Is it possible to create a numpy array which points to the same data in a different numpy array (but in different order etc)?

For example:

Code: ------------------------------------------------------------------------------ import numpy as np a = np.arange(10) ids = np.array([0,0,5,5,9,9,1,1]) b = a[ids] a[0] = -1 b[0] #should be -1 if b[0] referenced the same data as a[0] 0 ------------------------------------------------------------------------------

ctypes almost does it for me, but the access is inconvenient. I would like to access b as a regular numpy array:

Code: ------------------------------------------------------------------------------ import numpy as np import ctypes a = np.arange(10) ids = np.array([0,0,5,5,9,9,1,1]) b = [a[id:id+1].ctypes.data_as(ctypes.POINTER(ctypes.c_long)) for id in ids] a[0] = -1 b[0][0] #access is inconvenient -1 ------------------------------------------------------------------------------ Some more information: I've written a finite-element code, and I'm working on optimizing the python implementation. Profiling shows the slowest operation is the re-creation of an array that extracts edge degrees of freedom from the volume of the element (similar to b above). So, I'm trying to avoid copying the data every time, and just setting up 'b' once. The ctypes solution is sub-optimal since my code is mostly vectorized, that is, later I'd like to something like

Code: ------------------------------------------------------------------------------ c[ids] = b[ids] + d[ids] ------------------------------------------------------------------------------

where c, and d are the same shape as b but contain different data.

Any thoughts? If it's not possible that will save me time searching.