在 numpy ndarray 的子类中覆盖 .T（转置）

Question

I have a three dimensional dataset where the 1st dimension gives the type of the variable and the 2nd and 3rd dimensions are spatial indexes. I am attempting to make this data more user friendly by creating a subclass of ndarray containing the data, but with attributes that have sensible names that point to the appropriate variable dimension. One of the variable types is temperature, which I would like to represent with the attribute .T . I attempt to set it like this:

self.T = self[8,:,:]

However, this clashes with the underlying numpy attribute for transposing an array. Normally, overriding a class attribute is trivial, however in this case I get an exception when I try to re-write the attribute. The following is a minimal example of the same problem:

import numpy as np

class foo(np.ndarray):
    def __new__(cls, input_array):
        obj = np.asarray(input_array).view(cls)
        obj.T = 100.0
        return obj

foo([1,2,3,4])

results in:

Traceback (most recent call last):
  File "tmp.py", line 9, in <module>
    foo([1,2,3,4])
  File "tmp.py", line 6, in __new__
    obj.T = 100.0
AttributeError: attribute 'T' of 'numpy.ndarray' objects is not writable

I have tried using setattr(obj, 'T', 100.0) to set the attribute, but the result is the same.

Obviously, I could just give up and name my attribute .temperature , or something else. However .T will be much more eloquent for the subsequent mathematical expressions which will be done with these data objects. How can I force python/numpy to override this attribute?

Answer 1

For np.matrix subclass, as defined in np.matrixlib.defmatrix:

@property
def T(self):
    """
    Returns the transpose of the matrix.
    ....
    """
    return self.transpose()

Answer 2

Following Mad Physicist and hpaulj's lead, the solution to my minimal working example is:

import numpy as np

class foo(np.ndarray):
    def __new__(cls, input_array):
        obj = np.asarray(input_array).view(cls)
        return obj

    @property
    def T(self):
        return 100.0

x = foo([1,2,3,4])
print("T is", x.T)

Which results in:

T is [1 2 3 4]

Answer 3

T is not a conventional attribute that lives in a__dict__ or __slots__ . In fact, you can see this immediately because the result of T changes if you modify the shape or contents of an array.

Since ndarray is a class written in C, it has special descriptors for the dynamic attributes it exposes. T is one of these dynamic attributes, defined as a PyGetSetDef structure. You can't override it by simple assignment, because there is nothing to assign to, but you can make a descriptor that overrides it at the class level.

As @hpaulj's answer suggests, the simplest solution may be to use aproperty to implement the descriptor protocol for you:

import numpy as np

class foo(np.ndarray):
    @property
    def T(self):
        return self[8, :, :]

More complicated alternatives would be to make your own descriptor type, or even to extend the class in C and write your own PyGetSetDef structure. It all depends on what you are trying to achieve.