Inconsistent results when using np.where on masked arrays in NumPy 1.24.0
I've spent hours debugging this and I'm building a feature where I'm deploying to production and I'm relatively new to this, so bear with me... I've looked through the documentation and I'm still confused about I'm working with an scenario with using `np.where` on masked arrays. I have a masked array created with `np.ma.masked_array`, and I want to replace the masked values with a specific number. However, the results seem inconsistent depending on the shape of the array. Here's a snippet of my code: ```python import numpy as np # Create a masked array data = np.ma.masked_array([1, 2, 3, 4, 5], mask=[0, 1, 0, 1, 0]) # Attempt to use np.where to replace masked values result = np.where(data.mask, -1, data) print(result) ``` When I run this, I expect to see `[-1, 2, -1, 4, -1]`, but I get `masked_array(data=[1, -1, 3, -1, 5], mask=[0, 1, 0, 1, 0])` instead. It seems like the masked values are not being replaced as I expected. I've tried reshaping the array and also using `np.ma.filled(data, -1)` before applying `np.where`, but the output remains the same. What am I missing here? Is there a better way to handle this situation with masked arrays in NumPy 1.24.0? Any insights or best practices would be greatly appreciated! For context: I'm using Python on macOS. Thanks in advance! This is part of a larger service I'm building. I'm developing on Ubuntu 22.04 with Python. Thanks in advance! I've been using Python for about a year now. Is there a better approach?