bruno Piguet wrote:
>
>Can someone point me to a doc on dot product vectorisation ?
>
As I posted in the past you can try this one liner:
"numpy.array(map(numpy.dot, a, b))
that works for matrix multiply if a, b are (n, 3, 3). "
This would also work if a is (n, 3, 3) and b is (n, 3
On Wed, Jun 10, 2009 at 12:21 AM, bruno Piguet wrote:
> 2009/6/9 Charles R Harris
>
>>
>> Well, in this case you can use complex multiplication and either work with
>> just the x,y components or use two complex components, i.e., [x + 1j*y, z].
>> In the first case you can then do the rotation as
2009/6/9 Charles R Harris
>
> Well, in this case you can use complex multiplication and either work with
> just the x,y components or use two complex components, i.e., [x + 1j*y, z].
> In the first case you can then do the rotation as V*exp(1j*phi).
In the real case, it's a real 3-axes rotation
On Tue, Jun 9, 2009 at 12:56 PM, bruno Piguet wrote:
> Dear all,
>
>Can someone point me to a doc on dot product vectorisation ?
>
> Here is what I try to do :
>
> I've got a rotation function which looks like :
>
> def rotat_scal(phi, V):
> s = math.sin(phi)
> c = math.cos(phi)
>
On 9-Jun-09, at 2:56 PM, bruno Piguet wrote:
> Phi is now of size(n) and V (n, 3).
> (I really whish to have this shape, for direct correspondance to
> file).
>
> The corresponding function looks like :
>
> def rotat_vect(phi, V):
>s = np.sin(phi)
>c = np.cos(phi)
>M = np.zeros((len(p
Dear all,
Can someone point me to a doc on dot product vectorisation ?
Here is what I try to do :
I've got a rotation function which looks like :
def rotat_scal(phi, V):
s = math.sin(phi)
c = math.cos(phi)
M = np.zeros((3, 3))
M[2, 2] = M[1, 1] = c
M[1, 2] = -s
M[2, 1
