Swap Array Data In Numpy

I have many large multidimensional NP arrays (2D and 3D) used in an algorithm. There are numerous iterations in this, and during each iteration the arrays are recalculated by perfo

Solution 1:

Perhaps you could solve this by adding a level of indirection.

You could have an "array holder" class. All that would do is keep a reference to the underlying NumPy array. Implementing a cheap swap operation for a pair of these would be trivial.

If all external references are to these holder objects and not directly to the arrays, none of those references would get invalidated by a swap.

Solution 2:

Even though you way should work as good (I suspect the problem is somewhere else), you can try doing it explicitly:

import numpy as np
A, A_temp = np.frombuffer(A_temp), np.frombuffer(A)

It's not hard to verify that your method works as well:

>>>import numpy as np>>>arr = np.zeros(100)>>>arr2 = np.ones(100)>>>print arr.__array_interface__['data'][0], arr2.__array_interface__['data'][0]
152523144 152228040

>>>arr, arr2 = arr2, arr>>>print arr.__array_interface__['data'][0], arr2.__array_interface__['data'][0]
152228040 152523144

... pointers succsessfully switched

Solution 3:

I realize this is an old question, but for what it's worth you could also swap data between two ndarray buffers (without a temp copy) by performing an xor swap:

A_bytes = A.view('ubyte')
A_temp_bytes = A.view('ubyte')
A_bytes ^= A_temp_bytes
A_temp_bytes ^= A_bytes
A_bytes ^= A_temp_bytes

Since this was done on views, if you look at the original A and A_temp arrays (in whatever their original dtype was) their values should be correctly swapped. This is basically equivalent to the numpy.swap(A, A_temp) you were looking for. It's unfortunate that it requires 3 loops--if this were implemented as a ufunc (maybe it should be) it would be a lot faster.