WrappedScipyRungeKutta¶

class probnum.diffeq.WrappedScipyRungeKutta(solver)[source]¶

Bases: probnum.diffeq.ODESolver

Wrapper for Runge-Kutta methods from Scipy, implements the stepfunction and dense output.

Methods Summary

`dense_output`()	Compute the interpolant after each step.
`initialise`()	Return t0 and y0 (for the solver, which might be different to ivp.y0) and initialize the solver.
`method_callback`(time, current_guess, …)	Call dense output after each step and store the interpolants.
`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`(steprule)	Generate ODE solver steps.
`solve`(steprule)	Solve an IVP.
`step`(start, stop, current, **kwargs)	Perform one ODE-step from start to stop and set variables to the corresponding values.

Methods Documentation

dense_output()[source]¶

Compute the interpolant after each step.

Returns: sol – Interpolant between the last and current location.
Return type: rk.RkDenseOutput

initialise()[source]¶

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(time, current_guess, current_error)[source]¶: Call dense output after each step and store the interpolants.

postprocess(odesol)¶: Process the ODESolution object before returning.

rvlist_to_odesol(times, rvs)[source]¶: Create a ScipyODESolution object which is a subclass of diffeq.ODESolution.

solution_generator(steprule)¶: Generate ODE solver steps.

solve(steprule)¶

Solve an IVP.

Parameters: steprule (StepRule) – Step-size selection rule, e.g. constant steps or adaptive steps.

step(start, stop, current, **kwargs)[source]¶

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

start (float) – starting location of the step
stop (float) – stopping location of the step
current (list of RandomVariable) – current state of the ODE.

Returns

random_var (randvars.RandomVariable) – Estimated states of the discrete-time solution.
error_estimation (float) – estimated error after having performed the step.