Return quaternion from rotation matrix. >>> R = rotation_matrix(0.123, (1, 2, 3)) >>> q = quaternion_from_matrix(R) >>> numpy.allclose(q,

\brief Rebuild the internal data structure.

\brief The split operation finds pivot elements for the child nodes and moves each data element of this node to the appropriate child node.

Return matrix to translate by direction vector. >>> v = numpy.random.random(3) - 0.5 >>> numpy.allclose(v, translation_matrix(v)[:3, 3])

\brief Update minRadius_ and maxRadius_, given that an element was added with distance dist to the pivot.

\brief Update minRange_[i] and maxRange_[i], given that an element was added to the i-th child of the parent that has distance dist to this Node's piv

Return axis, which arc is nearest to point.

Same deal with div()...

Determine whether any of a set of spheres collides with a point in this tree. Templates - `FVectorT`: type of a vector of floats - `IVectorT`: type

Return concatenation of series of transformation matrices. >>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5 >>> numpy.allclose(M, concaten

Return homogeneous rotation matrix from Euler angles and axis sequence. ai, aj, ak : Euler's roll, pitch and yaw angles axes : One of 24 axis

Return 4x4 identity/unit matrix. >>> I = identity_matrix() >>> numpy.allclose(I, numpy.dot(I, I)) True >>> numpy.sum(I), numpy.trace(

Insert data in nbh if it is a near neighbor. Return true iff data was added to nbh.

Insert data in nbh if it is a near neighbor.

Continue rotation in direction of last drag.

Return conjugate of quaternion. >>> q0 = random_quaternion() >>> q1 = quaternion_conjugate(q0) >>> q1[3] == q0[3] and all(q1[:3] == -q0[:

Return multiplication of two quaternions. >>> q = quaternion_multiply([1, -2, 3, 4], [-5, 6, 7, 8]) >>> numpy.allclose(q, [-44, -14, 48, 28])

Return spherical linear interpolation between two quaternions. >>> q0 = random_quaternion() >>> q1 = random_quaternion() >>> q = quaterni

Return uniform random unit quaternion. rand: array like or None Three independent random variables that are uniformly distributed

Return translation vector from translation matrix. >>> v0 = numpy.random.random(3) - 0.5 >>> v1 = translation_from_matrix(translation_matrix(

Construct a new affordance tree. Inputs - `points`: buffer filled with 3-dimensional points. All these points will be included in the tree. - `r_min`

\brief Construct a node of given degree with at most \e capacity data elements and with given pivot.