OdeSolver(fun, t0, y0, t_bound, vectorized, support_complex=False)¶
Base class for ODE solvers.
In order to implement a new solver you need to follow the guidelines:
- A constructor must accept parameters presented in the base class (listed below) along with any other parameters specific to a solver.
- 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.
- A solver must implement a private method _step_impl(self) which
propagates a solver one step further. It must return tuple
(success, message), where
successis a boolean indicating whether a step was successful, and
messageis a string containing description of a failure if a step failed or None otherwise.
- A solver must implement a private method _dense_output_impl(self)
which returns a
DenseOutputobject covering the last successful step.
- A solver must have attributes listed below in Attributes section.
Note that t_old and
step_sizeare updated automatically.
- Use fun(self, t, y) method for the system rhs evaluation, this way the number of function evaluations (nfev) will be tracked automatically.
- 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.
- 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).
- 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.
- fun : callable
Right-hand side of the system. The calling signature is
fun(t, y). Here
tis a scalar and there are two options for ndarray
y. It can either have shape (n,), then
funmust return array_like with shape (n,). Or alternatively it can have shape (n, n_points), then
funmust 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
- y0 : array_like, shape (n,)
- 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.
- n : int
Number of equations.
- status : string
Current status of the solver: ‘running’, ‘finished’ or ‘failed’.
- t_bound : float
- direction : float
Integration direction: +1 or -1.
- t : float
- y : ndarray
- 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.
Compute a local interpolant over the last successful step.
Perform one integration step.