def fig_61_62_63(kernel_language='Python',
iplot=False,
htmlplot=False,
solver_type='classic',
outdir='./_output'):
"""
Compare several methods for advecting a Gaussian and square wave.
The settings coded here are for Figure 6.1(a).
For Figure 6.1(b), set solver.order=2.
For Figure 6.2(a), set solver.order=2 and solver.limiters = pyclaw.limiters.tvd.minmod
For Figure 6.2(b), set solver.order=2 and solver.limiters = pyclaw.limiters.tvd.superbee
For Figure 6.2(c), set solver.order=2 and solver.limiters = pyclaw.limiters.tvd.MC
For Figure 6.3, set IC='wavepacket' and other options as appropriate.
"""
import numpy as np
import pyclaw
if solver_type == 'sharpclaw':
solver = pyclaw.SharpClawSolver1D()
else:
solver = pyclaw.ClawSolver1D()
solver.kernel_language = kernel_language
from riemann import rp_advection
solver.num_waves = rp_advection.num_waves
if solver.kernel_language == 'Python':
solver.rp = rp_advection.rp_advection_1d
solver.bc_lower[0] = 2
solver.bc_upper[0] = 2
solver.limiters = 0
solver.order = 1
solver.cfl_desired = 0.8
x = pyclaw.Dimension('x', 0.0, 1.0, mx)
grid = pyclaw.Grid(x)
num_eqn = 1
state = pyclaw.State(grid, num_eqn)
state.problem_data['u'] = 1.
xc = grid.x.center
if IC == 'gauss_square':
state.q[0, :] = np.exp(-beta * (xc - x0)**2) + (xc > 0.6) * (xc < 0.8)
elif IC == 'wavepacket':
state.q[0, :] = np.exp(-beta * (xc - x0)**2) * np.sin(80. * xc)
else:
raise Exception('Unrecognized initial condition specification.')
claw = pyclaw.Controller()
claw.solution = pyclaw.Solution(state)
claw.solver = solver
claw.outdir = outdir
claw.tfinal = 10.0
status = claw.run()
if htmlplot: pyclaw.plot.html_plot(outdir=outdir)
if iplot: pyclaw.plot.interactive_plot(outdir=outdir)
def burgers(iplot=1, htmlplot=0, outdir='./_output'):
"""
Example from Chapter 11 of LeVeque, Figure 11.8.
Shows decay of an initial wave packet to an N-wave with Burgers' equation.
"""
import numpy as np
import pyclaw
solver = pyclaw.ClawSolver1D()
solver.num_waves = 1
solver.limiters = pyclaw.limiters.tvd.MC
solver.bc_lower[0] = pyclaw.BC.periodic
solver.bc_upper[0] = pyclaw.BC.periodic
#===========================================================================
# Initialize grids and then initialize the solution associated to the grid
#===========================================================================
x = pyclaw.Dimension('x', -8.0, 8.0, 1000)
grid = pyclaw.Grid(x)
num_eqn = 1
state = pyclaw.State(grid, num_eqn)
xc = grid.x.center
state.q[0, :] = (xc > -np.pi) * (xc < np.pi) * (2. * np.sin(3. * xc) +
np.cos(2. * xc) + 0.2)
state.q[0, :] = state.q[0, :] * (np.cos(xc) + 1.)
state.problem_data['efix'] = True
#===========================================================================
# Setup controller and controller parameters. Then solve the problem
#===========================================================================
claw = pyclaw.Controller()
claw.tfinal = 6.0
claw.num_output_times = 30
claw.solution = pyclaw.Solution(state)
claw.solver = solver
claw.outdir = outdir
status = claw.run()
if htmlplot: pyclaw.plot.html_plot(outdir=outdir)
if iplot: pyclaw.plot.interactive_plot(outdir=outdir)
def shallow_4_Rossby_Haurwitz(iplot=0,htmlplot=False,outdir='./_output'):
# Import pyclaw module
import pyclaw
#===========================================================================
# Set up solver and solver parameters
#===========================================================================
solver = pyclaw.ClawSolver2D()
import riemann
solver.rp = riemann.rp2_shallow_sphere
import classic2
solver.fmod = classic2
# Set boundary conditions
# =======================
# Conserved variables
solver.bc_lower[0] = pyclaw.BC.periodic
solver.bc_upper[0] = pyclaw.BC.periodic
solver.bc_lower[1] = pyclaw.BC.custom # Custom BC for sphere
solver.bc_upper[1] = pyclaw.BC.custom # Custom BC for sphere
solver.user_bc_lower = qbc_lower_y
solver.user_bc_upper = qbc_upper_y
# Auxiliary array
solver.aux_bc_lower[0] = pyclaw.BC.periodic
solver.aux_bc_upper[0] = pyclaw.BC.periodic
solver.aux_bc_lower[1] = pyclaw.BC.custom # Custom BC for sphere
solver.aux_bc_upper[1] = pyclaw.BC.custom # Custom BC for sphere
solver.user_aux_bc_lower = auxbc_lower_y
solver.user_aux_bc_upper = auxbc_upper_y
# Dimensional splitting ?
# =======================
solver.dimensional_split = 0
# Transverse increment waves and transverse correction waves are computed
# and propagated.
# =======================================================================
solver.transverse_waves = 2
# Number of waves in each Riemann solution
# ========================================
solver.num_waves = 3
# Use source splitting method
# ===========================
solver.source_split = 2
# Set source function
# ===================
solver.step_source = fortran_src_wrapper
# Set the limiter for the waves
# =============================
solver.limiters = pyclaw.limiters.tvd.MC
#===========================================================================
# Initialize domain and state, then initialize the solution associated to the
# state and finally initialize aux array
#===========================================================================
# Domain:
# ====
xlower = -3.0
xupper = 1.0
mx = 100
ylower = -1.0
yupper = 1.0
my = 50
# Check whether or not the even number of cells are used in in both
# directions. If odd numbers are used a message is print at screen and the
# simulation is interrputed.
if(mx % 2 != 0 or my % 2 != 0):
import sys
message = 'Please, use even numbers of cells in both direction. ' \
'Only even numbers allow to impose correctly the boundary ' \
'conditions!'
print message
sys.exit(0)
x = pyclaw.Dimension('x',xlower,xupper,mx)
y = pyclaw.Dimension('y',ylower,yupper,my)
domain = pyclaw.Domain([x,y])
dx = domain.grid.delta[0]
dy = domain.grid.delta[1]
# Define some parameters used in Fortran common blocks
solver.fmod.comxyt.dxcom = dx
solver.fmod.comxyt.dycom = dy
solver.fmod.sw.g = 11489.57219
solver.rp.comxyt.dxcom = dx
solver.rp.comxyt.dycom = dy
solver.rp.sw.g = 11489.57219
# Define state object
# ===================
num_eqn = 4 # Number of equations
num_aux = 16 # Number of auxiliary variables
state = pyclaw.State(domain,num_eqn,num_aux)
# Override default mapc2p function
# ================================
state.grid.mapc2p = mapc2p_sphere_vectorized
# Set auxiliary variables
# =======================
import problem
# Get lower left corner coordinates
xlower,ylower = state.grid.lower[0],state.grid.lower[1]
num_ghost = 2
auxtmp = np.ndarray(shape=(num_aux,mx+2*num_ghost,my+2*num_ghost), dtype=float, order='F')
auxtmp = problem.setaux(mx,my,num_ghost,mx,my,xlower,ylower,dx,dy,auxtmp,Rsphere)
state.aux[:,:,:] = auxtmp[:,num_ghost:-num_ghost,num_ghost:-num_ghost]
# Set index for capa
state.index_capa = 0
# Set initial conditions
# ======================
# 1) Call fortran function
qtmp = np.ndarray(shape=(num_eqn,mx+2*num_ghost,my+2*num_ghost), dtype=float, order='F')
qtmp = problem.qinit(mx,my,num_ghost,mx,my,xlower,ylower,dx,dy,qtmp,auxtmp,Rsphere)
state.q[:,:,:] = qtmp[:,num_ghost:-num_ghost,num_ghost:-num_ghost]
# 2) call python function define above
#qinit(state,mx,my)
#===========================================================================
# Set up controller and controller parameters
#===========================================================================
claw = pyclaw.Controller()
claw.keep_copy = False
claw.output_style = 1
claw.num_output_times = 10
claw.tfinal = 10
claw.solution = pyclaw.Solution(state,domain)
claw.solver = solver
claw.outdir = outdir
#===========================================================================
# Solve the problem
#===========================================================================
status = claw.run()
#===========================================================================
# Plot results
#===========================================================================
if htmlplot: pyclaw.plot.html_plot(outdir=outdir)
if iplot: pyclaw.plot.interactive_plot(outdir=outdir)
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 acoustics(solver_type='classic',iplot=True,htmlplot=False,outdir='./_output',problem='figure 9.4'):
"""
This example solves the 1-dimensional variable-coefficient acoustics
equations in a medium with a single interface.
"""
from numpy import sqrt, abs
import pyclaw
if solver_type=='classic':
solver = pyclaw.ClawSolver1D()
elif solver_type=='sharpclaw':
solver = pyclaw.SharpClawSolver1D()
else: raise Exception('Unrecognized value of solver_type.')
solver.num_waves=2
solver.limiters = pyclaw.limiters.tvd.MC
solver.bc_lower[0] = pyclaw.BC.extrap
solver.bc_upper[0] = pyclaw.BC.extrap
solver.aux_bc_lower[0] = pyclaw.BC.extrap
solver.aux_bc_upper[0] = pyclaw.BC.extrap
x = pyclaw.Dimension('x',-5.0,5.0,500)
grid = pyclaw.Grid(x)
num_eqn = 2
num_aux = 2
state = pyclaw.State(grid,num_eqn,num_aux)
if problem == 'figure 9.4':
rhol = 1.0
cl = 1.0
rhor = 2.0
cr = 0.5
elif problem == 'figure 9.5':
rhol = 1.0
cl = 1.0
rhor = 4.0
cr = 0.5
zl = rhol*cl
zr = rhor*cr
xc = grid.x.center
state.aux[0,:] = (xc<=0)*zl + (xc>0)*zr # Impedance
state.aux[1,:] = (xc<=0)*cl + (xc>0)*cr # Sound speed
# initial condition: half-ellipse
state.q[0,:] = sqrt(abs(1.-(xc+3.)**2))*(xc>-4.)*(xc<-2.)
state.q[1,:] = state.q[0,:] + 0.
claw = pyclaw.Controller()
claw.solution = pyclaw.Solution(state)
claw.solver = solver
claw.tfinal = 5.0
claw.num_output_times = 10
# Solve
status = claw.run()
# Plot results
if htmlplot: pyclaw.plot.html_plot(outdir=outdir)
if iplot: pyclaw.plot.interactive_plot(outdir=outdir)
def contourLineSphere(fileName='fort.q0000', path='./_output'):
"""
This function plot the contour lines on a spherical surface for the shallow
water equations solved on a sphere.
"""
# Open file
# =========
# Concatenate path and file name
pathFileName = path + "/" + fileName
try:
f = file(pathFileName, "r")
except IOError as e:
print("({})".format(e))
sys.exit()
# Read file header
# ================
# The information contained in the first two lines are not used.
unsed = f.readline() # patch_number
unused = f.readline() # AMR_level
# Read mx, my, xlow, ylow, dx and dy
line = f.readline()
sline = line.split()
mx = int(sline[0])
line = f.readline()
sline = line.split()
my = int(sline[0])
line = f.readline()
sline = line.split()
xlower = float(sline[0])
line = f.readline()
sline = line.split()
ylower = float(sline[0])
line = f.readline()
sline = line.split()
dx = float(sline[0])
line = f.readline()
sline = line.split()
dy = float(sline[0])
# Patch:
# ====
xupper = xlower + mx * dx
yupper = ylower + my * dy
x = pyclaw.Dimension('x', xlower, xupper, mx)
y = pyclaw.Dimension('y', ylower, yupper, my)
patch = pyclaw.Patch([x, y])
# Override default mapc2p function
# ================================
patch.mapc2p = rh.mapc2p_sphere_vectorized
# Compute the physical coordinates of each cell's centers
# ======================================================
patch.compute_p_centers(recompute=True)
xp = patch._p_centers[0]
yp = patch._p_centers[1]
zp = patch._p_centers[2]
patch.compute_c_centers(recompute=True)
xc = patch._c_centers[0]
yc = patch._c_centers[1]
# Define arrays of conserved variables
h = np.zeros((mx, my))
hu = np.zeros((mx, my))
hv = np.zeros((mx, my))
hw = np.zeros((mx, my))
# Read solution
for j in range(my):
tmp = np.fromfile(f, dtype='float', sep=" ", count=4 * mx)
tmp = tmp.reshape((mx, 4))
h[:, j] = tmp[:, 0]
hu[:, j] = tmp[:, 1]
hv[:, j] = tmp[:, 2]
hw[:, j] = tmp[:, 3]
# Plot solution in the computational domain
# =========================================
# Fluid height
plt.figure()
CS = plt.contour(xc, yc, h)
plt.title('Fluid height (computational domain)')
plt.xlabel('xc')
plt.ylabel('yc')
plt.clabel(CS, inline=1, fontsize=10)
plt.show()
def fig_31_38(kernel_language='Fortran',solver_type='classic',iplot=False,htmlplot=False,outdir='./_output'):
r"""Produces the output shown in Figures 3.1 and 3.8 of the FVM book.
These involve simple waves in the acoustics system."""
import pyclaw
import numpy as np
#=================================================================
# Import the appropriate solver type, depending on the options passed
#=================================================================
if solver_type=='classic':
solver = pyclaw.ClawSolver1D()
elif solver_type=='sharpclaw':
solver = pyclaw.SharpClawSolver1D()
else: raise Exception('Unrecognized value of solver_type.')
#========================================================================
# Instantiate the solver and define the system of equations to be solved
#========================================================================
solver.kernel_language=kernel_language
from riemann import rp_acoustics
solver.num_waves=rp_acoustics.num_waves
if kernel_language=='Python':
solver.rp = rp_acoustics.rp_acoustics_1d
solver.limiters = pyclaw.limiters.tvd.MC
solver.bc_lower[0] = pyclaw.BC.wall
solver.bc_upper[0] = pyclaw.BC.extrap
#========================================================================
# Instantiate the grid and set the boundary conditions
#========================================================================
x = pyclaw.Dimension('x',-1.0,1.0,800)
grid = pyclaw.Grid(x)
num_eqn = 2
state = pyclaw.State(grid,num_eqn)
#========================================================================
# Set problem-specific variables
#========================================================================
rho = 1.0
bulk = 0.25
state.problem_data['rho']=rho
state.problem_data['bulk']=bulk
state.problem_data['zz']=np.sqrt(rho*bulk)
state.problem_data['cc']=np.sqrt(bulk/rho)
#========================================================================
# Set the initial condition
#========================================================================
xc=grid.x.center
beta=100; gamma=0; x0=0.75
state.q[0,:] = 0.5*np.exp(-80 * xc**2) + 0.5*(np.abs(xc+0.2)<0.1)
state.q[1,:] = 0.
#========================================================================
# Set up the controller object
#========================================================================
claw = pyclaw.Controller()
claw.solution = pyclaw.Solution(state)
claw.solver = solver
claw.outdir = outdir
claw.tfinal = 3.0
claw.num_output_times = 30
# Solve
status = claw.run()
# Plot results
if htmlplot: pyclaw.plot.html_plot(outdir=outdir)
if iplot: pyclaw.plot.interactive_plot(outdir=outdir)
def shallow_4_Rossby_Haurwitz(iplot=0, htmlplot=False, outdir='./_output'):
# Import pyclaw module
import pyclaw
#===========================================================================
# Set up solver and solver parameters
#===========================================================================
solver = pyclaw.ClawSolver2D()
# Set boundary conditions
# =======================
# Conserved variables
solver.bc_lower[0] = pyclaw.BC.periodic
solver.bc_upper[0] = pyclaw.BC.periodic
solver.bc_lower[1] = pyclaw.BC.custom # Custom BC for sphere
solver.bc_upper[1] = pyclaw.BC.custom # Custom BC for sphere
solver.user_bc_lower = qbc_lower_y
solver.user_bc_upper = qbc_upper_y
# Auxiliary array
solver.aux_bc_lower[0] = pyclaw.BC.periodic
solver.aux_bc_upper[0] = pyclaw.BC.periodic
solver.aux_bc_lower[1] = pyclaw.BC.custom # Custom BC for sphere
solver.aux_bc_upper[1] = pyclaw.BC.custom # Custom BC for sphere
solver.user_aux_bc_lower = auxbc_lower_y
solver.user_aux_bc_upper = auxbc_upper_y
# Dimensional splitting ?
# =======================
solver.dimensional_split = 0
# Transverse increment waves and transverse correction waves are computed
# and propagated.
# =======================================================================
solver.transverse_waves = 2
# Number of waves in each Riemann solution
# ========================================
solver.num_waves = 3
# Use source splitting method
# ===========================
solver.source_split = 2
# Set source function
# ===================
solver.step_source = fortran_src_wrapper
# Set the limiter for the waves
# =============================
solver.limiters = pyclaw.limiters.tvd.MC
#===========================================================================
# Initialize domain and state, then initialize the solution associated to the
# state and finally initialize aux array
#===========================================================================
# Domain:
# ====
xlower = -3.0
xupper = 1.0
mx = 40
ylower = -1.0
yupper = 1.0
my = 20
x = pyclaw.Dimension('x', xlower, xupper, mx)
y = pyclaw.Dimension('y', ylower, yupper, my)
domain = pyclaw.Domain([x, y])
dx = domain.delta[0]
dy = domain.delta[1]
# Define some parameters used in classic2
import classic2
classic2.comxyt.dxcom = dx
classic2.comxyt.dycom = dy
classic2.sw.g = 11489.57219
# Override default mapc2p function
# ================================
grid = domain.grid
grid.mapc2p = mapc2p_sphere_vectorized
# Define state object
# ===================
num_eqn = 4 # Number of equations
num_aux = 16 # Number of auxiliary variables
state = pyclaw.State(domain, num_eqn, num_aux)
# Set auxiliary variables
# =======================
import problem
# Get lower left corner coordinates
xlower, ylower = grid.lower[0], grid.lower[1]
num_ghost = 2
auxtmp = np.ndarray(shape=(num_aux, mx + 2 * num_ghost,
my + 2 * num_ghost),
dtype=float,
order='F')
auxtmp = problem.setaux(mx, my, num_ghost, mx, my, xlower, ylower, dx, dy,
auxtmp, Rsphere)
state.aux[:, :, :] = auxtmp[:, num_ghost:-num_ghost, num_ghost:-num_ghost]
# Set index for capa
state.index_capa = 0
# Set initial conditions
# ======================
# 1) Call fortran function
qtmp = np.ndarray(shape=(num_eqn, mx + 2 * num_ghost, my + 2 * num_ghost),
dtype=float,
order='F')
qtmp = problem.qinit(mx, my, num_ghost, mx, my, xlower, ylower, dx, dy,
qtmp, auxtmp, Rsphere)
state.q[:, :, :] = qtmp[:, num_ghost:-num_ghost, num_ghost:-num_ghost]
# 2) call python function define above
#qinit(state,mx,my)
#===========================================================================
# Set up controller and controller parameters
#===========================================================================
claw = pyclaw.Controller()
claw.keep_copy = True
claw.output_style = 1
claw.num_output_times = 10
claw.tfinal = 10
claw.solution = pyclaw.Solution(state, domain)
claw.solver = solver
claw.outdir = outdir
#===========================================================================
# Solve the problem
#===========================================================================
status = claw.run()
#===========================================================================
# Plot results
#===========================================================================
if htmlplot: pyclaw.plot.html_plot(outdir=outdir)
if iplot: pyclaw.plot.interactive_plot(outdir=outdir)
# Define variable usedto verify the correctness of the regression test
# ====================================================================
height = claw.frames[claw.num_output_times].state.q[0, :, :]
return height
