[Numpy-discussion] Faster way to generate a rotation matrix?
Charles R Harris
Wed Mar 4 03:07:40 CST 2009
On Wed, Mar 4, 2009 at 1:27 AM, Robert Cimrman <email@example.com> wrote:
> Jonathan Taylor wrote:
> > Sorry.. obviously having some copy and paste trouble here. The
> > message should be as follows:
> > Hi,
> > I am doing optimization on a vector of rotation angles tx,ty and tz
> > using scipy.optimize.fmin. Unfortunately the function that I am
> > optimizing needs the rotation matrix corresponding to this vector so
> > it is getting constructed once for each iteration with new values.
> >> >From profiling I can see that the function I am using to construct
> > this rotation matrix is a bottleneck. I am currently using:
> > def rotation(theta):
> > tx,ty,tz = theta
> > Rx = np.array([[1,0,0], [0, cos(tx), -sin(tx)], [0, sin(tx),
> > Ry = np.array([[cos(ty), 0, -sin(ty)], [0, 1, 0], [sin(ty), 0,
> > Rz = np.array([[cos(tz), -sin(tz), 0], [sin(tz), cos(tz), 0],
> > return np.dot(Rx, np.dot(Ry, Rz))
> > Is there a faster way to do this? Perhaps I can do this faster with a
> > small cython module, but this might be overkill?
> > Thanks for any help,
> > Jonathan.
> An alternative to specifying the rotation by the three angles tx,ty and
> tz could be creating directly the rotation matrix given an axis and an
> def make_axis_rotation_matrix(direction, angle):
> Create a rotation matrix corresponding to the rotation around a general
> axis by a specified angle.
> R = dd^T + cos(a) (I - dd^T) + sin(a) skew(d)
> angle : float a
> direction : array d
> d = np.array(direction, dtype=np.float64)
> d /= np.linalg.norm(d)
> eye = np.eye(3, dtype=np.float64)
> ddt = np.outer(d, d)
> skew = np.array([[ 0, d, -d],
> [-d, 0, d],
> [d, -d, 0]], dtype=np.float64)
> mtx = ddt + np.cos(angle) * (eye - ddt) + np.sin(angle) * skew
> return mtx
It might be worth looking at the function in the original problem to see if
it can be cast to a different form. Multiplication by a 3d skew matrix can
also be represented as a cross product. BTW, the formula above is the matrix
exponential of a skew matrix and rotations in higher dimensions can be
represented that way also.
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