quaternion.calculus

definite_integral

definite_integral(f, t)


Source: quaternion/calculus.py

derivative

derivative(f, t)


Source: quaternion/calculus.py

Fourth-order finite-differencing with non-uniform time steps The formula for this finite difference comes from Eq. (A 5b) of "Derivative formulas and errors for non-uniformly spaced points" by M. K. Bowen and Ronald Smith. As explained in their Eqs. (B 9b) and (B 10b), this is a fourth-order formula -- though that's a squishy concept with non-uniform time steps. TODO: If there are fewer than five points, the function should revert to simpler (lower-order) formulas.

fd_definite_integral

fd_definite_integral(f, t)


Source: quaternion/calculus.py

fd_derivative

fd_derivative(f, t)


Source: quaternion/calculus.py

Fourth-order finite-differencing with non-uniform time steps The formula for this finite difference comes from Eq. (A 5b) of "Derivative formulas and errors for non-uniformly spaced points" by M. K. Bowen and Ronald Smith. As explained in their Eqs. (B 9b) and (B 10b), this is a fourth-order formula -- though that's a squishy concept with non-uniform time steps. TODO: If there are fewer than five points, the function should revert to simpler (lower-order) formulas.

spline_definite_integral

spline_definite_integral(f, t, t1=None, t2=None, axis=0)


Source: quaternion/calculus.py

spline_derivative

spline_derivative(f, t, derivative_order=1, axis=0)


Source: quaternion/calculus.py

spline_evaluation

spline_evaluation(
f,
t,
t_out=None,
axis=None,
spline_degree=3,
derivative_order=0,
definite_integral_bounds=None,
)


Source: quaternion/calculus.py

Approximate input data using a spline and evaluate Note that this function is somewhat more general than it needs to be, so that it can be reused for closely related functions involving derivatives, antiderivatives, and integrals.

Parameters

• f: ...) array_like, N, (...

Real or complex function values to be interpolated.

• t: ) array_like, (N

An N-D array of increasing real values. The length of f along the interpolation axis must be equal to the length of t. The number of data points must be larger than the spline degree.

• t_out: ) array_like [defaults to None], (M, None

The new values of t on which to evaluate the result. If None, it is assumed that some other feature of the data is needed, like a derivative or antiderivative, which are then output using the same t values as the input.

• axis: int [defaults to None], None

The axis of f with length equal to the length of t. If None, this function searches for an axis of equal length in reverse order -- that is, starting from the last axis of f. Note that this feature is helpful when f is one-dimensional or will always satisfy that criterion, but is dangerous otherwise. Caveat emptor.

• spline_degree: int [defaults to 3]

Degree of the interpolating spline. Must be 1 <= spline_degree <= 5.

• derivative_order: int [defaults to 0]

The order of the derivative to apply to the data. Note that this may be negative, in which case the corresponding antiderivative is returned.

• definite_integral_bounds: ) array_like [defaults to None], (2, None

If this is not None, the t_out and derivative_order parameters are ignored, and the returned values are just the (first) definite integrals of the splines between these limits, along each remaining axis.

spline_indefinite_integral

spline_indefinite_integral(f, t, integral_order=1, axis=0)


Source: quaternion/calculus.py