Don't understand how to pass numpy arrays to Julia function

[arbenede]

Let's consider the code fragment

import numpy as np

X = np.ones((10,10))
Y = np.ones((10,10))

x, y = jl.MyModule.compute_something(X, Y, -1.0, 1.0, -1.0, 1.0)

where I call a Julia function defined in the MyModule module. I am getting the error

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Julia: MethodError: no method matching compute_something(::PythonCall.PyArray{Float64, 2, Float64, true, false}, ::PythonCall.PyArray{Float64, 2, Float64, true, false}, ::Float64, ::Float64, ::Float64, ::Float64)
Closest candidates are:
  compute_something(!Matched::Matrix{T}, !Matched::Matrix{T}, ::T, ::T, ::T, ::T) where T<:Real at /opt/julia/compute_something:362

Does this mean that I need to define a new method for compute_something() with ::PythonCall.PyArray arguments? Can I avoid doing that? From the documentation it is not clear what to do in this situation.

[arbenede]

I think I figured it out:

x, y = jl.MyModule.compute_something(jl.Matrix[jl.Float64](target), jl.Matrix[jl.Float64](reference), -5.0, 5.0, -5.0, 5.0)

[cjdoris]

That's one way. Probably a better way is to change the signature of your function to

compute_something(::AbstractMatrix{T}, ::AbstractMatrix{T}, ::T, ::T, ::T, ::T) where {T<:Real}

so that it can take any matrix type. There are lots of matrix types in Julia (see subtypes(AbstractMatrix)) of which Matrix is only one, and your function probably doesn't care what kind of matrix it got.