Using np.reshape causing segmentation fault with large arrays in NumPy 1.25
I'm writing unit tests and I'm working with a segmentation fault when I try to reshape a large NumPy array with np.reshape. The array I'm working with is over 10 million elements. Here's the code that's causing the scenario: ```python import numpy as np # Create a large array large_array = np.arange(10_000_000) # Attempt to reshape it reshaped_array = np.reshape(large_array, (100, 100_000)) ``` When I run this code, I get a segmentation fault and the program crashes. I've checked my system memory, and it seems sufficient to handle this operation. I also tried using `np.resize`, but that doesn't give me the intended shape and fills the rest with zeros, which is not what I need. To troubleshoot, I tested reshaping smaller arrays and they worked fine: ```python small_array = np.arange(1_000) reshaped_small = np.reshape(small_array, (10, 100)) ``` This works perfectly, but anything close to my large array size leads to an immediate crash. I'm using NumPy version 1.25 on a Linux system with Python 3.9. Has anyone faced a similar scenario, or does anyone know why the reshape operation would cause a segmentation fault? Are there any best practices for handling really large arrays in NumPy to avoid this kind of crash? Is there a simpler solution I'm overlooking?