\brief Compute the k nearest neighbors of data in the tree. For k=1, isPivot is true if the nearest neighbor is a pivot (which is important during rem

\brief Return in nbhQueue the k nearest neighbors of data. For k=1, return true if the nearest neighbor is a pivot. (which is important during removal

\brief Return in nbhQueue the elements that are within distance radius of data.

I said we use *all* the neighbors

Return orthogonalization matrix for crystallographic cell coordinates. Angles are expected in degrees. The de-orthogonalization matrix is th

Place Arcball, e.g. when window size changes. center : sequence[2] Window coordinates of trackball center. radius : float

\brief Convert the internal data structure used for storing neighbors to the vector that NearestNeighbor API requires.

Return projection plane and perspective point from projection matrix. Return values are same as arguments for projection_matrix function: poi

Return matrix to project onto plane defined by point and normal. Using either perspective point, projection direction, or none of both. If p

Return quaternion for rotation about axis. >>> q = quaternion_about_axis(0.123, (1, 0, 0)) >>> numpy.allclose(q, [0.06146124, 0, 0, 0.9981094

Return quaternion from Euler angles and axis sequence. ai, aj, ak : Euler's roll, pitch and yaw angles axes : One of 24 axis sequences as str

Return inverse of quaternion. >>> q0 = random_quaternion() >>> q1 = quaternion_inverse(q0) >>> numpy.allclose(quaternion_multiply(q0, q1)

Return uniform random rotation matrix. rnd: array like Three independent random variables that are uniformly distributed between

Return array of random doubles in the half-open interval [0.0, 1.0). >>> v = random_vector(10000) >>> numpy.all(v >= 0.0) and numpy.all(v < 1

Return mirror plane point and normal vector from reflection matrix. >>> v0 = numpy.random.random(3) - 0.5 >>> v1 = numpy.random.random(3) - 0

Return matrix to mirror at plane defined by point and normal vector. >>> v0 = numpy.random.random(4) - 0.5 >>> v0[3] = 1.0 >>> v1 = numpy