Apply NumPy's random functions using awkward inputs?

Hello,

Thanks for the excellent work!! So I’m working on an application where I want to apply np.random.normal on each element of the awkward array. I’m trying to do the followings:

a = ak.from_iter([[8],[7],[9,11],[5]])
f = lambda x : np.random.normal(x,x*0.15)
# f(a) gives errors
f_arr = np.frompyfunc(f, 1, 1) # try making ufunc
# f_arr(a) also gives errors

I’m wondering if anyone here have suggestions as to what I should do in this case. 😄 Our solution right now is to basically do nested loops to change each element, which is not very efficient.

[jpivarski]

I’ll want to start a new submodule for this, so it might be done before there’s enough of a pattern to build on. However, I could start with the randomization functions and if there are any others you need, it should be clear how to build on that pattern.

[jpivarski]

Your NumpyArray is unnecessarily wrapped (Array) and unwrapped (.layout), but otherwise, yes. Also, the general function would work for all data structures. (There’s an internal ak._util.broadcast_and_apply that generalizes the process of unwrapping and re-wrapping. That’s how all of the ufuncs work. But since it’s an internal function and not a part of the stable API, you should use the technique you use here.)

Oh! I guess the reason you wrapped and unwrapped the NumpyArray is because you didn’t know it was called that. :) Your can use ak.layout.NumpyArray instead of Array and then .layout. The effect is the same, but it’s more direct.