Contents
Using PyClaw’s solvers: Classic and SharpClaw¶
At present, PyClaw includes two types of solvers:
 Classic: the original Clawpack algorithms, in 1/2/3D
 SharpClaw: higherorder wave propagation using WENO reconstruction and RungeKutta integration, in 1/2D
Solver initialization takes one argument: a Riemann solver, usually from the Riemann repository. Typically, all that is needed to select a different solver is to specify it in the problem script, e.g.
>>> from clawpack import pyclaw
>>> from clawpack import riemann
>>> solver = pyclaw.ClawSolver2D(riemann.acoustics_2D)
for the 2D acoustics equations and the Classic Clawpack solver or
>>> solver = pyclaw.SharpClawSolver2D(riemann.acoustics_2D)
for the SharpClaw solver. Most of the applications distributed with PyClaw are set up to use either solver, depending on the value of the command line option solver_type, which should be set to classic or sharpclaw.
Typically, for a given grid resolution, the SharpClaw solvers are more accurate but also more computationally expensive. For typical problems involving shocks, the Classic solvers are recommended. For problems involving highfrequency waves, turbulence, or smooth solutions, the SharpClaw solvers may give more accurate solutions at less cost. This is an active area of research and you may wish to experiment with both solvers.
Future plans include incorporation of finitedifference and discontinuous Galerkin solvers.
Key differences between the Classic and SharpClaw solvers are:
 The source term routine for the Classic solver should return the integral of the source term over a step, while the source term routine for SharpClaw should return the instantaneous value of the source term.
 The solvers have different options. For a list of options and possible values, see the documentation of the
ClawSolver
andSharpClawSolver
classes.
SharpClaw Solvers¶
The SharpClaw solvers are a collection of solvers that contain the functionality of the Fortran code SharpClaw, developed in David Ketcheson’s thesis. The 1D SharpClaw solver contains a pure Python implementation as well as a wrapped Fortran version. The 2D solver is in progress but not available yet. The SharpClaw solvers provide an interface similar to that of the classic Clawpack solvers, but with a few different options. The superclass solvers are not meant to be used separately but are there to provide common routines for all the Clawpack solvers. Please refer to each of the inherited classes for more info about the methods and attributes they provide each class. .. The inheritance structure is:
Example:  This is a simple example of how to instantiate and evolve a solution to a later time \(\text{t_end}\) using the 1D acoustics Riemann solver. 

>>> from clawpack import pyclaw
>>> solver = pyclaw.SharpClawSolver1D() # Instantiate a default, 1d solver
>>> solver.evolve_to_time(solution,t_end) # Evolve the solution to t_end
pyclaw.sharpclaw
¶

class
pyclaw.sharpclaw.solver.
SharpClawSolver
(riemann_solver=None, claw_package=None)¶ Superclass for all SharpClawND solvers.
Implements RungeKutta time stepping and the basic form of a semidiscrete step (the dq() function). If another methodoflines solver is implemented in the future, it should be based on this class, which then ought to be renamed to something like “MOLSolver”.

lim_type
¶ Limiter(s) to be used. 0: No limiting. 1: TVD reconstruction. 2: WENO reconstruction.
Default = 2

weno_order
¶ Order of the WENO reconstruction. From 1st to 17th order (PyWENO)
Default = 5

time_integrator
¶ Time integrator to be used. Currently implemented methods:
‘Euler’ : 1storder Forward Euler integration ‘SSP33’ : 3rdorder strong stability preserving method of Shu & Osher ‘SSP104’ : 4thorder strong stability preserving method Ketcheson ‘SSPLMM32’: 2ndorder strong stability preserving 3step linear multistep method,
using Euler for starting values ‘SSPLMM43’: 3rdorder strong stability preserving 4step linear multistep method
 using SSPRK22 for starting values
 ‘RK’ : Arbitrary RungeKutta method, specified by setting solver.a
 and solver.b to the Butcher arrays of the method.
 ‘LMM’ : Arbitrary linear multistep method, specified by setting the
 coefficient arrays solver.alpha and solver.beta.
Default = 'SSP104'

char_decomp
¶ Type of WENO reconstruction. 0: conservative variables WENO reconstruction (standard). 1: Waveslope reconstruction. 2: characteristicwise WENO reconstruction. 3: transmissionbased WENO reconstruction.
Default = 0

tfluct_solver
¶ Whether a total fluctuation solver have to be used. If True the function that calculates the total fluctuation must be provided.
Default = False

tfluct
¶ Pointer to Fortran routine to calculate total fluctuation
Default = default_tfluct (None)

aux_time_dep
¶ Whether the auxiliary array is time dependent.
Default = False

kernel_language
¶ Specifies whether to use wrapped Fortran routines (‘Fortran’) or pure Python (‘Python’).
Default = 'Fortran'
.

num_ghost
¶ Number of ghost cells.
Default = 3

fwave
¶ Whether to split the flux jump (rather than the jump in Q) into waves; requires that the Riemann solver performs the splitting.
Default = False

cfl_desired
¶ Desired CFL number.
Default = 2.45

cfl_max
¶ Maximum CFL number.
Default = 2.50

dq_src
¶ Whether a source term is present. If it is present the function that computes its contribution must be provided.
Default = None

call_before_step_each_stage
¶ Whether to call the method self.before_step before each RK stage.
Default = False

accept_reject_step
(state)¶ Decide whether to accept or not the current step. For RungeKutta methods the step is accepted if cfl <= cfl_max. For SSPLMM32 the choice of stepsize guarantees the cfl condition is satisfied for the steps the LMM is used. Hence, we need to check the cfl condition only for the starting steps.

check_3rd_ord_cond
(state, step_index, dtFE)¶ This routine checks the additional conditions for the 3rdorder SSPLMMs. This is a posteriori check after a step is accepted. In particular, there is a condition on the step size for the starting values and a condition on the ratio of forward Euler step sizes at very step. If the conditions are violated we muct retrieve the previous solution and discard that step; otherwise the step is accepted.

dq
(state)¶ Evaluate dq/dt * (delta t)

dqdt
(state)¶ Evaluate dq/dt. This routine is used for implicit time stepping.

get_dt_new
()¶ Set size of next step depending on the time integrator and whether or not the current step was accepted.

setup
(solution)¶ Allocate RK stage arrays or previous step solutions and fortran routine work arrays.

step
(solution, take_one_step, tstart, tend)¶ Evolve q over one time step.
Take one step with a RungeKutta or multistep method as specified by solver.time_integrator.

update_saved_values
(state, step_index)¶ Updates lists of saved function evaluations, solution values, dt and dtFE for LMMs. For 3rdorder SSPLMM additional conditions are checked if self.check_lmm_cond is set to True. If these conditions are violated, the step is rejected.

Pyclaw Classic Clawpack Solvers¶
The pyclaw classic clawpack solvers are a collection of solvers that represent the functionality of the older versions of clawpack. It comes in two forms, a pure python version and a python wrapping of the fortran libraries. All of the solvers available provide the same basic interface and provide the same options as the old versions of clawpack. The superclass solvers are not meant to be used separately but there to provide common routines for all the Clawpack solvers. Please refer to each of the inherited classes for more info about the methods and attributes they provide each class. .. The inheritance structure is:
Example:  This is a simple example of how to instantiate and evolve a solution to a later time \(\text{t_end}\) using the linearized 1d acoustics Riemann solver 

>>> from clawpack import pyclaw
>>> solver = pyclaw.ClawSolver1D() # Instantiate a default, 1d solver
>>> solver.limiters = pyclaw.limiters.tvd.vanleer # Use the van Leer limiter
>>> solver.dt = 0.0001 # Set the initial time step
>>> solver.max_steps = 500 # Set the maximum number of time steps
>>> solver.evolve_to_time(solution,t_end) # Evolve the solution to t_end
pyclaw.classic.solver
¶

class
clawpack.pyclaw.classic.solver.
ClawSolver
(riemann_solver=None, claw_package=None)¶ Generic classic Clawpack solver
All Clawpack solvers inherit from this base class.

mthlim
¶ Limiter(s) to be used. Specified either as one value or a list. If one value, the specified limiter is used for all wave families. If a list, the specified values indicate which limiter to apply to each wave family. Take a look at pyclaw.limiters.tvd for an enumeration.
Default = limiters.tvd.minmod

order
¶ Order of the solver, either 1 for first order (i.e., Godunov’s method) or 2 for second order (LaxWendroffLeVeque).
Default = 2

source_split
¶ Which source splitting method to use: 1 for first order Godunov splitting and 2 for second order Strang splitting.
Default = 1

fwave
¶ Whether to split the flux jump (rather than the jump in Q) into waves; requires that the Riemann solver performs the splitting.
Default = False

step_source
¶ Handle for function that evaluates the source term. The required signature for this function is:
def step_source(solver,state,dt)

kernel_language
¶ Specifies whether to use wrapped Fortran routines (‘Fortran’) or pure Python (‘Python’).
Default = 'Fortran'
.

verbosity
¶ The level of detail of logged messages from the Fortran solver.
Default = 0
.

setup
(solution)¶ Perform essential solver setup. This routine must be called before solver.step() may be called.

step
(solution, take_one_step, tstart, tend)¶ Evolve solution one time step
The elements of the algorithm for taking one step are:
 Pick a step size as specified by the base solver attribute
get_dt()
 A half step on the source term
step_source()
if Strang splitting is being used (source_split
= 2)  A step on the homogeneous problem \(q_t + f(q)_x = 0\) is taken
 A second half step or a full step is taken on the source term
step_source()
depending on whether Strang splitting was used (source_split
= 2) or Godunov splitting (source_split
= 1)
This routine is called from the method evolve_to_time defined in the pyclaw.solver.Solver superclass.
Input:  solution  (
Solution
) solution to be evolved
Output:  (bool)  True if full step succeeded, False otherwise
 Pick a step size as specified by the base solver attribute

step_hyperbolic
(solution)¶ Take one homogeneous step on the solution.
This is a dummy routine and must be overridden.

Change to Custom BC Function Signatures¶
To allow better access to aux array data in the boundary condition functions both the qbc and auxbc arrays are now passed to the custom boundary condition functions. The new signature is
 def my_custom_BC(state, dim, t, qbc, auxbc, num_ghost):
 ...
and should be adopted as soon as possible. The old signature
 def my_custom_BC(state, dim, t, bc_array, num_ghost):
 ...
can still be used but a warning will be issued and the old signature will not be supported when version 6.0 is released. This addition is available in versions > 5.2.0.