quaternion.quaternion_time_series

Class appending_array

appending_array(shape, dtype=<class 'numpy.float64'>, initial_array=None)

Source: quaternion/quaternion_time_series.py

Methods

append

append(self, row)

Source: quaternion/quaternion_time_series.py

a

a

Source: quaternion/quaternion_time_series.py

angular_velocity

angular_velocity(R, t)

Source: quaternion/quaternion_time_series.py

integrate_angular_velocity

integrate_angular_velocity(Omega, t0, t1, R0=None, tolerance=1e-12)

Source: quaternion/quaternion_time_series.py

Compute frame with given angular velocity

Parameters

  • Omega: callable, tuple

    Angular velocity from which to compute frame. Can be 1) a 2-tuple of float arrays (t, v) giving the angular velocity vector at a series of times, 2) a function of time that returns the 3-vector angular velocity, or 3) a function of time and orientation (t, R) that returns the 3-vector angular velocity In case 1, the angular velocity will be interpolated to the required times. Note that accuracy is poor in case 1.

  • t0: float

    Initial time

  • t1: float

    Final time

  • R0: optional, quaternion

    Initial frame orientation. Defaults to 1 (the identity orientation).

  • tolerance: optional, float

    Absolute tolerance used in integration. Defaults to 1e-12.

Returns

  • t: float array

  • R: quaternion array

minimal_rotation

minimal_rotation(R, t, iterations=2)

Source: quaternion/quaternion_time_series.py

Adjust frame so that there is no rotation about z' axis The output of this function is a frame that rotates the z axis onto the same z' axis as the input frame, but with minimal rotation about that axis. This is done by pre-composing the input rotation with a rotation about the z axis through an angle gamma, where dgamma/dt = 2*(dR/dt * z * R.conjugate()).w This ensures that the angular velocity has no component along the z' axis. Note that this condition becomes easier to impose the closer the input rotation is to a minimally rotating frame, which means that repeated application of this function improves its accuracy. By default, this function is iterated twice, though a few more iterations may be called for.

Parameters

  • R: quaternion array

    Time series describing rotation

  • t: float array

    Corresponding times at which R is measured

  • iterations: int [defaults to 2]

    Repeat the minimization to refine the result

slerp

slerp(R1, R2, t1, t2, t_out)

Source: quaternion/quaternion_time_series.py

Spherical linear interpolation of rotors This function uses a simpler interface than the more fundamental slerp_evaluate and slerp_vectorized functions. The latter are fast, being implemented at the C level, but take input tau instead of time. This function adjusts the time accordingly.

Parameters

  • R1: quaternion

    Quaternion at beginning of interpolation

  • R2: quaternion

    Quaternion at end of interpolation

  • t1: float

    Time corresponding to R1

  • t2: float

    Time corresponding to R2

  • t_out: array of floats, float

    Times to which the rotors should be interpolated

squad

squad(R_in, t_in, t_out, unflip_input_rotors=False)

Source: quaternion/quaternion_time_series.py

Spherical "quadrangular" interpolation of rotors with a cubic spline This is typically the best way to interpolate rotation timeseries. It uses the analog of a cubic spline, except that the interpolant is confined to the rotor manifold in a natural way. Alternative methods involving interpolation of other coordinates on the rotation group or normalization of interpolated values give bad results. The results from this method are continuous in value and first derivative everywhere, including around the sampling locations. The input R_in rotors are assumed to be reasonably continuous (no sign flips), and the input t arrays are assumed to be sorted. No checking is done for either case, and you may get silently bad results if these conditions are violated. The first dimension of R_in must have the same size as t_in, but may have additional axes following. This function simplifies the calling, compared to squad_evaluate (which takes a set of four quaternions forming the edges of the "quadrangle", and the normalized time tau) and squad_vectorized (which takes the same arguments, but in array form, and efficiently loops over them).

Parameters

  • R_in: array of quaternions

    A time-series of rotors (unit quaternions) to be interpolated

  • t_in: array of float

    The times corresponding to R_in

  • t_out: array of float

    The times to which R_in should be interpolated

  • unflip_input_rotors: optional, bool

    If True, this function calls unflip_rotors on the input, to ensure that the rotors are more continuous than not. Defaults to False.

unflip_rotors

unflip_rotors(q, axis=-1, inplace=False)

Source: quaternion/quaternion_time_series.py

Flip signs of quaternions along axis to ensure continuity Quaternions form a "double cover" of the rotation group, meaning that if q represents a rotation, then -q represents the same rotation. This is clear from the way a quaternion is used to rotate a vector v: the rotated vector is q * v * q.conjugate(), which is precisely the same as the vector resulting from (-q) * v * (-q).conjugate(). Some ways of constructing quaternions (such as converting from rotation matrices or other representations) can result in unexpected sign choices. For many applications, this will not be a problem. But if, for example, the quaternions need to be interpolated or differentiated, the results may be surprising. This function flips the signs of successive quaternions (along some chosen axis, if relevant), so that successive quaternions are as close as possible while still representing the same rotations.

Parameters

  • q: array_like

    Quaternion array to modify

  • axis: optional, int

    Axis along which successive quaternions will be compared. Default value is the last axis of the quaternion array.

  • inplace: optional, bool

    If True, modify the data in place without creating a copy; if False (the default), a new array is created and returned.

Returns

  • q_out: array_like

    An array of precisely the same shape as the input array, differing only by factors of precisely -1 in some elements.