Module « scipy.integrate »

Classe « OdeSolver »

Informations générales

Héritage

builtins.object
    OdeSolver

Définition

class OdeSolver(builtins.object):

Description _{[extrait de OdeSolver.__doc__]}

Base class for ODE solvers.

    In order to implement a new solver you need to follow the guidelines:

        1. A constructor must accept parameters presented in the base class
           (listed below) along with any other parameters specific to a solver.
        2. A constructor must accept arbitrary extraneous arguments
           ``**extraneous``, but warn that these arguments are irrelevant
           using `common.warn_extraneous` function. Do not pass these
           arguments to the base class.
        3. A solver must implement a private method `_step_impl(self)` which
           propagates a solver one step further. It must return tuple
           ``(success, message)``, where ``success`` is a boolean indicating
           whether a step was successful, and ``message`` is a string
           containing description of a failure if a step failed or None
           otherwise.
        4. A solver must implement a private method `_dense_output_impl(self)`,
           which returns a `DenseOutput` object covering the last successful
           step.
        5. A solver must have attributes listed below in Attributes section.
           Note that ``t_old`` and ``step_size`` are updated automatically.
        6. Use `fun(self, t, y)` method for the system rhs evaluation, this
           way the number of function evaluations (`nfev`) will be tracked
           automatically.
        7. For convenience, a base class provides `fun_single(self, t, y)` and
           `fun_vectorized(self, t, y)` for evaluating the rhs in
           non-vectorized and vectorized fashions respectively (regardless of
           how `fun` from the constructor is implemented). These calls don't
           increment `nfev`.
        8. If a solver uses a Jacobian matrix and LU decompositions, it should
           track the number of Jacobian evaluations (`njev`) and the number of
           LU decompositions (`nlu`).
        9. By convention, the function evaluations used to compute a finite
           difference approximation of the Jacobian should not be counted in
           `nfev`, thus use `fun_single(self, t, y)` or
           `fun_vectorized(self, t, y)` when computing a finite difference
           approximation of the Jacobian.

    Parameters
    ----------
    fun : callable
        Right-hand side of the system. The calling signature is ``fun(t, y)``.
        Here ``t`` is a scalar and there are two options for ndarray ``y``.
        It can either have shape (n,), then ``fun`` must return array_like with
        shape (n,). Or, alternatively, it can have shape (n, n_points), then
        ``fun`` must return array_like with shape (n, n_points) (each column
        corresponds to a single column in ``y``). The choice between the two
        options is determined by `vectorized` argument (see below).
    t0 : float
        Initial time.
    y0 : array_like, shape (n,)
        Initial state.
    t_bound : float
        Boundary time --- the integration won't continue beyond it. It also
        determines the direction of the integration.
    vectorized : bool
        Whether `fun` is implemented in a vectorized fashion.
    support_complex : bool, optional
        Whether integration in a complex domain should be supported.
        Generally determined by a derived solver class capabilities.
        Default is False.

    Attributes
    ----------
    n : int
        Number of equations.
    status : string
        Current status of the solver: 'running', 'finished' or 'failed'.
    t_bound : float
        Boundary time.
    direction : float
        Integration direction: +1 or -1.
    t : float
        Current time.
    y : ndarray
        Current state.
    t_old : float
        Previous time. None if no steps were made yet.
    step_size : float
        Size of the last successful step. None if no steps were made yet.
    nfev : int
        Number of the system's rhs evaluations.
    njev : int
        Number of the Jacobian evaluations.
    nlu : int
        Number of LU decompositions.
    

Constructeur(s)

Signature du constructeur	Description
__init__(self, fun, t0, y0, t_bound, vectorized, support_complex=False)

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Méthodes héritées de la classe object

__delattr__, __dir__, __format__, __getattribute__, __hash__, __init_subclass__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__