseMatrixCSC{Float64, Int64} with 4 stored entries:
+  1.49137  1.09378
+ -1.56692  1.1035
 ```
 """
 sprandn(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) =