def test_initialization():
from pyclaw.clawpack.clawpack import ClawSolver2D
solver = ClawSolver2D()
import peanoclaw
peano_solver = peanoclaw.Solver(solver, 1.0)
import inspect
for member in inspect.getmembers(peano_solver):
if (not member[0].startswith("_") and not inspect.ismethod(member[1])):
print((member[0]))
for member in inspect.getmembers(solver):
if (not member[0].startswith("_") and not inspect.ismethod(member[1])):
print((member[0]))
def shallow2D(use_petsc=False,iplot=0,htmlplot=False,outdir='./_output',solver_type='classic',amr_type=None):
#===========================================================================
# Import libraries
#===========================================================================
from clawpack import riemann
if use_petsc:
import clawpack.petclaw as pyclaw
else:
from clawpack import pyclaw
#===========================================================================
# Setup solver and solver parameters
#===========================================================================
if solver_type == 'classic':
solver = pyclaw.ClawSolver2D(riemann.shallow_roe_with_efix_2D)
solver.limiters = pyclaw.limiters.tvd.MC
solver.dimensional_split=1
elif solver_type == 'sharpclaw':
solver = pyclaw.SharpClawSolver2D(riemann.shallow_roe_with_efix_2D)
solver.bc_lower[0] = pyclaw.BC.wall
solver.bc_upper[0] = pyclaw.BC.wall
solver.bc_lower[1] = pyclaw.BC.wall
solver.bc_upper[1] = pyclaw.BC.wall
#===========================================================================
# Initialize domain and state, then initialize the solution associated to the
# state and finally initialize aux array
#===========================================================================
# resolution of each grid
mgrid = 6
# number of initial AMR grids in each dimension
msubgrid = 9
if amr_type is not None:
m = mgrid
else:
# number of Domain grid cells expressed as the product of
# grid resolution and the number of AMR sub-grids for
# easy comparison between the two methods
m = mgrid*msubgrid
mx = m
my = m
# Domain:
xlower = 0
xupper = 1
ylower = 0
yupper = 1
x = pyclaw.Dimension('x',xlower,xupper,mx)
y = pyclaw.Dimension('y',ylower,yupper,my)
domain = pyclaw.Domain([x,y])
state = pyclaw.State(domain,solver.num_eqn)
grav = 1.0 # Parameter (global auxiliary variable)
state.problem_data['grav'] = grav
# Initial solution
# ================
# Riemann states of the dam break problem
damRadius = 0.25 #0.5
hl = 2.
ul = 0.
vl = 0.
hr = 1.
ur = 0.
vr = 0.
qinit(state,hl,ul,vl,hr,ur,vl,damRadius) # This function is defined above
# this closure is used by AMR-style codes
def qinit_callback(state):
qinit(state,hl,ul,vl,hr,ur,vl,damRadius)
#===========================================================================
# Set up controller and controller parameters
#===========================================================================
claw = pyclaw.Controller()
claw.tfinal = 3e-1 #3e-1 #0.03
if amr_type is not None:
if amr_type == 'peano':
import peanoclaw as amrclaw
claw.solver = amrclaw.Solver(solver
,1/(mgrid*msubgrid)
,qinit_callback
#,refinement_criterion=refinement_criterion_time_dependent
#,refinement_criterion=refinement_criterion
#,refinement_criterion=refinement_criterion_gradient
)
claw.solution = amrclaw.Solution(state, domain)
else:
raise Exception('unsupported amr_type %s' % amr_type)
else:
claw.solver = solver
claw.solution = pyclaw.Solution(state,domain)
#claw.keep_copy = True
#claw.outdir = outdir
claw.keep_copy = False
claw.output_format = None
claw.outdir = None
claw.num_output_times = 10
#===========================================================================
# Plot results
#===========================================================================
if htmlplot: pyclaw.plot.html_plot(outdir=outdir)
if iplot: pyclaw.plot.interactive_plot(outdir=outdir)
return claw
def test_3x3_grid():
r"""This test simply solves a 3x3 grid, once with PyClaw and once as one patch
with PeanoClaw. In the end it checks if the resulting qbcs match.
"""
#===========================================================================
# Import libraries
#===========================================================================
import numpy as np
import pyclaw
import peanoclaw
#===========================================================================
# Setup solver and solver parameters for PyClaw run
#===========================================================================
pyclaw_solver = setup_solver()
#===========================================================================
# Setup solver and solver parameters for PeanoClaw run
#===========================================================================
peanoclaw_solver = setup_solver()
peano_solver = peanoclaw.Solver(peanoclaw_solver, 1.0)
#===========================================================================
# Initialize domain and state, then initialize the solution associated to the
# state and finally initialize aux array
#===========================================================================
# Domain:
xlower = 0.0
xupper = 1.0
mx = 3
ylower = 0.0
yupper = 1.0
my = 3
x = pyclaw.Dimension(xlower,xupper,mx,name='x')
y = pyclaw.Dimension(ylower,yupper,my,name='y')
domain = pyclaw.Domain([x,y])
num_eqn = 3 # Number of equations
pyclaw_state = pyclaw.State(domain,num_eqn)
peanoclaw_state = pyclaw.State(domain, num_eqn)
grav = 1.0 # Parameter (global auxiliary variable)
pyclaw_state.problem_data['grav'] = grav
peanoclaw_state.problem_data['grav'] = grav
# Initial solution
# ================
# Riemann states of the dam break problem
damRadius = 0.2
hl = 2.
ul = 0.
vl = 0.
hr = 1.
ur = 0.
vr = 0.
qinit(pyclaw_state,hl,ul,vl,hr,ur,vl,damRadius) # This function is defined above
qinit(peanoclaw_state,hl,ul,vl,hr,ur,vl,damRadius) # This function is defined above
tfinal = 1.0
#===========================================================================
# Set up controller and controller parameters for PyClaw run
#===========================================================================
pyclaw_controller = pyclaw.Controller()
pyclaw_controller.tfinal = tfinal
pyclaw_controller.solution = pyclaw.Solution(pyclaw_state,domain)
pyclaw_controller.solver = pyclaw_solver
pyclaw_controller.num_output_times = 1
pyclaw_controller.run()
#===========================================================================
# Set up controller and controller parameters for PyClaw run
#===========================================================================
peanoclaw_controller = pyclaw.Controller()
peanoclaw_controller.tfinal = tfinal
peanoclaw_controller.solution = pyclaw.Solution(peanoclaw_state,domain)
peanoclaw_controller.solver = peano_solver
peanoclaw_controller.num_output_times = 1
peanoclaw_controller.run()
assert(np.max(np.abs(pyclaw_solver.qbc - peanoclaw_solver.qbc)) < 1e-9)
def shockbubble(use_petsc=False,
iplot=False,
htmlplot=False,
outdir='./_output',
solver_type='classic',
amr_type=None):
"""
Solve the Euler equations of compressible fluid dynamics.
This example involves a bubble of dense gas that is impacted by a shock.
"""
from clawpack import riemann
if use_petsc:
import clawpack.petclaw as pyclaw
else:
from clawpack import pyclaw
if solver_type == 'sharpclaw':
solver = pyclaw.SharpClawSolver3D(riemann.euler_3D)
solver.weno_order = 5
solver.lim_type = 2
else:
solver = pyclaw.ClawSolver3D(riemann.euler_3D)
solver.dimensional_split = 1
# solver.transverse_waves = 3
solver.limiters = 4
solver.cfl_max = 0.2
solver.cfl_desired = 0.15
solver.order = 2
solver.dt_initial = 1 # 0.000125
solver.num_waves = 3
solver.bc_lower[0] = pyclaw.BC.custom
solver.bc_upper[0] = pyclaw.BC.extrap
solver.bc_lower[1] = pyclaw.BC.extrap
solver.bc_upper[1] = pyclaw.BC.extrap
solver.bc_lower[2] = pyclaw.BC.extrap
solver.bc_upper[2] = pyclaw.BC.extrap
# Initialize domain
factor = 1 #2.0/3.0+1e-10#1.5+1e-10
mx = int(12 * factor)
my = int(6 * factor)
mz = int(6 * factor)
# number of initial AMR grids in each dimension
msubgrid = 27
if amr_type is None:
# number of Domain grid cells expressed as the product of
# grid resolution and the number of AMR sub-grids for
# easy comparison between the two methods
mx = mx * msubgrid
my = my * msubgrid
mz = mz * msubgrid
x = pyclaw.Dimension('x', 0.0, 2.0, mx)
y = pyclaw.Dimension('y', -0.5, 0.5, my)
z = pyclaw.Dimension('z', -0.5, 0.5, mz)
domain = pyclaw.Domain([x, y, z])
num_eqn = 5
state = pyclaw.State(domain, num_eqn)
state.problem_data['gamma'] = gamma
state.problem_data['gamma1'] = gamma1
qinit(state)
print np.min(state.q[0, ...].reshape(-1))
solver.user_bc_lower = shockbc
claw = pyclaw.Controller()
claw.tfinal = 0.5 #1
claw.num_output_times = 10 #50
claw.outdir = outdir
claw.output_format = None
if amr_type is not None:
if amr_type == 'peano':
import peanoclaw as amrclaw
claw.solver = amrclaw.Solver(
solver,
(x.upper - x.lower) / (mx * msubgrid),
qinit
#,refinement_criterion=refinement_criterion_gradient
,
internal_settings=amrclaw.InternalSettings(
enable_peano_logging=True,
fork_level_increment=1,
use_dimensional_splitting_optimization=True))
claw.solution = amrclaw.Solution(state, domain)
else:
raise Exception('unsupported amr_type %s' % amr_type)
else:
claw.solver = solver
claw.solution = pyclaw.Solution(state, domain)
return claw