Struct OdeWrapper 

pub struct OdeWrapper(/* private fields */);

Implementations

impl OdeWrapper

pub fn get_matrix_type(&self) -> Result<MatrixType, DiffsolRtError>

Matrix type used in the ODE solver. This is fixed after construction.

pub fn get_nstates(&self) -> Result<usize, DiffsolRtError>

pub fn get_nparams(&self) -> Result<usize, DiffsolRtError>

pub fn get_nout(&self) -> Result<usize, DiffsolRtError>

pub fn has_stop(&self) -> Result<bool, DiffsolRtError>

pub fn get_ode_solver(&self) -> Result<OdeSolverType, DiffsolRtError>

Ode solver method, default Bdf (backward differentiation formula).

pub fn set_ode_solver(&self, value: OdeSolverType) -> Result<(), DiffsolRtError>

pub fn get_linear_solver(&self) -> Result<LinearSolverType, DiffsolRtError>

Linear solver type used in the ODE solver. Set to default to use the solver’s default choice, which is typically an LU solver.

pub fn set_linear_solver( &self, value: LinearSolverType, ) -> Result<(), DiffsolRtError>

pub fn get_rtol(&self) -> Result<f64, DiffsolRtError>

Relative tolerance for the solver, default 1e-6. Governs the error relative to the solution size.

pub fn set_rtol(&self, value: f64) -> Result<(), DiffsolRtError>

pub fn get_atol(&self) -> Result<f64, DiffsolRtError>

Absolute tolerance for the solver, default 1e-6. Governs the error as the solution goes to zero.

pub fn set_atol(&self, value: f64) -> Result<(), DiffsolRtError>

pub fn get_code(&self) -> Result<String, DiffsolRtError>

pub fn get_scalar_type(&self) -> Result<ScalarType, DiffsolRtError>

pub fn get_jit_backend(&self) -> Result<Option<JitBackendType>, DiffsolRtError>

pub fn get_ic_options(&self) -> InitialConditionSolverOptions

pub fn get_options(&self) -> OdeSolverOptions

pub fn y0(&self, params: HostArray) -> Result<HostArray, DiffsolRtError>

Get the initial condition vector y0 as a 1D numpy array.

pub fn rhs( &self, params: HostArray, t: f64, y: HostArray, ) -> Result<HostArray, DiffsolRtError>

evaluate the right-hand side function at time t and state y.

pub fn rhs_jac_mul( &self, params: HostArray, t: f64, y: HostArray, v: HostArray, ) -> Result<HostArray, DiffsolRtError>

evaluate the right-hand side Jacobian-vector product Jv`` at time tand statey`.

pub fn solve( &self, params: HostArray, final_time: f64, ) -> Result<SolutionWrapper, DiffsolRtError>

Using the provided state, solve the problem up to time final_time.

The number of params must match the expected params in the diffsl code. If specified, the config can be used to override the solver method (Bdf by default) and SolverType (Lu by default) along with other solver params like rtol.

:param params: 1D array of solver parameters :type params: numpy.ndarray :param final_time: end time of solver :type final_time: float :return: (ys, ts) tuple where ys is a 2D array of values at times ts chosen by the solver :rtype: Tuple[numpy.ndarray, numpy.ndarray]

Example: >>> print(ode.solve(np.array([]), 0.5))

pub fn solve_hybrid( &self, params: HostArray, final_time: f64, ) -> Result<SolutionWrapper, DiffsolRtError>

Solve a hybrid ODE up to final_time, automatically applying reset functions and continuing after root events until the solution completes.

pub fn solve_dense( &self, params: HostArray, t_eval: HostArray, ) -> Result<SolutionWrapper, DiffsolRtError>

Using the provided state, solve the problem up to time t_eval[t_eval.len()-1]. Returns 2D array of solution values at timepoints given by t_eval.

The number of params must match the expected params in the diffsl code. The config may be optionally specified to override solver settings.

:param params: 1D array of solver parameters :type params: numpy.ndarray :param t_eval: 1D array of solver times :type params: numpy.ndarray :return: 2D array of values at times t_eval :rtype: numpy.ndarray

pub fn solve_hybrid_dense( &self, params: HostArray, t_eval: HostArray, ) -> Result<SolutionWrapper, DiffsolRtError>

Solve a hybrid ODE at dense evaluation times, automatically applying reset functions and continuing after root events until all requested output points are filled.

pub fn solve_fwd_sens( &self, params: HostArray, t_eval: HostArray, ) -> Result<SolutionWrapper, DiffsolRtError>

Using the provided state, solve the problem up to time t_eval[t_eval.len()-1]. Returns 2D array of solution values at timepoints given by t_eval. Also returns a list of 2D arrays of sensitivities at the same timepoints as the solution. The number of params must match the expected params in the diffsl code. The config may be optionally specified to override solver settings. :param params: 1D array of solver parameters :type params: numpy.ndarray :param t_eval: 1D array of solver times :type params: numpy.ndarray :return: 2D array of values at times t_eval and a list of 2D arrays of sensitivities at the same timepoints :rtype: (numpy.ndarray, List[numpy.ndarray])

pub fn solve_hybrid_fwd_sens( &self, params: HostArray, t_eval: HostArray, ) -> Result<SolutionWrapper, DiffsolRtError>

Solve a hybrid ODE with forward sensitivities at dense evaluation times, automatically applying sensitivity-aware reset functions and continuing after root events until all requested output points are filled.

pub fn solve_sum_squares_adj( &self, params: HostArray, data: HostArray, t_eval: HostArray, ) -> Result<(f64, HostArray), DiffsolRtError>

Using the provided state, solve the adjoint problem for the sum of squares objective given data at timepoints t_eval. Returns the objective value and a list of 1D arrays of adjoint sensitivities for each parameter.

Trait Implementations

impl Clone for OdeWrapper

fn clone(&self) -> OdeWrapper

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.