setting Numpy array elements with a class object

Question

I have created a class called Tensor

import numpy as np

class Tensor:
    def __init__(self, data):
        self.data = np.array(data)

and I'd like to set elements of a numpy array using Tensor:

x = np.array([[1,2,3,4],[4,3,2,1]])
x[:,::2] = Tensor([[0,0],[1,1]])

but it results an error ValueError: setting an array element with a sequence.

one workaround is to retrieve Tensor's data attribute: x[:,::2] = Tensor([[0,0],[1,1]]).data, but I'd like to know how to do it without manually retrieving anything, like when you can set values with a list or numpy array: x[:,::2] = [[0,0],[1,1]] or x[:,::2] = np.array([[0,0],[1,1]])

Mechanic Pig · Accepted Answer · 2022-05-29 08:59:23Z

3

Numpy array objects all follow a protocol, as long as you implement __array__ method, you can use the object as an array:

>>> class Tensor:
...     def __init__(self, data):
...         self.data = np.array(data)
...     def __array__(self, dtype=None):
...         return self.data    # self.data.astype(dtype, copy=False) maybe better
...
>>> x[:,::2] = Tensor([[0,0],[1,1]])
>>> x
array([[0, 2, 0, 4],
       [1, 3, 1, 1]])

Reference: Writing custom array containers

answered May 29, 2022 at 8:59

Mechanic Pig

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2 Comments

Sam-gege Over a year ago

Thanks for your answer it works! BTW, what is the use of dtype=None? when I delete it it results a TypeError: __array__() takes 1 positional argument but 2 were given. When do you have to specify a dtype? Thanks!

Mechanic Pig Over a year ago

@Sam-gege This is the signature format required by numpy. Usually, different dtype expect to get arrays of corresponding data types.

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