Generating A Remapped Numpy Array As A View.

A toy-case for my problem: I have a numpy array of size, say, 1000: import numpy as np a = np.arange(1000) I also have a 'projection array' p which is a mapping from a to another

Solution 1:

What your asking for isn't practical and that's why the numpy folks haven't implemented it. You could do it yourself with something like:

classFancyView(object):def__init__(self, array, index):
        self._array = array
        self._index = index.copy()
    def__array__(self):
        returnself._array[self._index]
    def__getitem__(self, index):
        returnself._array[self._index[index]]

b = FancyView(a, p)

But notice that the expensive a[p] operation will get called every time you use b as an array. There is no other practice way of making a 'view' of this kind. Numpy can get away with using views for basic slicing because it can manipulate the strides, but there is no way to do something like this using strides.

If you only need parts of b you might be able to get some time savings by indexing the fancy view instead of using it as an array.