Le 24/02/2012 14:49, Bob Dowling a écrit :
> Thank you all (and especially the gentleman who spotted I was rotating
> in the wrong direction).
No I hadn't ! I had just mentioned the transpose issue for writing
"numpy.dot(data,rotation)".
So in the end the two sign flips cancel each other and Nicol
numpy.dot and numpy.tensordot do exactly what I need.
Thank you all (and especially the gentleman who spotted I was rotating
in the wrong direction).
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I haven't checked correctness, but how about
np.tensordot(rotation, data, axes=1)
Gary R
On 24 February 2012 23:11, Bob Dowling wrote:
> Conceptually, I have a 2-d grid of 2-d vectors. I am representing this
> as an ndarray of shape (2,M,N). I want to apply a 2x2 matrix
> individually to each
Hi,
Le 24/02/2012 13:55, Nicolas Rougier a écrit :
> You should use a (M,N,2) array to store your vectors:
> [...]
> [...]
> numpy.dot(data,rotation)
looking at how numpy.dot generalizes the matrix product* to N-dim
arrays, I came to the same conclusion.
I just suspect that the 'rotation' array sh
You should use a (M,N,2) array to store your vectors:
import math
import numpy
import numpy.random
# Rotation angle
theta = math.pi/6.0
# Grid shape
M = 10
N = 10
# Establish the rotation matrix
c = math.cos(theta)
s = math.sin(theta)
rotation = numpy.array([[c, s],
Conceptually, I have a 2-d grid of 2-d vectors. I am representing this
as an ndarray of shape (2,M,N). I want to apply a 2x2 matrix
individually to each vector in the grid.
However, I can't work out the appropriate syntax for getting the matrix
multiplication to broadcast over the grid. All
