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.
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