On Wed, Mar 13, 2013 at 6:56 AM, Matt U mpuecker@mit.edu wrote:
Is it possible to create a numpy array which points to the same data in a different numpy array (but in different order etc)?
You can do this (easily), but only if the "different order" can be defined in terms of strides. A simple example is a transpose:
In [3]: a = np.arange(12).reshape((3,4))
In [4]: a Out[4]: array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])
In [5]: b = a.T
In [6]: b Out[6]: array([[ 0, 4, 8], [ 1, 5, 9], [ 2, 6, 10], [ 3, 7, 11]])
# b is the transpose of a # but a view on the same data block: # change a: In [7]: a[2,1] = 44
In [8]: a Out[8]: array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 44, 10, 11]])
# b is changed, too. In [9]: b Out[9]: array([[ 0, 4, 8], [ 1, 5, 44], [ 2, 6, 10], [ 3, 7, 11]])
check out "stride tricks" for clever things you can do.
But numpy does require that the data in your array be a contiguous block, in order, so you can't arbitrarily re-arrange it while keeping a view.
HTH, -Chris