pylupnt.Dynamics

class pylupnt.Dynamics

Base interface for state-propagation models (orbit, attitude, clock, …).

propagate(*args, **kwargs)

Overloaded function.

  1. propagate(self: pylupnt._pylupnt.Dynamics, x0: typing.Annotated[numpy.typing.ArrayLike, Real, “[m, 1]”], t0: Real, tf: Real, u: typing.Annotated[numpy.typing.ArrayLike, Real, “[m, 1]”] = None) -> typing.Annotated[numpy.typing.NDArray[Real], “[m, 1]”]

Propagate state x0 from epoch t0 to tf [s]; u is an optional control input.

  1. propagate(self: pylupnt._pylupnt.Dynamics, x0: typing.Annotated[numpy.typing.ArrayLike, Real, “[m, 1]”], t0: Real, tf: Real, u: typing.Annotated[numpy.typing.ArrayLike, Real, “[m, 1]”], stm: bool) -> object

Propagate x0 from t0 to tf [s]; if stm=True, also return the state transition matrix.

  1. propagate(self: pylupnt._pylupnt.Dynamics, x0: typing.Annotated[numpy.typing.ArrayLike, Real, “[m, 1]”], tfs: typing.Annotated[numpy.typing.ArrayLike, Real, “[m, 1]”]) -> typing.Annotated[numpy.typing.NDArray[Real], “[m, n]”]

Propagate x0 to each output epoch in tfs [s], returning states as rows.

propagate_stm(self: pylupnt._pylupnt.Dynamics, x0: Annotated[numpy.typing.ArrayLike, numpy.float64, '[m, 1]'], t0: SupportsFloat | SupportsIndex, tf: SupportsFloat | SupportsIndex) → tuple

Propagate a numpy state vector [r; v] from t0 to tf, returning (state_tf, STM) as numpy arrays – convenient for a filter’s predict step authored in Python.

set_print_progress(self: pylupnt._pylupnt.Dynamics, arg0: bool) → None

Enable or disable a progress bar during multi-step propagation.