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solver.py
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solver.py
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r"""
Module containing the classic Clawpack solvers.
This module contains the pure and wrapped classic clawpack solvers. All
clawpack solvers inherit from the :class:`ClawSolver` superclass which in turn
inherits from the :class:`~pyclaw.solver.Solver` superclass. These
are both pure virtual classes; the only solver classes that should be instantiated
are the dimension-specific ones, :class:`ClawSolver1D` and :class:`ClawSolver2D`.
"""
from __future__ import absolute_import
from clawpack.pyclaw.util import add_parent_doc
from clawpack.pyclaw.solver import Solver
from clawpack.pyclaw.limiters import tvd
from six.moves import range
from imp import reload
import matplotlib.pyplot as plt
# ============================================================================
# Generic Clawpack solver class
# ============================================================================
class ClawSolver(Solver):
r"""
Generic classic Clawpack solver
All Clawpack solvers inherit from this base class.
.. attribute:: 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``
.. attribute:: order
Order of the solver, either 1 for first order (i.e., Godunov's method)
or 2 for second order (Lax-Wendroff-LeVeque).
``Default = 2``
.. attribute:: source_split
Which source splitting method to use: 1 for first
order Godunov splitting and 2 for second order Strang splitting.
``Default = 1``
.. attribute:: 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``
.. attribute:: step_source
Handle for function that evaluates the source term.
The required signature for this function is:
def step_source(solver,state,dt)
.. attribute:: kernel_language
Specifies whether to use wrapped Fortran routines ('Fortran')
or pure Python ('Python'). ``Default = 'Fortran'``.
.. attribute:: verbosity
The level of detail of logged messages from the Fortran solver.
``Default = 0``.
"""
# ========== Generic Init Routine ========================================
def __init__(self,riemann_solver=None,claw_package=None):
r"""
See :class:`ClawSolver` for full documentation.
Output:
- (:class:`ClawSolver`) - Initialized clawpack solver
"""
self.num_ghost = 2
self.limiters = tvd.minmod
self.order = 2
self.source_split = 1
self.fwave = True
self.step_source = None
self.kernel_language = 'Fortran'
self.verbosity = 0
self.cfl_max = 0.5
self.cfl_desired = 0.5
self._mthlim = self.limiters
self._method = None
self.dt_old = None
# Call general initialization function
super(ClawSolver,self).__init__(riemann_solver,claw_package)
# ========== Time stepping routines ======================================
def step(self,solution,take_one_step,tstart,tend):
r"""
Evolve solution one time step
The elements of the algorithm for taking one step are:
1. Pick a step size as specified by the base solver attribute :func:`get_dt`
2. A half step on the source term :func:`step_source` if Strang splitting is
being used (:attr:`source_split` = 2)
3. A step on the homogeneous problem :math:`q_t + f(q)_x = 0` is taken
4. A second half step or a full step is taken on the source term
:func:`step_source` depending on whether Strang splitting was used
(:attr:`source_split` = 2) or Godunov splitting
(:attr:`source_split` = 1)
This routine is called from the method evolve_to_time defined in the
pyclaw.solver.Solver superclass.
:Input:
- *solution* - (:class:`~pyclaw.solution.Solution`) solution to be evolved
:Output:
- (bool) - True if full step succeeded, False otherwise
"""
self.get_dt(solution.t,tstart,tend,take_one_step)
self.cfl.set_global_max(0.)
if self.source_split == 2 and self.step_source is not None:
self.step_source(self,solution.states[0],self.dt/2.0)
self.step_hyperbolic(solution)
# Check here if the CFL condition is satisfied.
# If not, return # immediately to evolve_to_time and let it deal with
# picking a new step size (dt).
if self.cfl.get_cached_max() >= self.cfl_max:
return False
if self.step_source is not None:
# Strang splitting
if self.source_split == 2:
self.step_source(self,solution.states[0],self.dt/2.0)
# Godunov Splitting
if self.source_split == 1:
self.step_source(self,solution.states[0],self.dt)
return True
def _check_cfl_settings(self):
pass
def _allocate_workspace(self,solution):
pass
def step_hyperbolic(self,solution):
r"""
Take one homogeneous step on the solution.
This is a dummy routine and must be overridden.
"""
raise Exception("Dummy routine, please override!")
def _set_mthlim(self):
r"""
Convenience routine to convert users limiter specification to
the format understood by the Fortran code (i.e., a list of length num_waves).
"""
self._mthlim = self.limiters
if not isinstance(self.limiters,list): self._mthlim=[self._mthlim]
if len(self._mthlim)==1: self._mthlim = self._mthlim * self.num_waves
if len(self._mthlim)!=self.num_waves:
raise Exception('Length of solver.limiters is not equal to 1 or to solver.num_waves')
def _set_method(self,state):
r"""
Set values of the solver._method array required by the Fortran code.
These are algorithmic parameters.
"""
import numpy as np
#We ought to put method and many other things in a Fortran
#module and set the fortran variables directly here.
self._method =np.empty(7, dtype=int,order='F')
self._method[0] = self.dt_variable
self._method[1] = self.order
if self.num_dim==1:
self._method[2] = 0 # Not used in 1D
elif self.dimensional_split:
self._method[2] = -1 # First-order dimensional splitting
else:
self._method[2] = self.transverse_waves
self._method[3] = self.verbosity
self._method[4] = 0 # Not used for PyClaw (would be self.source_split)
self._method[5] = state.index_capa + 1
self._method[6] = state.num_aux
def setup(self,solution):
r"""
Perform essential solver setup. This routine must be called before
solver.step() may be called.
"""
# This is a hack to deal with the fact that petsc4py
# doesn't allow us to change the stencil_width (num_ghost)
solution.state.set_num_ghost(self.num_ghost)
# End hack
self._check_cfl_settings()
self._set_mthlim()
if(self.kernel_language == 'Fortran'):
if self.fmod is None:
so_name = 'clawpack.pyclaw.classic.classic'+str(self.num_dim)
self.fmod = __import__(so_name,fromlist=['clawpack.pyclaw.classic'])
self._set_fortran_parameters(solution)
self._allocate_workspace(solution)
elif self.num_dim>1:
raise Exception('Only Fortran kernels are supported in multi-D.')
self._allocate_bc_arrays(solution.states[0])
super(ClawSolver,self).setup(solution)
def _set_fortran_parameters(self,solution):
r"""
Pack parameters into format recognized by Clawpack (Fortran) code.
Sets the solver._method array and the cparam common block for the Riemann solver.
"""
self._set_method(solution.state)
# The reload here is necessary because otherwise the common block
# cparam in the Riemann solver doesn't get flushed between running
# different tests in a single Python session.
reload(self.fmod)
solution.state.set_cparam(self.fmod)
solution.state.set_cparam(self.rp)
def __del__(self):
r"""
Delete Fortran objects, which otherwise tend to persist in Python sessions.
"""
if(self.kernel_language == 'Fortran'):
del self.fmod
super(ClawSolver,self).__del__()
# ============================================================================
# ClawPack 1d Solver Class
# ============================================================================
class ClawSolver1D(ClawSolver):
r"""
Clawpack evolution routine in 1D
This class represents the 1d clawpack solver on a single grid. Note that
there are routines here for interfacing with the fortran time stepping
routines and the Python time stepping routines. The ones used are
dependent on the argument given to the initialization of the solver
(defaults to python).
"""
__doc__ += add_parent_doc(ClawSolver)
def __init__(self, riemann_solver=None, claw_package=None):
r"""
Create 1d Clawpack solver
Output:
- (:class:`ClawSolver1D`) - Initialized 1d clawpack solver
See :class:`ClawSolver1D` for more info.
"""
self.num_dim = 1
self.reflect_index = [1]
super(ClawSolver1D,self).__init__(riemann_solver, claw_package)
# ========== Homogeneous Step =====================================
def step_hyperbolic(self,solution):
r"""
Take one time step on the homogeneous hyperbolic system.
:Input:
- *solution* - (:class:`~pyclaw.solution.Solution`) Solution that
will be evolved
"""
import numpy as np
state = solution.states[0]
grid = state.grid
self._apply_bcs(state)
num_eqn,num_ghost = state.num_eqn,self.num_ghost
if(self.kernel_language == 'Fortran'):
mx = grid.num_cells[0]
dx,dt = grid.delta[0],self.dt
dtdx = np.zeros( (mx+2*num_ghost) ) + dt/dx
rp1 = self.rp.rp1._cpointer
self.qbc,cfl = self.fmod.step1(num_ghost,mx,self.qbc,self.auxbc,dx,dt,self._method,self._mthlim,self.fwave,rp1)
elif(self.kernel_language == 'Python'):
q = self.qbc
aux = self.auxbc
# Limiter to use in the pth family
limiter = np.array(self._mthlim,ndmin=1)
dtdx = np.zeros( (2*self.num_ghost+grid.num_cells[0]) )
# Find local value for dt/dx
if state.index_capa>=0:
dtdx = self.dt / (grid.delta[0] * aux[state.index_capa,:])
else:
dtdx += self.dt/grid.delta[0]
# Solve Riemann problem at each interface
q_l=q[:,:-1]
q_r=q[:,1:]
if state.aux is not None:
aux_l=aux[:,:-1]
aux_r=aux[:,1:]
else:
aux_l = None
aux_r = None
wave,s,amdq,apdq = self.rp(q_l,q_r,aux_l,aux_r,state.problem_data)
# Update loop limits, these are the limits for the Riemann solver
# locations, which then update a grid cell value
# We include the Riemann problem just outside of the grid so we can
# do proper limiting at the grid edges
# LL | | UL
# | LL | | | | ... | | | UL | |
# | |
LL = self.num_ghost - 1
UL = self.num_ghost + grid.num_cells[0] + 1
# Update q for Godunov update
for m in range(num_eqn):
q[m,LL:UL] -= dtdx[LL:UL]*apdq[m,LL-1:UL-1]
q[m,LL-1:UL-1] -= dtdx[LL-1:UL-1]*amdq[m,LL-1:UL-1]
# Compute maximum wave speed
cfl = 0.0
for mw in range(wave.shape[1]):
smax1 = np.max(dtdx[LL:UL]*s[mw,LL-1:UL-1])
smax2 = np.max(-dtdx[LL-1:UL-1]*s[mw,LL-1:UL-1])
cfl = max(cfl,smax1,smax2)
# If we are doing slope limiting we have more work to do
if self.order == 2:
# Initialize flux corrections
f = np.zeros( (num_eqn,grid.num_cells[0] + 2*self.num_ghost) )
# Apply Limiters to waves
if (limiter > 0).any():
wave = tvd.limit(state.num_eqn,wave,s,limiter,dtdx)
# Compute correction fluxes for second order q_{xx} terms
dtdxave = 0.5 * (dtdx[LL-1:UL-1] + dtdx[LL:UL])
if self.fwave:
for mw in range(wave.shape[1]):
sabs = np.abs(s[mw,LL-1:UL-1])
om = 1.0 - sabs*dtdxave[:UL-LL]
ssign = np.sign(s[mw,LL-1:UL-1])
for m in range(num_eqn):
f[m,LL:UL] += 0.5 * ssign * om * wave[m,mw,LL-1:UL-1]
else:
for mw in range(wave.shape[1]):
sabs = np.abs(s[mw,LL-1:UL-1])
om = 1.0 - sabs*dtdxave[:UL-LL]
for m in range(num_eqn):
f[m,LL:UL] += 0.5 * sabs * om * wave[m,mw,LL-1:UL-1]
# Update q by differencing correction fluxes
for m in range(num_eqn):
q[m,LL:UL-1] -= dtdx[LL:UL-1] * (f[m,LL+1:UL] - f[m,LL:UL-1])
else: raise Exception("Unrecognized kernel_language; choose 'Fortran' or 'Python'")
self.cfl.update_global_max(cfl)
state.set_q_from_qbc(num_ghost,self.qbc)
if state.num_aux > 0:
state.set_aux_from_auxbc(num_ghost,self.auxbc)
# ============================================================================
# ClawPack 2d Solver Class
# ============================================================================
class ClawSolver2D(ClawSolver):
r"""
2D Classic (Clawpack) solver.
Solve using the wave propagation algorithms of Randy LeVeque's
Clawpack code (www.clawpack.org).
In addition to the attributes of ClawSolver1D, ClawSolver2D
also has the following options:
.. attribute:: dimensional_split
If True, use dimensional splitting (Godunov splitting).
Dimensional splitting with Strang splitting is not supported
at present but could easily be enabled if necessary.
If False, use unsplit Clawpack algorithms, possibly including
transverse Riemann solves.
.. attribute:: transverse_waves
If dimensional_split is True, this option has no effect. If
dimensional_split is False, then transverse_waves should be one of
the following values:
ClawSolver2D.no_trans: Transverse Riemann solver
not used. The stable CFL for this algorithm is 0.5. Not recommended.
ClawSolver2D.trans_inc: Transverse increment waves are computed
and propagated.
ClawSolver2D.trans_cor: Transverse increment waves and transverse
correction waves are computed and propagated.
Note that only the fortran routines are supported for now in 2D.
"""
__doc__ += add_parent_doc(ClawSolver)
no_trans = 0
trans_inc = 1
trans_cor = 2
def __init__(self,riemann_solver=None, claw_package=None):
r"""
Create 2d Clawpack solver
See :class:`ClawSolver2D` for more info.
"""
self.dimensional_split = True
self.transverse_waves = self.trans_inc
self.num_dim = 2
self.reflect_index = [1,2]
self.aux1 = None
self.aux2 = None
self.aux3 = None
self.work = None
super(ClawSolver2D,self).__init__(riemann_solver, claw_package)
def _check_cfl_settings(self):
# if (not self.dimensional_split) and (self.transverse_waves==0):
cfl_recommended = 0.5
# else:
# cfl_recommended = 1.0
if self.cfl_max > cfl_recommended:
import warnings
warnings.warn('cfl_max is set higher than the recommended value of %s' % cfl_recommended)
warnings.warn(str(self.cfl_desired))
def _allocate_workspace(self,solution):
r"""
Pack parameters into format recognized by Clawpack (Fortran) code.
Sets the method array and the cparam common block for the Riemann solver.
"""
import numpy as np
state = solution.state
num_eqn,num_aux,num_waves,num_ghost,aux,auxu = state.num_eqn,state.num_aux,self.num_waves,self.num_ghost,state.aux,state.auxu
#The following is a hack to work around an issue
#with f2py. It involves wastefully allocating three arrays.
#f2py seems not able to handle multiple zero-size arrays being passed.
# it appears the bug is related to f2py/src/fortranobject.c line 841.
if aux is None: num_aux=1
grid = state.grid
maxmx,maxmy = grid.num_cells[0],grid.num_cells[1]
maxm = max(maxmx, maxmy)
# These work arrays really ought to live inside a fortran module
# as is done for sharpclaw
self.aux1 = np.empty((num_aux,maxm+2*num_ghost),order='F')
self.aux2 = np.empty((num_aux,maxm+2*num_ghost),order='F')
self.aux3 = np.empty((num_aux,maxm+2*num_ghost),order='F')
mwork = (maxm+2*num_ghost) * (5*num_eqn + num_waves + num_eqn*num_waves)
self.work = np.empty((mwork),order='F')
# ========== Hyperbolic Step =====================================
def step_hyperbolic(self,solution):
r"""
Take a step on the homogeneous hyperbolic system using the Clawpack
algorithm.
Clawpack is based on the Lax-Wendroff method, combined with Riemann
solvers and TVD limiters applied to waves.
"""
if(self.kernel_language == 'Fortran'):
state = solution.states[0]
grid = state.grid
dx,dy = grid.delta
mx,my = grid.num_cells
maxm = max(mx,my)
bar_ht = state.problem_data['bar_ht']
self._apply_bcs(state)
qold = self.qbc.copy('F')
qold2 = self.qbc2.copy('F')
rpn2 = self.rp.rpn2._cpointer
if (self.dimensional_split) or (self.transverse_waves==0):
rpt2 = rpn2 # dummy value; it won't be called
else:
rpt2 = self.rp.rpt2._cpointer
# print("PROBLEM ROW/COL:",qold2[0,:,-1])
if self.dimensional_split:
#Right now only Godunov-dimensional-splitting is implemented.
#Strang-dimensional-splitting could be added following dimsp2.f in Clawpack.
if state.problem_data['method'] == 'hbox':
self.qbc,self.qbc2,cfl_x = self.fmod.step2ds(maxm,self.num_ghost,mx,my, \
qold,self.qbc,qold2,self.qbc2,self.auxbc,self.auxbc,dx,dy,self.dt,self._method,self._mthlim,\
self.aux1,self.aux2,self.aux3,self.work,2,self.fwave,rpn2,rpt2,bar_ht,self.order)
#self.qbc2 = self.boundary_qbc2(self.qbc2,state.problem_data['BC2'],mx,my,2)
cfl = cfl_x
else:
self.qbc, cfl = self.fmod.step2(maxm,self.num_ghost,mx,my, \
qold,self.qbc,self.auxbc,dx,dy,self.dt,self._method,self._mthlim,\
self.aux1,self.aux2,self.aux3,self.work,self.fwave,rpn2,rpt2)
self.cfl.update_global_max(cfl)
state.set_q_from_qbc(self.num_ghost,self.qbc,self.qbc2)
if state.num_aux > 0:
state.set_aux_from_auxbc(self.num_ghost,self.auxbc,self.auxbc2)
# print("UPPER:", self.qbc2[0,range(2,mx),range(2,my)])
else:
raise NotImplementedError("No python implementation for step_hyperbolic in 2D.")
return self.qbc2
def boundary_qbc2(self,array,BC,mx,my,ixy):
if BC=="ww":# and ixy == 1:
array[0,1,:] = array[0,2,:]
array[0,0,:] = array[0,3,:]
array[1:3,1,:] = -array[1:3,2,:]
array[1:3,0,:] = -array[1:3,3,:]
array[1:3,:,mx+2] = -array[1:3,:,mx+1]
array[1:3,:,mx+3] = -array[1:3,:,mx]
array[0,:,mx+2] = array[0,:,mx+1]
array[0,:,mx+3] = array[0,:,mx]
# if BC=="ww" and ixy == 2:
array[1:3,:,mx+2] = -array[1:3,:,mx+1]
array[1:3,:,mx+3] = -array[1:3,:,mx]
array[0,:,mx+2] = array[0,:,mx+1]
array[0,:,mx+3] = array[0,:,mx]
array[0,1,:] = array[0,2,:]
array[0,0,:] = array[0,3,:]
array[1:3,1,:] = -array[1:3,2,:]
array[1:3,0,:] = -array[1:3,3,:]
if BC=="ee":# and ixy == 1:
array[0,1,:] = array[0,2,:]
array[0,0,:] = array[0,1,:]
array[1:3,1,:] = array[1:3,2,:]
array[1:3,0,:] = array[1:3,1,:]
# array[1:3,mx+2,:] = array[1:3,mx+1,:]
# array[1:3,mx+3,:] = array[1:3,mx+2,:]
# array[0,mx+2,:] = array[0,mx+1,:]
# array[0,mx+3,:] = array[0,mx+2,:]
# if BC=="ee" and ixy == 2:
# array[0,:,1] = array[0,:,2]
# array[0,:,0] = array[0,:,1]
# array[1:3,:,1] = array[1:3,:,2]
# array[1:3,:,0] = array[1:3,:,1]
array[1:3,:,mx+2] = array[1:3,:,mx+1]
array[1:3,:,mx+3] = array[1:3,:,mx+2]
array[0,:,mx+2] = array[0,:,mx+1]
array[0,:,mx+3] = array[0,:,mx+2]
if BC=="we":# and ixy == 1:
array[0,1,:] = array[0,2,:]
array[0,0,:] = array[0,3,:]
array[1:3,1,:] = -array[1:3,2,:]
array[1:3,0,:] = -array[1:3,3,:]
# array[1:3,mx+2,:] = -array[1:3,mx+1,:]
# array[1:3,mx+3,:] = -array[1:3,mx,:]
# array[0,mx+2,:] = array[0,mx+1,:]
# array[0,mx+3,:] = array[0,mx,:]
# if BC=="we" and ixy == 2:
# array[0,:,1] = array[0,:,2]
# array[0,:,0] = array[0,:,1]
# array[1:3,:,1] = array[1:3,:,2]
# array[1:3,:,0] = array[1:3,:,1]
array[1:3,:,mx+2] = array[1:3,:,mx+1]
array[1:3,:,mx+3] = array[1:3,:,mx+2]
array[0,:,mx+2] = array[0,:,mx+1]
array[0,:,mx+3] = array[0,:,mx+2]
if BC=="ew" and ixy == 1:
array[0,:,1] = array[0,:,2]
array[0,:,0] = array[0,:,1]
array[1:3,:,1] = array[1:3,:,2]
array[1:3,:,0] = array[1:3,:,1]
# array[1:3,:,mx+2] = array[1:3,:,mx+1]
# array[1:3,:,mx+3] = array[1:3,:,mx+2]
# array[0,:,mx+2] = array[0,:,mx+1]
# array[0,:,mx+3] = array[0,:,mx+2]
if BC=="ew" and ixy == 2:
# array[0,:,1] = array[0,:,2]
# array[0,:,0] = array[0,:,3]
# array[1:3,:,1] = -array[1:3,:,2]
# array[1:3,:,0] = -array[1:3,:,3]
array[1:3,:,mx+2] = -array[1:3,:,mx+1]
array[1:3,:,mx+3] = -array[1:3,:,mx]
array[0,:,mx+2] = array[0,:,mx+1]
array[0,:,mx+3] = array[0,:,mx]
return array
# ============================================================================
# ClawPack 3d Solver Class
# ============================================================================
class ClawSolver3D(ClawSolver):
r"""
3D Classic (Clawpack) solver.
Solve using the wave propagation algorithms of Randy LeVeque's
Clawpack code (www.clawpack.org).
In addition to the attributes of ClawSolver, ClawSolver3D
also has the following options:
.. attribute:: dimensional_split
If True, use dimensional splitting (Godunov splitting).
Dimensional splitting with Strang splitting is not supported
at present but could easily be enabled if necessary.
If False, use unsplit Clawpack algorithms, possibly including
transverse Riemann solves.
.. attribute:: transverse_waves
If dimensional_split is True, this option has no effect. If
dim_plit is False, then transverse_waves should be one of
the following values:
ClawSolver3D.no_trans: Transverse Riemann solver
not used. The stable CFL for this algorithm is 0.5. Not recommended.
ClawSolver3D.trans_inc: Transverse increment waves are computed
and propagated.
ClawSolver3D.trans_cor: Transverse increment waves and transverse
correction waves are computed and propagated.
Note that only Fortran routines are supported for now in 3D --
there is no pure-python version.
"""
__doc__ += add_parent_doc(ClawSolver)
no_trans = 0
trans_inc = 11
trans_cor = 22
def __init__(self, riemann_solver=None, claw_package=None):
r"""
Create 3d Clawpack solver
See :class:`ClawSolver3D` for more info.
"""
# Add the functions as required attributes
self.dimensional_split = True
self.transverse_waves = self.trans_cor
self.num_dim = 3
self.reflect_index = [1,2,3]
self.aux1 = None
self.aux2 = None
self.aux3 = None
self.work = None
super(ClawSolver3D,self).__init__(riemann_solver, claw_package)
# ========== Setup routine =============================
def _allocate_workspace(self,solution):
r"""
Allocate auxN and work arrays for use in Fortran subroutines.
"""
import numpy as np
state = solution.states[0]
num_eqn,num_aux,num_waves,num_ghost,aux = state.num_eqn,state.num_aux,self.num_waves,self.num_ghost,state.aux
#The following is a hack to work around an issue
#with f2py. It involves wastefully allocating three arrays.
#f2py seems not able to handle multiple zero-size arrays being passed.
# it appears the bug is related to f2py/src/fortranobject.c line 841.
if(aux is None): num_aux=1
grid = state.grid
maxmx,maxmy,maxmz = grid.num_cells[0],grid.num_cells[1],grid.num_cells[2]
maxm = max(maxmx, maxmy, maxmz)
# These work arrays really ought to live inside a fortran module
# as is done for sharpclaw
self.aux1 = np.empty((num_aux,maxm+2*num_ghost,3),order='F')
self.aux2 = np.empty((num_aux,maxm+2*num_ghost,3),order='F')
self.aux3 = np.empty((num_aux,maxm+2*num_ghost,3),order='F')
mwork = (maxm+2*num_ghost) * (31*num_eqn + num_waves + num_eqn*num_waves)
self.work = np.empty((mwork),order='F')
# ========== Hyperbolic Step =====================================
def step_hyperbolic(self,solution):
r"""
Take a step on the homogeneous hyperbolic system using the Clawpack
algorithm.
Clawpack is based on the Lax-Wendroff method, combined with Riemann
solvers and TVD limiters applied to waves.
"""
if(self.kernel_language == 'Fortran'):
state = solution.states[0]
grid = state.grid
dx,dy,dz = grid.delta
mx,my,mz = grid.num_cells
maxm = max(mx,my,mz)
self._apply_bcs(state)
qnew = self.qbc
qold = qnew.copy('F')
rpn3 = self.rp.rpn3._cpointer
if (self.dimensional_split) or (self.transverse_waves==0):
rpt3 = rpn3 # dummy value; it won't be called
rptt3 = rpn3 # dummy value; it won't be called
else:
rpt3 = self.rp.rpt3._cpointer
rptt3 = self.rp.rptt3._cpointer
if self.dimensional_split:
#Right now only Godunov-dimensional-splitting is implemented.
#Strang-dimensional-splitting could be added following dimsp3.f in Clawpack.
q, cfl_x = self.fmod.step3ds(maxm,self.num_ghost,mx,my,mz, \
qold,qnew,self.auxbc,dx,dy,dz,self.dt,self._method,self._mthlim,\
self.aux1,self.aux2,self.aux3,self.work,1,self.fwave,rpn3,rpt3,rptt3)
q, cfl_y = self.fmod.step3ds(maxm,self.num_ghost,mx,my,mz, \
q,q,self.auxbc,dx,dy,dz,self.dt,self._method,self._mthlim,\
self.aux1,self.aux2,self.aux3,self.work,2,self.fwave,rpn3,rpt3,rptt3)
q, cfl_z = self.fmod.step3ds(maxm,self.num_ghost,mx,my,mz, \
q,q,self.auxbc,dx,dy,dz,self.dt,self._method,self._mthlim,\
self.aux1,self.aux2,self.aux3,self.work,3,self.fwave,rpn3,rpt3,rptt3)
cfl = max(cfl_x,cfl_y,cfl_z)
else:
q, cfl = self.fmod.step3(maxm,self.num_ghost,mx,my,mz, \
qold,qnew,self.auxbc,dx,dy,dz,self.dt,self._method,self._mthlim,\
self.aux1,self.aux2,self.aux3,self.work,self.fwave,rpn3,rpt3,rptt3)
self.cfl.update_global_max(cfl)
state.set_q_from_qbc(self.num_ghost,self.qbc)
if state.num_aux > 0:
state.set_aux_from_auxbc(self.num_ghost,self.auxbc)
else:
raise NotImplementedError("No python implementation for step_hyperbolic in 3D.")