See for example mutable struct Coords x::Float64 y::Float64 z::Float64 end Coords() = Coords(rand(), rand(), rand()) c5 = [Coords() for i in 1:5] c1a = [Coords() for i in 1:1] c1b = Coords() fieldnames(typeof(c1b)) # (:x, :y, :z) fieldnames(c1a) # () fieldnames(typeof(c1a…

Note that is_single(1) == is_single([1]) == false, and that this is quite slow. Dispatch on f(x::AbstractArray) vs. f(x) will I think do what you want. Or x isa AbstractArray if you want a test within a function: julia> @btime is_single(1) 323.328 ns (3 allocations: 144 bytes) false julia> @bti…

I am not sure I understand what you want but I think in this case multiple dispatch is your friend: # First method function myfun(x::Coords) # Do something with a single element end # Second method function myfun(x::Vector{Coords}) # Do something with an array end

OK for the example. I am dealing with an unknown number of types (which could be higher dimensional as well). So I would prefer to have a test like is_single/is_vector/is_matrix.

You can dispatch on the dimension as well myfun(x::Cords) = ... myfun(x::Vector{Coords}) myfun(x::Matrix{Coods}) etc. Maybe the best bet is to choose a type you think is most natural, Vector, Matrix etc. and then for the other methods, just convert it to that type.

Right, but still complicating my code… I finally came up with this: julia> is_single(x) = !hasmethod(length, tuple(typeof(x))) is_single (generic function with 1 method) julia> is_single(c1a) false julia> is_single(c1b) true

If that works for you, then that’s fine. One final thing to note is that you can dispatch on AbstractArray, which will catch all arrays of any dimension. myfun(x::AbstractArray{Coords}) = ... myfun(x::Coords) = myfun([x])

Cool, so julia> issingle(x) = !(typeof(x) <: AbstractArray) issingle (generic function with 1 method) julia> issingle(c1a) false julia> issingle(c1b) true

I rarely come across situations where I don’t know if I’m dealing with a scalar or an array without running the code. This is also why explicit syntactic broadcasting is preferred over dispatching to different methods for scalar vs array (except when you need non-broadcasting behavior, e.g. matrix m…

I’m going to second this. This situation usually comes up from new Julia users coming from Matlab, and the real solution usually is to use broadcasting and not quite vector/matrix versions of your functions.

To elaborate on this, Matlab (and Python and R) want you to push vectorized operations “in” as far as possible so that you can pass large arrays into the primitive operations that are written in fast C code, allowing them to amortize over the entire array and try to make up for the slowness of the o…

Three times agree with the last posters. Coming from Matlab and translating a Matlab serializer code. To be specific, a Julia serializer translating into Matlab compatible structures, so departing from a MAT.jl approach using HDF as intermediate format. So of course I know what is single or Vect…

Handling of single object vs. 1-element array of object

General Usage

Bardo August 7, 2021, 6:22am 14

Here we go: https://discourse.julialang.org/t/how-to-dispatch-this-serializer-function/65981

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Handling of single object vs. 1-element array of object

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