Performance implementing large 3D array slicing in NumPy 1.25
Does anyone know how to I've encountered a strange issue with I've been working with a large 3D NumPy array in version 1.25, and I'm working with important performance optimization when trying to slice it... My array has a shape of (1000, 1000, 1000), and I need to extract a specific subarray for further processing. However, the slicing operation seems to take an unreasonably long time, especially when I try to slice along multiple axes. Here's my current slicing code: ```python import numpy as np # Create a large 3D array large_array = np.random.rand(1000, 1000, 1000) # Attempting to slice the array sub_array = large_array[100:200, 200:300, 300:400] ``` I expected this operation to be relatively quick, but it's taking several seconds to complete. I tried using `np.copy()` to ensure that the sliced array is independent, but the performance didn't improve: ```python sub_array = np.copy(large_array[100:200, 200:300, 300:400]) ``` Additionally, I checked the memory usage using `memory_profiler`, and it seems like the slicing operation is causing a memory spike. Is there a more efficient way to slice large 3D arrays, or am I missing some optimization technique in NumPy? Any advice on improving this performance scenario would be greatly appreciated! This is part of a larger application I'm building. Any help would be greatly appreciated! I'm developing on Ubuntu 22.04 with Python. Is there a better approach? Any pointers in the right direction? What are your experiences with this? I'm developing on Linux with Python. Any advice would be much appreciated.