Arithmetic with numpy arrays: inconsistency depending on mask

[rcomer]

What happened:
If I multiply a plain numpy array by a dask array, I get a dask array. But if I multiply a numpy masked array by a dask array, I get a numpy masked array.

What you expected to happen:
I would expect to either get a dask array in both cases, or the type of my first operand in both cases.

Minimal Complete Verifiable Example:

import numpy as np
import dask.array as da

plain_numpy_array = np.arange(5)
masked_numpy_array = np.ma.array(np.arange(5), mask=[0, 1, 1, 0, 0])
dask_array = da.from_array(plain_numpy_array)

print(type(plain_numpy_array * dask_array))
print(type(masked_numpy_array * dask_array))

output:

<class 'dask.array.core.Array'>
<class 'numpy.ma.core.MaskedArray'>

Anything else we need to know?:
This relates to #4441, and I note the advice at #4441 (comment) to explicitly convert the numpy arrays to dask arrays before the arithmetic operation. I am attempting to do this for Iris (SciTools/iris#3790) but, while teasing out the details of that, we noticed this inconsistency between plain numpy arrays and masked numpy arrays. I don't think the inconsistency was noted at #4441, and it possibly throws a new light on it?

Environment:

• Dask version: 2.26.0
• Python version: 3.7.8
• Operating System: RHEL7.8
• Install method (conda, pip, source): miniconda

[jthielen]

This appears to be an instance of numpy/numpy#15200, now that Dask Arrays more closely follow a NEP-13/18-style type hierarchy after #6393. If that is the case, then I would believe this is fully an upstream issue in NumPy. To check, what result to you get when you reverse the order of multiplication?

[rcomer]

Thanks @jthielen. If the order of multiplication is reversed, we do indeed get dask arrays for both cases. I've also used your WrappedArray example from numpy/numpy#15200 to reproduce the problem that the dask/wrapped array's mask is ignored (which is what I was originally trying to fix in Iris).

# Masks in different places.
wm = WrappedArray(np.ma.masked_array([1, 3, 5], mask=[False, False, True]), test=2)
m = np.ma.masked_array([2, 0, 1], mask=[False, True, False])

print(wm * m)
print(m * wm)

output:

WrappedArray(
[2 -- --]
{'test': 2}
)

[2 -- 5]

So it looks like I should close this and subscribe to numpy/numpy#15200 instead?

[TomAugspurger]

Thanks for following up. Are we agreed that numpy/numpy#15200 is the root culprit and this can be closed?

[rcomer]

Closing this on the assumption that the problem is with numpy.ma. Thanks for the advice!