My guess is that this has something to do with currently lacking facilities
for type inference on global variables. If you wrap your indirect approach
in a function, you get the predictable behavior:
julia> function f()
xvals = [ d for d in 1.0:10.0 ]
xxvals = [ (d,d) for d in xvals ]
return xxvals
end
f (generic function with 1 method)
julia> f()
10-element Array{(Float64,Float64),1}:
(1.0,1.0)
(2.0,2.0)
(3.0,3.0)
(4.0,4.0)
(5.0,5.0)
(6.0,6.0)
(7.0,7.0)
(8.0,8.0)
(9.0,9.0)
(10.0,10.0)
On Wednesday, May 13, 2015 at 7:44:26 AM UTC-4, [email protected] wrote:
>
> Currently having a mental block about this here. Why does this code:
>
> julia> [(d,d) for d in 1.0:10.0]
> 10-element Array{(Float64,Float64),1}:
> (1.0,1.0)
> (2.0,2.0)
> (3.0,3.0)
> (4.0,4.0)
> (5.0,5.0)
> (6.0,6.0)
> (7.0,7.0)
> (8.0,8.0)
> (9.0,9.0)
> (10.0,10.0)
>
> produce an array of floats, but this less direct version:
>
> julia> x_vals = [d for d in 1.0:10.0]
> 10-element Array{Float64,1}:
> 1.0
> 2.0
> 3.0
> 4.0
> 5.0
> 6.0
> 7.0
> 8.0
> 9.0
> 10.0
>
> julia> [(d,d) for d in x_vals]
> 10-element Array{(Any,Any),1}:
> (1.0,1.0)
> (2.0,2.0)
> (3.0,3.0)
> (4.0,4.0)
> (5.0,5.0)
> (6.0,6.0)
> (7.0,7.0)
> (8.0,8.0)
> (9.0,9.0)
> (10.0,10.0)
>
> produces an array of Any?
>
