Reference

Runge-Kutta Methods

leap.rk.ORDER_TO_RK_METHOD

A dictionary mapping desired order of accuracy to a corresponding RK method.

class leap.rk.ForwardEulerMethod(component_id, state_filter_name=None, rhs_func_name=None)
__init__(component_id, state_filter_name=None, rhs_func_name=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode
class leap.rk.BackwardEulerMethod(component_id, state_filter_name=None, rhs_func_name=None)
__init__(component_id, state_filter_name=None, rhs_func_name=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode
class leap.rk.MidpointMethod(component_id, state_filter_name=None, rhs_func_name=None)
__init__(component_id, state_filter_name=None, rhs_func_name=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode
class leap.rk.HeunsMethod(component_id, state_filter_name=None, rhs_func_name=None)
__init__(component_id, state_filter_name=None, rhs_func_name=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode
class leap.rk.RK3Method(component_id, state_filter_name=None, rhs_func_name=None)

Source: J. C. Butcher, Numerical Methods for Ordinary Differential Equations, 2nd ed., pages 94 - 99

__init__(component_id, state_filter_name=None, rhs_func_name=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode
class leap.rk.RK4Method(component_id, state_filter_name=None, rhs_func_name=None)
__init__(component_id, state_filter_name=None, rhs_func_name=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode
class leap.rk.RK5Method(component_id, state_filter_name=None, rhs_func_name=None)

Source: J. C. Butcher, Numerical Methods for Ordinary Differential Equations, 2nd ed., pages 94 - 99

Low-Storage Methods

class leap.rk.LSRK4Method(component_id, state_filter_name=None, rhs_func_name=None)

A low storage fourth-order Runge-Kutta method

See JSH, TW: Nodal Discontinuous Galerkin Methods p.64 or Carpenter, M.H., and Kennedy, C.A., Fourth-order-2N-storage Runge-Kutta schemes, NASA Langley Tech Report TM 109112, 1994

__init__(component_id, state_filter_name=None, rhs_func_name=None)
Parameters:component_id – an identifier to be used for the single state component supported.
generate()
Returns:dagrt.language.DAGCode

Adaptive/Embedded Methods

class leap.rk.ODE23Method(component_id, use_high_order=True, state_filter_name=None, atol=0, rtol=0, max_dt_growth=None, min_dt_shrinkage=None)

Bogacki-Shampine second/third-order Runge-Kutta.

(same as Matlab’s ode23)

Bogacki, Przemyslaw; Shampine, Lawrence F. (1989), “A 3(2) pair of Runge-Kutta formulas”, Applied Mathematics Letters 2 (4): 321-325, http://dx.doi.org/10.1016/0893-9659(89)90079-7

__init__(component_id, use_high_order=True, state_filter_name=None, atol=0, rtol=0, max_dt_growth=None, min_dt_shrinkage=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode
class leap.rk.ODE45Method(component_id, use_high_order=True, state_filter_name=None, atol=0, rtol=0, max_dt_growth=None, min_dt_shrinkage=None)

Dormand-Prince fourth/fifth-order Runge-Kutta.

(same as Matlab’s ode45)

Dormand, J. R.; Prince, P. J. (1980), “A family of embedded Runge-Kutta formulae”, Journal of Computational and Applied Mathematics 6 (1): 19-26, http://dx.doi.org/10.1016/0771-050X(80)90013-3.

__init__(component_id, use_high_order=True, state_filter_name=None, atol=0, rtol=0, max_dt_growth=None, min_dt_shrinkage=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode

IMEX Methods

class leap.rk.imex.KennedyCarpenterIMEXRungeKuttaMethodBase(component_id, use_high_order=True, state_filter_name=None, use_explicit=True, use_implicit=True, atol=0, rtol=0, max_dt_growth=None, min_dt_shrinkage=None, implicit_rhs_name=None, explicit_rhs_name=None)

Christopher A. Kennedy, Mark H. Carpenter. Additive Runge-Kutta schemes for convection-diffusion-reaction equations. Applied Numerical Mathematics Volume 44, Issues 1-2, January 2003, Pages 139-181 http://dx.doi.org/10.1016/S0168-9274(02)00138-1

User-supplied context:
<state> + component_id: The value that is integrated <func>rhs_expl_ + component_id: The explicit right hand side function <func>rhs_impl_ + component_id: The implicit right hand side function
__init__(component_id, use_high_order=True, state_filter_name=None, use_explicit=True, use_implicit=True, atol=0, rtol=0, max_dt_growth=None, min_dt_shrinkage=None, implicit_rhs_name=None, explicit_rhs_name=None)

Initialize self. See help(type(self)) for accurate signature.

generate()
Returns:dagrt.language.DAGCode

Multi-Step Methods

class leap.multistep.AdamsBashforthMethod(component_id, function_family=None, state_filter_name=None, hist_length=None, static_dt=False, order=None)
User-supplied context:
<state> + component_id: The value that is integrated <func> + component_id: The right hand side
__init__(component_id, function_family=None, state_filter_name=None, hist_length=None, static_dt=False, order=None)
Parameters:
  • function_family – Accepts an instance of ABIntegrationFunctionFamily or an integer, in which case the classical monomial function family with the order given by the integer is used.
  • static_dt – If True, changing the timestep during time integration is not allowed.
generate()
Returns:dagrt.language.DAGCode

Multi-Rate Multi-Step Methods

class leap.multistep.multirate.rhs_policy
late
early
early_and_late
class leap.multistep.multirate.MultiRateHistory(interval, func_name, arguments, order=None, rhs_policy=0, invalidate_computed_state=False, hist_length=None)
__init__(interval, func_name, arguments, order=None, rhs_policy=0, invalidate_computed_state=False, hist_length=None)
Parameters:
  • interval – An integer indicating the interval (relative to the smallest available timestep) at which this history is to be updated. (where each update will typically involve a call to func_name)
  • arguments – A tuple of component names (see MultiRateMultiStepMethod) which are passed to this right-hand side function.
  • order – The AB approximation order to be used for this history, or None if the method default is to be used.
  • rhs_policy – One of the constants in rhs_policy
  • invalidate_dependent_state – Whether evaluating this right-hand side should force a recomputation of any state that depended upon now-superseded state.
  • hist_length – history length. If greater than order, we use a least-squares solve rather than a linear solve to obtain the AB coefficients for this history
class leap.multistep.multirate.MultiRateMultiStepMethod(default_order, system_description, state_filter_names=None, component_arg_names=None, static_dt=False, hist_consistency_threshold=None, early_hist_consistency_threshold=None)

Simultaneously timesteps multiple parts of an ODE system, each with adjustable orders, rates, and dependencies.

Considerably generalizes [GearWells].

[GearWells]C.W. Gear and D.R. Wells, “Multirate linear multistep methods,” BIT Numerical Mathematics, vol. 24, Dec. 1984,pg. 484-502.
__init__(default_order, system_description, state_filter_names=None, component_arg_names=None, static_dt=False, hist_consistency_threshold=None, early_hist_consistency_threshold=None)
Parameters:
  • default_order – The order to be used for right-hand sides where no differing order is specified.
  • system_description –

    A tuple of the form:

    (
    ('dt', 'fast',
    '=', MultiRateHistory(1, '<func>f1', ('fast', 'slow', 'dep')),
    MultiRateHistory(3, '<func>f2', ('slow', 'dep')),
    ),
    ('dt', slow',
    '=', MultiRateHistory(3, '<func>f3', ('fast', 'slow', 'dep')),
    ),
    ('dep',
    '=', MultiRateHistory(3, '<func>f4', ('slow')),
    ),
)

    i.e. the outermost tuple represents a list of state components. These can either be ODEs (in which case the first string in the tuple is 'dt', followed by the component name, or computed/dependent state, in which case the first string in the tuple is just the name of the computed piece of state.

    The ‘right-hand-side’ of each tuple (after the intervening '=' string) consists of one or more instances of MultiRateHistory describing the rate at which evaluations of the given functions should be stored (along with other parameters that can be configured in MultiRateHistory). The right-hand sides are combined additively.

    Computed state components may not (directly) or indirectly depend on themselves. The result will be undefined.

  • state_filter_names – a dictionary mapping (non-ODE) component names from the system description to the names of “state filter” functions (as in functions that receive this particular component state and return a potentially modified version thereof).
  • component_arg_names – A tuple of names of the components to be used as keywords for passing arguments to the right hand sides in rhss.
  • static_dt – If True, changing the timestep in between steps during time integration is not allowed.
generate(explainer=None)
Parameters:explainer – a subclass of SchemeExplainerBase, possibly TextualSchemeExplainer, or None.
Returns:dagrt.language.DAGCode
class leap.multistep.multirate.SchemeExplainerBase
class leap.multistep.multirate.TextualSchemeExplainer
__init__()

Initialize self. See help(type(self)) for accurate signature.

__str__()

Return str(self).

Analysis

class leap.step_matrix.StepMatrixFinder(code, function_map, variables=None, exclude_variables=None)

Constructs a step matrix on-the-fly while interpreting code.

Assumes that all function evaluations occur as the root node of a separate assignment statement.

leap.step_matrix.fast_evaluator(matrix, sparse=False)

Generate a function to evaluate a step matrix quickly. The input comes from StepMatrixFinder.

leap.stability.find_stability_region(code, parallel=None, n_angles=100, prec=0.01, origin=-0.3)

Transformation

leap.transform.strang_splitting(dag1, dag2, stepping_phase)

Given two time advancement routines (in dag1 and dag2), returns a single second-order accurate time advancement routine representing the sum of both of those advancements.

Parameters:
Returns:

a dagrt.language.DAGCode