WrappedScipyRungeKutta

class probnum.diffeq.perturbed.scipy_wrapper.WrappedScipyRungeKutta(solver_type, steprule)

Bases: ODESolver

Wrapper for Runge-Kutta methods from SciPy.

Methods Summary

attempt_step(state, dt)	Perform one ODE-step from start to stop and set variables to the corresponding values.
dense_output()	Compute the interpolant after each step.
initialize(ivp)	Return t0 and y0 (for the solver, which might be different to ivp.y0) and initialize the solver.
method_callback(state)	Call dense output after each step and store the interpolants.
perform_full_step(state, initial_dt)	Perform a full ODE solver step.
postprocess(odesol)	Process the ODESolution object before returning.
rvlist_to_odesol(times, rvs)	Create a ScipyODESolution object which is a subclass of diffeq.ODESolution.
solution_generator(ivp[, stop_at, callbacks])	Generate ODE solver steps.
solve(ivp[, stop_at, callbacks])	Solve an IVP.

Methods Documentation

Parameters

solver_type (RungeKutta) –

attempt_step(state, dt)

Perform one ODE-step from start to stop and set variables to the corresponding values.

To specify start and stop directly, rk_step() and not _step_impl() is used.

Parameters

• state (ODESolverState) – Current state of the ODE solver.

• dt (Union[float, Real, floating]) – Step-size.

Returns

New state.

Return type

_odesolver_state.ODESolverState

dense_output()

Compute the interpolant after each step.

Returns

sol – Interpolant between the last and current location.

Return type

rk.RkDenseOutput

initialize(ivp)

Return t0 and y0 (for the solver, which might be different to ivp.y0) and initialize the solver. Reset the solver when solving the ODE multiple times, i.e. explicitly setting y_old, t, y and f to the respective initial values, otherwise those are wrong when running the solver twice.

Returns

• self.ivp.t0 (float) – initial time point

• self.ivp.initrv (randvars.RandomVariable) – initial random variable

method_callback(state)

Call dense output after each step and store the interpolants.

perform_full_step(state, initial_dt)

Perform a full ODE solver step.

This includes the acceptance/rejection decision as governed by error estimation and steprule.

postprocess(odesol)

Process the ODESolution object before returning.

rvlist_to_odesol(times, rvs)

Create a ScipyODESolution object which is a subclass of diffeq.ODESolution.

Parameters

• times (array) –

• rvs (array) –

solution_generator(ivp, stop_at=None, callbacks=None)

Generate ODE solver steps.

Parameters

• ivp (problems.InitialValueProblem) –

• stop_at (Iterable[FloatLike]) –

• callbacks (Optional[CallbackType]) –

solve(ivp, stop_at=None, callbacks=None)

Solve an IVP.

Parameters

• ivp (problems.InitialValueProblem) – Initial value problem.

• stop_at (Iterable[FloatLike]) – Time-points through which the solver must step. Optional. Default is None.

• callbacks (Optional[CallbackType]) – Callbacks to happen after every accepted step.