implementing np.split on non-contiguous arrays in NumPy 1.24
I'm working through a tutorial and I've looked through the documentation and I'm still confused about I'm working with unexpected behavior when using `np.split` with non-contiguous arrays in NumPy 1.24... My goal is to split a 2D array into multiple sub-arrays along the first axis, but it seems the resulting arrays are not what I expected. Here's what I've tried: First, I created a non-contiguous array using slicing: ```python import numpy as np # Create a 4x4 array arr = np.arange(16).reshape(4, 4) sub_arr = arr[::2] # Take every second row to create a non-contiguous array ``` Now, when I attempt to split this non-contiguous array into two parts, I do the following: ```python result = np.split(sub_arr, 2) ``` However, I receive the following behavior message: ``` ValueError: array split does not result in an equal division ``` This is puzzling because I expected `sub_arr` to have 2 rows, which should allow for a split of 1 row each. I also verified the shape of `sub_arr`: ```python print(sub_arr.shape) # Output: (2, 4) ``` Could it be that `np.split` does not work correctly with non-contiguous arrays? Additionally, I have considered using the `np.array_split` function as an alternative, but I would like to understand why `np.split` is failing in this case. Is there something specific about the way `np.split` calculates indices for non-contiguous arrays? Any insights would be much appreciated. How would you solve this? I've been using Python for about a year now. Any advice would be much appreciated.