Subclassing numpy ndarray problem

Question

I would like to subclass numpy ndarray. However, I cannot change the array. Why self = ... does not change the array? Thanks.

import numpy as np

class Data(np.ndarray):

    def __new__(cls, inputarr):
        obj = np.asarray(inputarr).view(cls)
        return obj

    def remove_some(self, t):
        test_cols, test_vals = zip(*t)
        test_cols = self[list(test_cols)]
        test_vals = np.array(test_vals, test_cols.dtype)

        self = self[test_cols != test_vals] # Is this part correct?

        print len(self) # correct result

z = np.array([(1,2,3), (4,5,6), (7,8,9)],
    dtype=[('a', int), ('b', int), ('c', int)])
d = Data(z)
d.remove_some([('a',4)])

print len(d)  # output the same size as original. Why?

Answer 1

The reason you are not getting the result you expect is because you are re-assigning self within the method remove_some. You are just creating a new local variable self. If your array shape were not to change, you could simply do self[:] = ... and you could keep the reference to self and all would be well, but you are trying to change the shape of self. Which means we need to re-allocate some new memory and change where we point when we refer to self.

I don't know how to do this. I thought it could be achieved by __array_finalize__ or __array__ or __array_wrap__. But everything I've tried is falling short.

Now, there's another way to go about this that doesn't subclass ndarray. You can make a new class that keeps an attribute that is an ndarray and then override all the usual __add__, __mul__, etc.. Something like this:

Class Data(object):
    def __init__(self, inarr):
        self._array = np.array(inarr)
    def remove_some(x):
        self._array = self._array[x]
    def __add__(self, other):
        return np.add(self._array, other)

Well, you get the picture. It's a pain to override all the operators, but in the long run, I think more flexible.

You'll have to read this thoroughly to do it right. There are methods like __array_finalize__ that need to be called a the right time to do "cleanup".

Answer 2

Perhaps make this a function, rather than a method:

import numpy as np

def remove_row(arr,col,val):
    return arr[arr[col]!=val]

z = np.array([(1,2,3), (4,5,6), (7,8,9)],
    dtype=[('a', int), ('b', int), ('c', int)])

z=remove_row(z,'a',4)
print(repr(z))

# array([(1, 2, 3), (7, 8, 9)], 
#       dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<i4')])

---

Or, if you want it as a method,

import numpy as np

class Data(np.ndarray):

    def __new__(cls, inputarr):
        obj = np.asarray(inputarr).view(cls)
        return obj

    def remove_some(self, col, val):
        return self[self[col] != val]

z = np.array([(1,2,3), (4,5,6), (7,8,9)],
    dtype=[('a', int), ('b', int), ('c', int)])
d = Data(z)
d = d.remove_some('a', 4)
print(d)

The key difference here is that remove_some does not try to modify self, it merely returns a new instance of Data.

Answer 3

I tried to do the same, but it is really very complex to subclass ndarray.

If you only have to add some functionality, I would suggest to create a class which stores the array as attribute.

class Data(object):

    def __init__(self, array):
        self.array = array

    def remove_some(self, t):
        //operate on self.array
        pass

d = Data(z)
print(d.array)