np.concatenate doesn't behave as expected with masked arrays in NumPy 1.25
I'm updating my dependencies and I've spent hours debugging this and I'm stuck on something that should probably be simple. I've searched everywhere and can't find a clear answer... I'm working with an scenario when trying to concatenate two masked arrays using `np.concatenate` in NumPy 1.25. I expected the resulting array to retain the masked values, but it seems to flatten them instead of keeping the structure. Here's a small example of what I'm doing: ```python import numpy as np # Create two masked arrays arr1 = np.ma.array([[1, 2], [3, 4]], mask=[[0, 0], [1, 0]]) arr2 = np.ma.array([[5, 6], [7, 8]], mask=[[0, 1], [0, 0]]) # Attempt to concatenate the masked arrays result = np.concatenate((arr1, arr2), axis=0) print(result) print(result.mask) ``` When I run this code, I get the output: ``` [[1 2] [3 4] [5 6] [7 8]] [[False False] [ True False] [False True] [False False]] ``` I was expecting the resulting masked array to maintain the masks from the original arrays, but it seems to flatten the values instead. I've tried using `np.ma.concatenate` instead, but the behavior remains the same. Is there a specific way to concatenate masked arrays while preserving their masked structure? Am I missing something here? Any insights would be appreciated! Any help would be greatly appreciated! This is part of a larger service I'm building. Could someone point me to the right documentation? This is happening in both development and production on CentOS. Cheers for any assistance! I'm developing on Debian with Python. Any help would be greatly appreciated! Has anyone else encountered this?