WrappedScipyODESolution

class probnum.diffeq.perturbed.scipy_wrapper.WrappedScipyODESolution(scipy_solution, rvs)

Bases: probnum.diffeq.ODESolution

ODE solution corresponding to the WrappedScipyRungeKutta.

Attributes Summary

frozen

locations

states

Methods Summary

__call__(t)

Evaluate the time-continuous solution at time t.

append(location, state)

rtype

None

freeze()

rtype

None

interpolate(t[, previous_index, next_index])

Evaluate the posterior at a measurement-free point.

sample(rng[, t, size])

Sample from the ODE solution.

transform_base_measure_realizations(…)

Transform a set of realizations from a base measure into realizations from the posterior.

Attributes Documentation

frozen
locations
states

Methods Documentation

__call__(t)[source]

Evaluate the time-continuous solution at time t.

Parameters

  • t (Union[Real, ndarray]) –

  • solution. (Location / time at which to evaluate the continuous ODE) –

Returns

Estimate of the states at time t based on a fourth order polynomial.

Return type

randvars.RandomVariable or _randomvariablelist._RandomVariableList

append(location, state)
Return type

None

freeze()
Return type

None

interpolate(t, previous_index=None, next_index=None)

Evaluate the posterior at a measurement-free point.

Returns

Dense evaluation.

Return type

randvars.RandomVariable or _randomvariablelist._RandomVariableList

sample(rng, t=None, size=())

Sample from the ODE solution.

Parameters

  • rng (Generator) – Random number generator.

  • t (Union[Real, ndarray, None]) – Location / time at which to sample. If nothing is specified, samples at the ODE-solver grid points are computed. If it is a float, a sample of the ODE-solution at this time point is computed. Similarly, if it is a list of floats (or an array), samples at the specified grid-points are returned. This is not the same as computing i.i.d samples at the respective locations.

  • size (Union[Integral, Iterable[Integral], None]) – Number of samples.

Return type

ndarray

transform_base_measure_realizations(base_measure_realizations, t)

Transform a set of realizations from a base measure into realizations from the posterior.

Parameters

  • base_measure_realizations (ndarray) – Base measure realizations.

  • t (Union[Real, ndarray]) – Locations on which the transformed realizations shall represent realizations from the posterior.

Returns

Transformed realizations.

Return type

np.ndarray