如何为其他 numpy 数组创建一个 numpy 视图数组

Question

I have a large list of objects that include numpy arrays as attributes. They each have methods that manipulate the array. I would like to create a single 2D numpy array that stores the other arrays and updates when the individual numpy arrays are manipulated.

This is easy to accomplish with lists as you simply need to create a list of references to other lists.

>>> x = [1,2,3]
>>> y = [4,5,6]
>>> z = [x,y] # stores reference to x and y
>>> x[0] = 10
>>> z
[[10,2,3],[4,5,6]]

However, doing the same in numpy creates copies of the object.

>>> x = np.array([1,2,3])
>>> y = np.array([4,5,6])
>>> z = np.array([x,y])  # setting the optional argument copy = False didn't help either
>>> id(x)
140673084678272
>>> id(z[0])
140673084678512

I guessed that setting copy = False wouldn't work because I'm passing a new concatenated list object which hadn't existed before. Is there a way to create z where it's elements are references to the numpy arrays x and y ?

I recognize that references in numpy are typically accomplished with views , but this seems to be a different use case. Creating a view object of a single numpy array is fairly straightforward, but I'm unsure how to store in a numpy array, N view objects from N individual numpy arrays.

Answer 1

One solution would be to stack() or concatenate() arrays i certain dimensions match. if dims don't match - use padding, store dimensions in separate list and then stack.

Answer 2

Create an empty object array of the required length (it contains Nulls). Assign the list to a view of the whole array. Done. And yes, the original objects update when the individual numpy arrays are manipulated. Note that operations on the whole array, however, always seem to create copies even when you'd normally expect in-place operations.

import numpy as np
x = np.array([1,2,3])
y = np.array([4,5,6])
z = np.empty(2, dtype=object)
z[:] = [x,y]
print(id(x))
print(id(z[0]))

Output:

140293181519152
140293181519152