def do_demo():
""" show examples of the patch methods / classes """
import util.io as io
# illustrate basic mesh operations
myg = Grid2d(8, 16, xmax=1.0, ymax=2.0)
mydata = CellCenterData2d(myg)
bc = bnd.BC()
mydata.register_var("a", bc)
mydata.create()
a = mydata.get_var("a")
a[:, :] = np.exp(-(myg.x2d - 0.5)**2 - (myg.y2d - 1.0)**2)
print(mydata)
# output
print("writing\n")
mydata.write("mesh_test")
print("reading\n")
myd2 = io.read("mesh_test")
print(myd2)
mydata.pretty_print("a")
def do_demo():
""" show examples of the patch methods / classes """
import util.io as io
# illustrate basic mesh operations
myg = Grid2d(8, 16, xmax=1.0, ymax=2.0)
mydata = CellCenterData2d(myg)
bc = bnd.BC()
mydata.register_var("a", bc)
mydata.create()
a = mydata.get_var("a")
a[:, :] = np.exp(-(myg.x2d - 0.5)**2 - (myg.y2d - 1.0)**2)
print(mydata)
# output
print("writing\n")
mydata.write("mesh_test")
print("reading\n")
myd2 = io.read("mesh_test")
print(myd2)
mydata.pretty_print("a")
def makeplot(plotfile, variable, outfile):
sim = io.read(plotfile)
myd = sim.cc_data
myg = myd.grid
if variable == "vort":
vx = myd.get_var("x-velocity")
vy = myd.get_var("y-velocity")
v = myg.scratch_array()
v[myg.ilo:myg.ihi+1,myg.jlo:myg.jhi+1] = \
0.5*(vy[myg.ilo+1:myg.ihi+2,myg.jlo:myg.jhi+1] -
vy[myg.ilo-1:myg.ihi,myg.jlo:myg.jhi+1])/myg.dx - \
0.5*(vx[myg.ilo:myg.ihi+1,myg.jlo+1:myg.jhi+2] -
vx[myg.ilo:myg.ihi+1,myg.jlo-1:myg.jhi])/myg.dy
else:
v = myd.get_var(variable)
plt.figure(num=1, figsize=(6.0, 6.0), dpi=100, facecolor='w')
plt.imshow(np.transpose(v[myg.ilo:myg.ihi + 1, myg.jlo:myg.jhi + 1]),
interpolation="nearest",
origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax])
plt.axis("off")
plt.subplots_adjust(bottom=0.0, top=1.0, left=0.0, right=1.0)
plt.savefig(outfile)
def makeplot(plotfile_name, outfile, width, height):
""" plot the data in a plotfile using the solver's vis() method """
sim = io.read(plotfile_name)
plt.figure(num=1, figsize=(width, height), dpi=100, facecolor='w')
sim.dovis()
if outfile.endswith(".pdf"):
plt.savefig(outfile, bbox_inches="tight")
else:
plt.savefig(outfile)
def makeplot(basename, outfile, width, height):
""" plot the data in a plotfile using the solver's vis() method """
files = glob.glob(basename + '_[0-9]*_[1-9][0-9]*.h5')
print(files)
sim_average = io.read(files[0])
sim_average.cc_data.get_vars()[:, :, :] = 0
for f in files:
print(f)
s = io.read(f)
sim_average.cc_data.get_vars(
)[:, :, :] += s.cc_data.get_vars()[:, :, :] / len(files)
plt.figure(num=1, figsize=(width, height), dpi=100, facecolor='w')
sim_average.dovis()
if outfile.endswith(".pdf"):
plt.savefig(outfile, bbox_inches="tight")
else:
plt.savefig(outfile)
plt.show()
def makeplot(plotfile_name, outfile, width, height):
""" plot the data in a plotfile using the solver's vis() method """
sim = io.read(plotfile_name)
plt.figure(num=1, figsize=(width, height), dpi=100, facecolor='w')
sim.dovis()
if outfile.endswith(".pdf"):
plt.savefig(outfile, bbox_inches="tight")
else:
plt.savefig(outfile)
plt.show()
def makeplot(plotfile,
variable,
outfile,
width=6.5,
height=5.25,
log=False,
compact=False,
quiet=False):
sim = io.read(plotfile)
if isinstance(sim, patch.CellCenterData2d):
myd = sim
else:
myd = sim.cc_data
myg = myd.grid
plt.figure(num=1, figsize=(width, height), dpi=100, facecolor='w')
var = myd.get_var(variable)
if log:
var = np.log10(var)
img = plt.imshow(np.transpose(var.v()),
interpolation="nearest",
origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax])
if not compact:
plt.colorbar(img)
plt.xlabel("x")
plt.ylabel("y")
if compact:
plt.axis("off")
plt.subplots_adjust(bottom=0.0, top=1.0, left=0.0, right=1.0)
plt.savefig(outfile)
else:
plt.savefig(outfile, bbox_inches="tight")
if not quiet:
plt.show()
def compare_to_benchmark(self):
""" Are we comparing to a benchmark? """
basename = self.rp.get_param("io.basename")
compare_file = "{}/tests/{}{:04d}".format(self.solver_name, basename,
self.sim.n)
msg.warning("comparing to: {} ".format(compare_file))
try:
sim_bench = io.read(compare_file)
except IOError:
msg.warning("ERROR openning compare file")
return "ERROR openning compare file"
result = compare.compare(self.sim.cc_data, sim_bench.cc_data)
if result == 0:
msg.success("results match benchmark\n")
else:
msg.warning("ERROR: " + compare.errors[result] + "\n")
return result
def test_write_read():
myg = patch.Grid2d(8, 6, ng=2, xmax=1.0, ymax=1.0)
myd = patch.CellCenterData2d(myg)
bco = bnd.BC(xlb="outflow", xrb="outflow", ylb="outflow", yrb="outflow")
myd.register_var("a", bco)
myd.create()
a = myd.get_var("a")
a.v()[:, :] = np.arange(48).reshape(8, 6)
myd.write("io_test")
# now read it in
nd = io.read("io_test")
anew = nd.get_var("a")
assert_array_equal(anew.v(), a.v())
def makeplot(plotfile, variable, outfile):
sim = io.read(plotfile)
myd = sim.cc_data
myg = myd.grid
plt.figure(num=1, figsize=(6.5,5.25), dpi=100, facecolor='w')
var = myd.get_var(variable)
img = plt.imshow(np.transpose(var.v()),
interpolation="nearest", origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax])
plt.colorbar()
plt.xlabel("x")
plt.ylabel("y")
plt.savefig(outfile, bbox_inches="tight")
plt.show()
def compare_to_benchmark(self, rtol):
""" Are we comparing to a benchmark? """
basename = self.rp.get_param("io.basename")
compare_file = "{}/tests/{}{:04d}".format(
self.solver_name, basename, self.sim.n)
msg.warning("comparing to: {} ".format(compare_file))
try:
sim_bench = io.read(compare_file)
except IOError:
msg.warning("ERROR opening compare file")
return "ERROR opening compare file"
result = compare.compare(self.sim.cc_data, sim_bench.cc_data, rtol)
if result == 0:
msg.success("results match benchmark to within relative tolerance of {}\n".format(rtol))
else:
msg.warning("ERROR: " + compare.errors[result] + "\n")
return result
def test_write_read():
myg = patch.Grid2d(8, 6, ng=2, xmax=1.0, ymax=1.0)
myd = patch.CellCenterData2d(myg)
bco = bnd.BC(xlb="outflow", xrb="outflow",
ylb="outflow", yrb="outflow")
myd.register_var("a", bco)
myd.create()
a = myd.get_var("a")
a.v()[:, :] = np.arange(48).reshape(8, 6)
myd.write("io_test")
# now read it in
nd = io.read("io_test")
anew = nd.get_var("a")
assert_array_equal(anew.v(), a.v())
def makeplot(plotfile, variable, outfile,
width=6.5, height=5.25,
log=False, compact=False, quiet=False):
sim = io.read(plotfile)
if isinstance(sim, patch.CellCenterData2d):
myd = sim
else:
myd = sim.cc_data
myg = myd.grid
plt.figure(num=1, figsize=(width, height), dpi=100, facecolor='w')
var = myd.get_var(variable)
if log:
var = np.log10(var)
plt.imshow(np.transpose(var.v()),
interpolation="nearest", origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax])
if not compact:
plt.colorbar()
plt.xlabel("x")
plt.ylabel("y")
if compact:
plt.axis("off")
plt.subplots_adjust(bottom=0.0, top=1.0, left=0.0, right=1.0)
plt.savefig(outfile)
else:
plt.savefig(outfile, bbox_inches="tight")
if not quiet:
plt.show()
def makeplot(plotfile, variable, outfile, vmin=None, vmax=None):
sim = io.read(plotfile)
myd = sim.cc_data
myg = myd.grid
if variable == "vort":
vx = myd.get_var("x-velocity")
vy = myd.get_var("y-velocity")
v = myg.scratch_array()
v[myg.ilo:myg.ihi+1, myg.jlo:myg.jhi+1] = \
0.5*(vy[myg.ilo+1:myg.ihi+2, myg.jlo:myg.jhi+1] -
vy[myg.ilo-1:myg.ihi, myg.jlo:myg.jhi+1])/myg.dx - \
0.5*(vx[myg.ilo:myg.ihi+1, myg.jlo+1:myg.jhi+2] -
vx[myg.ilo:myg.ihi+1, myg.jlo-1:myg.jhi])/myg.dy
else:
v = myd.get_var(variable)
if vmin is None:
vmin = v.v().min()
if vmax is None:
vmax = v.v().max()
plt.figure(num=1, figsize=(6.0, 6.0), dpi=100, facecolor='w')
plt.imshow(np.transpose(v[myg.ilo:myg.ihi+1, myg.jlo:myg.jhi+1]),
interpolation="nearest", origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax],
vmin=vmin, vmax=vmax)
plt.axis("off")
plt.subplots_adjust(bottom=0.0, top=1.0, left=0.0, right=1.0)
plt.savefig(outfile)
def process(file):
# read the data and convert to the primitive variables (and
# velocity magnitude)
sim = io.read(file)
myd = sim.cc_data
myg = myd.grid
phi_t = myd.get_var("phi")
phi = phi_t[myg.ilo:myg.ihi + 1, myg.jlo:myg.jhi + 1]
# get the problem parameters
t_0 = myd.get_aux("t_0")
phi_0 = myd.get_aux("phi_0")
phi_max = myd.get_aux("phi_max")
k = myd.get_aux("k")
t = myd.t
# radially bin
# see http://code.google.com/p/agpy/source/browse/trunk/agpy/radialprofile.py?r=317
# for inspiration
# first define the bins
rmin = 0
rmax = np.sqrt(myg.xmax**2 + myg.ymax**2)
nbins = np.int(np.sqrt(myg.nx**2 + myg.ny**2))
# bins holds the edges, so there is one more value than actual bin
# bin_centers holds the center value of the bin
bins = np.linspace(rmin, rmax, nbins + 1)
bin_centers = 0.5 * (bins[1:] + bins[:-1])
# radius of each zone
xcenter = 0.5 * (myg.xmin + myg.xmax)
ycenter = 0.5 * (myg.ymin + myg.ymax)
r = np.sqrt(
(myg.x2d[myg.ilo:myg.ihi + 1, myg.jlo:myg.jhi + 1] - xcenter)**2 +
(myg.y2d[myg.ilo:myg.ihi + 1, myg.jlo:myg.jhi + 1] - ycenter)**2)
# bin the radii -- digitize returns an array with the same shape
# as the input array but with elements of the array specifying
# which bin that location belongs to. The value of whichbin will
# be 1 if we are located in the bin defined by bins[0] to bins[1].
# This means that there will be no 0s
whichbin = np.digitize(r.flat, bins)
# bincount counts the number of occurrences of each non-negative
# integer value in whichbin. Each entry in ncount gives the
# number of occurrences of it in whichbin. The length of ncount
# is set by the maximum value in whichbin
ncount = np.bincount(whichbin)
# now bin the associated data
phi_bin = np.zeros(len(ncount) - 1, dtype=np.float64)
for n in range(len(ncount)):
# remember that there are no whichbin == 0, since that
# corresponds to the left edge. So we want whichbin == 1 to
# correspond to the first value of bin_centers
# (bin_centers[0])
phi_bin[n - 1] = np.sum(phi.flat[whichbin == n]) / np.sum(ncount[n])
bin_centers = bin_centers[0:len(ncount) - 1]
# get the analytic solution
phi_exact = gaussian.phi_analytic(bin_centers, t, t_0, k, phi_0, phi_max)
return bin_centers, phi_exact, phi_bin
def doit(solver_name, problem_name, param_file,
other_commands=None,
comp_bench=False, reset_bench_on_fail=False, make_bench=False):
"""The main driver to run pyro"""
msg.bold('pyro ...')
tc = profile.TimerCollection()
tm_main = tc.timer("main")
tm_main.begin()
# import desired solver under "solver" namespace
solver = importlib.import_module(solver_name)
#-------------------------------------------------------------------------
# runtime parameters
#-------------------------------------------------------------------------
# parameter defaults
rp = runparams.RuntimeParameters()
rp.load_params("_defaults")
rp.load_params(solver_name + "/_defaults")
# problem-specific runtime parameters
rp.load_params(solver_name + "/problems/_" + problem_name + ".defaults")
# now read in the inputs file
if not os.path.isfile(param_file):
# check if the param file lives in the solver's problems directory
param_file = solver_name + "/problems/" + param_file
if not os.path.isfile(param_file):
msg.fail("ERROR: inputs file does not exist")
rp.load_params(param_file, no_new=1)
# and any commandline overrides
if other_commands is not None:
rp.command_line_params(other_commands)
# write out the inputs.auto
rp.print_paramfile()
#-------------------------------------------------------------------------
# initialization
#-------------------------------------------------------------------------
# initialize the Simulation object -- this will hold the grid and
# data and know about the runtime parameters and which problem we
# are running
sim = solver.Simulation(solver_name, problem_name, rp, timers=tc)
sim.initialize()
sim.preevolve()
#-------------------------------------------------------------------------
# evolve
#-------------------------------------------------------------------------
verbose = rp.get_param("driver.verbose")
plt.ion()
sim.cc_data.t = 0.0
# output the 0th data
basename = rp.get_param("io.basename")
sim.write("{}{:04d}".format(basename, sim.n))
dovis = rp.get_param("vis.dovis")
if dovis:
plt.figure(num=1, figsize=(8, 6), dpi=100, facecolor='w')
sim.dovis()
while not sim.finished():
# fill boundary conditions
sim.cc_data.fill_BC_all()
# get the timestep
sim.compute_timestep()
# evolve for a single timestep
sim.evolve()
if verbose > 0:
print("%5d %10.5f %10.5f" % (sim.n, sim.cc_data.t, sim.dt))
# output
if sim.do_output():
if verbose > 0:
msg.warning("outputting...")
basename = rp.get_param("io.basename")
sim.write("{}{:04d}".format(basename, sim.n))
# visualization
if dovis:
tm_vis = tc.timer("vis")
tm_vis.begin()
sim.dovis()
store = rp.get_param("vis.store_images")
if store == 1:
basename = rp.get_param("io.basename")
plt.savefig("{}{:04d}.png".format(basename, sim.n))
tm_vis.end()
# final output
if verbose > 0:
msg.warning("outputting...")
basename = rp.get_param("io.basename")
sim.write("{}{:04d}".format(basename, sim.n))
tm_main.end()
#-------------------------------------------------------------------------
# benchmarks (for regression testing)
#-------------------------------------------------------------------------
result = 0
# are we comparing to a benchmark?
if comp_bench:
compare_file = "{}/tests/{}{:04d}".format(
solver_name, basename, sim.n)
msg.warning("comparing to: {} ".format(compare_file))
try:
sim_bench = io.read(compare_file)
except:
msg.warning("ERROR openning compare file")
return "ERROR openning compare file"
result = compare.compare(sim.cc_data, sim_bench.cc_data)
if result == 0:
msg.success("results match benchmark\n")
else:
msg.warning("ERROR: " + compare.errors[result] + "\n")
# are we storing a benchmark?
if make_bench or (result != 0 and reset_bench_on_fail):
if not os.path.isdir(solver_name + "/tests/"):
try:
os.mkdir(solver_name + "/tests/")
except:
msg.fail("ERROR: unable to create the solver's tests/ directory")
bench_file = solver_name + "/tests/" + basename + "%4.4d" % (sim.n)
msg.warning("storing new benchmark: {}\n".format(bench_file))
sim.write(bench_file)
#-------------------------------------------------------------------------
# final reports
#-------------------------------------------------------------------------
if verbose > 0:
rp.print_unused_params()
tc.report()
sim.finalize()
if comp_bench:
return result
def test_poisson_dirichlet(N, store_bench=False, comp_bench=False,
make_plot=False, verbose=1, rtol=1e-12):
# test the multigrid solver
nx = N
ny = nx
# create the multigrid object
a = MG.CellCenterMG2d(nx, ny,
xl_BC_type="dirichlet", yl_BC_type="dirichlet",
xr_BC_type="dirichlet", yr_BC_type="dirichlet",
verbose=verbose)
# initialize the solution to 0
a.init_zeros()
# initialize the RHS using the function f
rhs = f(a.x2d, a.y2d)
a.init_RHS(rhs)
# solve to a relative tolerance of 1.e-11
a.solve(rtol=1.e-11)
# alternately, we can just use smoothing by uncommenting the following
# a.smooth(a.nlevels-1,50000)
# get the solution
v = a.get_solution()
# compute the error from the analytic solution
b = true(a.x2d, a.y2d)
e = v - b
print(" L2 error from true solution = %g\n rel. err from previous cycle = %g\n num. cycles = %d" %
(e.norm(), a.relative_error, a.num_cycles))
# plot it
if make_plot:
plt.figure(num=1, figsize=(5.0, 5.0), dpi=100, facecolor='w')
plt.imshow(np.transpose(v[a.ilo:a.ihi+1, a.jlo:a.jhi+1]),
interpolation="nearest", origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
plt.savefig("mg_test.png")
# store the output for later comparison
bench = "mg_poisson_dirichlet"
bench_dir = os.environ["PYRO_HOME"] + "/multigrid/tests/"
my_data = a.get_solution_object()
if store_bench:
my_data.write("{}/{}".format(bench_dir, bench))
# do we do a comparison?
if comp_bench:
compare_file = "{}/{}".format(bench_dir, bench)
msg.warning("comparing to: %s " % (compare_file))
bench_data = io.read(compare_file)
result = compare.compare(my_data, bench_data, rtol)
if result == 0:
msg.success("results match benchmark to within relative tolerance of {}\n".format(rtol))
else:
msg.warning("ERROR: " + compare.errors[result] + "\n")
return result
return None
usage = """
compare the output for a Sedov problem with the exact solution contained
in cylindrical-sedov.out. To do this, we need to bin the Sedov data
into radial bins."""
parser = argparse.ArgumentParser(description=usage)
parser.add_argument("-o", type=str, default="sedov_compare.png",
metavar="plot.png", help="output file name")
parser.add_argument("plotfile", type=str, nargs=1,
help="the plotfile you wish to plot")
args = parser.parse_args()
# read the data and convert to the primitive variables (and velocity
# magnitude)
sim = io.read(args.plotfile[0])
myd = sim.cc_data
myg = myd.grid
dens = myd.get_var("density")
xmom = myd.get_var("x-momentum")
ymom = myd.get_var("y-momentum")
ener = myd.get_var("energy")
rho = dens.v()
u = np.sqrt(xmom.v()**2 + ymom.v()**2)/rho
e = (ener.v() - 0.5*rho*u*u)/rho
gamma = myd.get_aux("gamma")
p = rho*e*(gamma - 1.0)
def test_poisson_dirichlet(N, store_bench=False, comp_bench=False,
make_plot=False, verbose=1, rtol=1e-12):
# test the multigrid solver
nx = N
ny = nx
# create the multigrid object
a = MG.CellCenterMG2d(nx, ny,
xl_BC_type="dirichlet", yl_BC_type="dirichlet",
xr_BC_type="dirichlet", yr_BC_type="dirichlet",
verbose=verbose)
# initialize the solution to 0
a.init_zeros()
# initialize the RHS using the function f
rhs = f(a.x2d, a.y2d)
a.init_RHS(rhs)
# solve to a relative tolerance of 1.e-11
a.solve(rtol=1.e-11)
# alternately, we can just use smoothing by uncommenting the following
# a.smooth(a.nlevels-1,50000)
# get the solution
v = a.get_solution()
# compute the error from the analytic solution
b = true(a.x2d, a.y2d)
e = v - b
print(" L2 error from true solution = %g\n rel. err from previous cycle = %g\n num. cycles = %d" %
(e.norm(), a.relative_error, a.num_cycles))
# plot it
if make_plot:
plt.figure(num=1, figsize=(5.0, 5.0), dpi=100, facecolor='w')
plt.imshow(np.transpose(v[a.ilo:a.ihi+1, a.jlo:a.jhi+1]),
interpolation="nearest", origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
print("Saving figure to mg_test.png")
plt.savefig("mg_test.png")
# store the output for later comparison
bench = "mg_poisson_dirichlet"
bench_dir = os.environ["PYRO_HOME"] + "/multigrid/tests/"
my_data = a.get_solution_object()
if store_bench:
my_data.write("{}/{}".format(bench_dir, bench))
# do we do a comparison?
if comp_bench:
compare_file = "{}/{}".format(bench_dir, bench)
msg.warning("comparing to: %s " % (compare_file))
bench_data = io.read(compare_file)
result = compare.compare(my_data, bench_data, rtol)
if result == 0:
msg.success("results match benchmark to within relative tolerance of {}\n".format(rtol))
else:
msg.warning("ERROR: " + compare.errors[result] + "\n")
return result
return None
def abort(string):
print(string)
sys.exit(2)
if not len(sys.argv) == 2:
print(usage)
sys.exit(2)
try:
file1 = sys.argv[1]
except IndexError:
print(usage)
sys.exit(2)
sim = io.read(file1)
myd = sim.cc_data
myg = myd.grid
# time of file
t = myd.t
if myg.nx > myg.ny:
# x-problem
xmin = myg.xmin
xmax = myg.xmax
param_file = "inputs.dam.x"
else:
# y-problem
xmin = myg.ymin
xmax = myg.ymax
param_file = "inputs.dam.y"
usage: ./convergence.py fine coarse
"""
def compare(fine, coarse):
dens = coarse.get_var("density")
dens_avg = fine.restrict("density", N=2)
e = coarse.grid.scratch_array()
e.v()[:, :] = dens.v() - dens_avg.v()
return float(np.abs(e).max()), e.norm()
if __name__ == "__main__":
if not len(sys.argv) == 3:
print(usage)
sys.exit(2)
fine = sys.argv[1]
coarse = sys.argv[2]
ff = io.read(fine)
cc = io.read(coarse)
result = compare(ff.cc_data, cc.cc_data)
print("inf/L2 norm of density: ", result)
def process(file):
# read the data and convert to the primitive variables (and
# velocity magnitude)
sim = io.read(file)
myd = sim.cc_data
myg = myd.grid
phi_t = myd.get_var("phi")
phi = phi_t[myg.ilo:myg.ihi+1, myg.jlo:myg.jhi+1]
# get the problem parameters
t_0 = myd.get_aux("t_0")
phi_0 = myd.get_aux("phi_0")
phi_max = myd.get_aux("phi_max")
k = myd.get_aux("k")
t = myd.t
# radially bin
# see http://code.google.com/p/agpy/source/browse/trunk/agpy/radialprofile.py?r=317
# for inspiration
# first define the bins
rmin = 0
rmax = np.sqrt(myg.xmax**2 + myg.ymax**2)
nbins = np.int(np.sqrt(myg.nx**2 + myg.ny**2))
# bins holds the edges, so there is one more value than actual bin
# bin_centers holds the center value of the bin
bins = np.linspace(rmin, rmax, nbins+1)
bin_centers = 0.5*(bins[1:] + bins[:-1])
# radius of each zone
xcenter = 0.5*(myg.xmin + myg.xmax)
ycenter = 0.5*(myg.ymin + myg.ymax)
r = np.sqrt((myg.x2d[myg.ilo:myg.ihi+1, myg.jlo:myg.jhi+1] - xcenter)**2 +
(myg.y2d[myg.ilo:myg.ihi+1, myg.jlo:myg.jhi+1] - ycenter)**2)
# bin the radii -- digitize returns an array with the same shape
# as the input array but with elements of the array specifying
# which bin that location belongs to. The value of whichbin will
# be 1 if we are located in the bin defined by bins[0] to bins[1].
# This means that there will be no 0s
whichbin = np.digitize(r.flat, bins)
# bincount counts the number of occurrences of each non-negative
# integer value in whichbin. Each entry in ncount gives the
# number of occurrences of it in whichbin. The length of ncount
# is set by the maximum value in whichbin
ncount = np.bincount(whichbin)
# now bin the associated data
phi_bin = np.zeros(len(ncount)-1, dtype=np.float64)
for n in range(len(ncount)):
# remember that there are no whichbin == 0, since that
# corresponds to the left edge. So we want whichbin == 1 to
# correspond to the first value of bin_centers
# (bin_centers[0])
phi_bin[n-1] = np.sum(phi.flat[whichbin == n])/np.sum(ncount[n])
bin_centers = bin_centers[0:len(ncount)-1]
# get the analytic solution
phi_exact = gaussian.phi_analytic(bin_centers, t, t_0, k, phi_0, phi_max)
return bin_centers, phi_exact, phi_bin
else:
print("{:20s} absolute error = {:10.10g}".format(name, abs_err))
if not np.allclose(d1.v(), d2.v(), rtol=rtol):
result = "varerr"
return result
if __name__ == "__main__":
if not (len(sys.argv) == 3 or len(sys.argv) == 4):
print(usage)
sys.exit(2)
file1 = sys.argv[1]
file2 = sys.argv[2]
s1 = io.read(file1)
s2 = io.read(file2)
if len(sys.argv) == 3:
result = compare(s1.cc_data, s2.cc_data)
else:
result = compare(s1.cc_data, s2.cc_data, rtol=float(sys.argv[3]))
if result == 0:
print("SUCCESS: files agree")
else:
print("ERROR: ", errors[result])
def test_general_poisson_dirichlet(N,
store_bench=False,
comp_bench=False,
make_plot=False,
verbose=1):
"""
test the general MG solver. The return value
here is the error compared to the exact solution, UNLESS
comp_bench=True, in which case the return value is the
error compared to the stored benchmark
"""
# test the multigrid solver
nx = N
ny = nx
# create the coefficient variable
g = patch.Grid2d(nx, ny, ng=1)
d = patch.CellCenterData2d(g)
bc_c = bnd.BC(xlb="neumann", xrb="neumann", ylb="neumann", yrb="neumann")
d.register_var("alpha", bc_c)
d.register_var("beta", bc_c)
d.register_var("gamma_x", bc_c)
d.register_var("gamma_y", bc_c)
d.create()
a = d.get_var("alpha")
a[:, :] = alpha(g.x2d, g.y2d)
b = d.get_var("beta")
b[:, :] = beta(g.x2d, g.y2d)
gx = d.get_var("gamma_x")
gx[:, :] = gamma_x(g.x2d, g.y2d)
gy = d.get_var("gamma_y")
gy[:, :] = gamma_y(g.x2d, g.y2d)
# create the multigrid object
a = MG.GeneralMG2d(nx,
ny,
xl_BC_type="dirichlet",
yl_BC_type="dirichlet",
xr_BC_type="dirichlet",
yr_BC_type="dirichlet",
coeffs=d,
verbose=verbose,
vis=0,
true_function=true)
# initialize the solution to 0
a.init_zeros()
# initialize the RHS using the function f
rhs = f(a.x2d, a.y2d)
a.init_RHS(rhs)
# solve to a relative tolerance of 1.e-11
a.solve(rtol=1.e-11)
# alternately, we can just use smoothing by uncommenting the following
# a.smooth(a.nlevels-1,50000)
# get the solution
v = a.get_solution()
# compute the error from the analytic solution
b = true(a.x2d, a.y2d)
e = v - b
enorm = e.norm()
print(
" L2 error from true solution = %g\n rel. err from previous cycle = %g\n num. cycles = %d"
% (enorm, a.relative_error, a.num_cycles))
# plot the solution
if make_plot:
plt.clf()
plt.figure(figsize=(10.0, 4.0), dpi=100, facecolor='w')
plt.subplot(121)
plt.imshow(np.transpose(v.v()),
interpolation="nearest",
origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
plt.title("nx = {}".format(nx))
plt.colorbar()
plt.subplot(122)
plt.imshow(np.transpose(e.v()),
interpolation="nearest",
origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
plt.title("error")
plt.colorbar()
plt.tight_layout()
plt.savefig("mg_general_dirichlet_test.png")
# store the output for later comparison
bench = "mg_general_poisson_dirichlet"
bench_dir = os.environ["PYRO_HOME"] + "/multigrid/tests/"
my_data = a.get_solution_object()
if store_bench:
my_data.write("{}/{}".format(bench_dir, bench))
# do we do a comparison?
if comp_bench:
compare_file = "{}/{}".format(bench_dir, bench)
msg.warning("comparing to: %s " % (compare_file))
bench = io.read(compare_file)
result = compare.compare(my_data, bench)
if result == 0:
msg.success("results match benchmark\n")
else:
msg.warning("ERROR: " + compare.errors[result] + "\n")
return result
# normal return -- error wrt true solution
return enorm
parser = argparse.ArgumentParser(description=usage)
parser.add_argument("-o",
type=str,
default="sedov_compare.png",
metavar="plot.png",
help="output file name")
parser.add_argument("plotfile",
type=str,
nargs=1,
help="the plotfile you wish to plot")
args = parser.parse_args()
# read the data and convert to the primitive variables (and velocity
# magnitude)
sim = io.read(args.plotfile[0])
myd = sim.cc_data
myg = myd.grid
dens = myd.get_var("density")
xmom = myd.get_var("x-momentum")
ymom = myd.get_var("y-momentum")
ener = myd.get_var("energy")
rho = dens.v()
u = np.sqrt(xmom.v()**2 + ymom.v()**2) / rho
e = (ener.v() - 0.5 * rho * u * u) / rho
gamma = myd.get_aux("gamma")
p = rho * e * (gamma - 1.0)
usage: ./incomp_converge_error.py file
"""
if not len(sys.argv) == 2:
print(usage)
sys.exit(2)
try:
file1 = sys.argv[1]
except IndexError:
print(usage)
sys.exit(2)
sim = io.read(file1)
myd = sim.cc_data
myg = myd.grid
# numerical solution
u = myd.get_var("x-velocity")
v = myd.get_var("y-velocity")
t = myd.t
# analytic solution
u_exact = myg.scratch_array()
u_exact[:, :] = 1.0 - 2.0*np.cos(2.0*math.pi*(myg.x2d-t))*np.sin(2.0*math.pi*(myg.y2d-t))
v_exact = myg.scratch_array()
v_exact[:, :] = 1.0 + 2.0*np.sin(2.0*math.pi*(myg.x2d-t))*np.cos(2.0*math.pi*(myg.y2d-t))
else:
print("{:20s} absolute error = {:10.10g}".format(name, abs_err))
if not np.allclose(d1.v(), d2.v(), rtol=rtol):
result = "varerr"
return result
if __name__ == "__main__":
if not (len(sys.argv) == 3 or len(sys.argv) == 4):
print(usage)
sys.exit(2)
file1 = sys.argv[1]
file2 = sys.argv[2]
s1 = io.read(file1)
s2 = io.read(file2)
if len(sys.argv) == 3:
result = compare(s1.cc_data, s2.cc_data)
else:
result = compare(s1.cc_data, s2.cc_data, rtol=float(sys.argv[3]))
if result == 0:
print("SUCCESS: files agree")
else:
print("ERROR: ", errors[result])
def test_vc_poisson_periodic(N,
store_bench=False,
comp_bench=False,
make_plot=False,
verbose=1):
"""
test the variable-coefficient MG solver. The return value
here is the error compared to the exact solution, UNLESS
comp_bench=True, in which case the return value is the
error compared to the stored benchmark
"""
# test the multigrid solver
nx = N
ny = nx
# create the coefficient variable
g = patch.Grid2d(nx, ny, ng=1)
d = patch.CellCenterData2d(g)
bc_c = bnd.BC(xlb="periodic",
xrb="periodic",
ylb="periodic",
yrb="periodic")
d.register_var("c", bc_c)
d.create()
c = d.get_var("c")
c[:, :] = alpha(g.x2d, g.y2d)
# check whether the RHS sums to zero (necessary for periodic data)
rhs = f(g.x2d, g.y2d)
print("rhs sum: {}".format(np.sum(rhs[g.ilo:g.ihi + 1, g.jlo:g.jhi + 1])))
# create the multigrid object
a = MG.VarCoeffCCMG2d(nx,
ny,
xl_BC_type="periodic",
yl_BC_type="periodic",
xr_BC_type="periodic",
yr_BC_type="periodic",
coeffs=c,
coeffs_bc=bc_c,
verbose=verbose,
vis=0,
true_function=true)
# initialize the solution to 0
a.init_zeros()
# initialize the RHS using the function f
rhs = f(a.x2d, a.y2d)
a.init_RHS(rhs)
# solve to a relative tolerance of 1.e-11
a.solve(rtol=1.e-11)
# alternately, we can just use smoothing by uncommenting the following
# a.smooth(a.nlevels-1,10000)
# get the solution
v = a.get_solution()
# get the true solution
b = true(a.x2d, a.y2d)
# compute the error from the analytic solution -- note that with
# periodic BCs all around, there is nothing to normalize the
# solution. We subtract off the average of phi from the MG
# solution (we do the same for the true solution to put them on
# the same footing)
e = v - np.sum(v.v()) / (nx * ny) - (
b - np.sum(b[a.ilo:a.ihi + 1, a.jlo:a.jhi + 1]) / (nx * ny))
enorm = e.norm()
print(
" L2 error from true solution = %g\n rel. err from previous cycle = %g\n num. cycles = %d"
% (enorm, a.relative_error, a.num_cycles))
# plot the solution
if make_plot:
plt.clf()
plt.figure(figsize=(10.0, 4.0), dpi=100, facecolor='w')
plt.subplot(121)
plt.imshow(np.transpose(v.v()),
interpolation="nearest",
origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
plt.title("nx = {}".format(nx))
plt.colorbar()
plt.subplot(122)
plt.imshow(np.transpose(e.v()),
interpolation="nearest",
origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
plt.title("error")
plt.colorbar()
plt.tight_layout()
plt.savefig("mg_vc_periodic_test.png")
# store the output for later comparison
bench = "mg_vc_poisson_periodic"
bench_dir = os.environ["PYRO_HOME"] + "/multigrid/tests/"
my_data = a.get_solution_object()
if store_bench:
my_data.write("{}/{}".format(bench_dir, bench))
# do we do a comparison?
if comp_bench:
compare_file = "{}/{}".format(bench_dir, bench)
msg.warning("comparing to {}".format(compare_file))
bench = io.read(compare_file)
result = compare.compare(my_data, bench)
if result == 0:
msg.success("results match benchmark\n")
else:
msg.warning("ERROR: {}\n".format(compare.errors[result]))
return result
# normal return -- error wrt true solution
return enorm
def test_vc_poisson_dirichlet(N, store_bench=False, comp_bench=False,
make_plot=False, verbose=1, rtol=1.e-12):
"""
test the variable-coefficient MG solver. The return value
here is the error compared to the exact solution, UNLESS
comp_bench=True, in which case the return value is the
error compared to the stored benchmark
"""
# test the multigrid solver
nx = N
ny = nx
# create the coefficient variable
g = patch.Grid2d(nx, ny, ng=1)
d = patch.CellCenterData2d(g)
bc_c = bnd.BC(xlb="neumann", xrb="neumann",
ylb="neumann", yrb="neumann")
d.register_var("c", bc_c)
d.create()
c = d.get_var("c")
c[:, :] = alpha(g.x2d, g.y2d)
# create the multigrid object
a = MG.VarCoeffCCMG2d(nx, ny,
xl_BC_type="dirichlet", yl_BC_type="dirichlet",
xr_BC_type="dirichlet", yr_BC_type="dirichlet",
coeffs=c, coeffs_bc=bc_c,
verbose=verbose, vis=0, true_function=true)
# initialize the solution to 0
a.init_zeros()
# initialize the RHS using the function f
rhs = f(a.x2d, a.y2d)
a.init_RHS(rhs)
# solve to a relative tolerance of 1.e-11
a.solve(rtol=1.e-11)
# alternately, we can just use smoothing by uncommenting the following
# a.smooth(a.nlevels-1,50000)
# get the solution
v = a.get_solution()
# compute the error from the analytic solution
b = true(a.x2d, a.y2d)
e = v - b
enorm = e.norm()
print(" L2 error from true solution = %g\n rel. err from previous cycle = %g\n num. cycles = %d" %
(enorm, a.relative_error, a.num_cycles))
# plot the solution
if make_plot:
plt.clf()
plt.figure(figsize=(10.0, 4.0), dpi=100, facecolor='w')
plt.subplot(121)
plt.imshow(np.transpose(v.v()),
interpolation="nearest", origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
plt.title("nx = {}".format(nx))
plt.colorbar()
plt.subplot(122)
plt.imshow(np.transpose(e.v()),
interpolation="nearest", origin="lower",
extent=[a.xmin, a.xmax, a.ymin, a.ymax])
plt.xlabel("x")
plt.ylabel("y")
plt.title("error")
plt.colorbar()
plt.tight_layout()
plt.savefig("mg_vc_dirichlet_test.png")
# store the output for later comparison
bench = "mg_vc_poisson_dirichlet"
bench_dir = os.environ["PYRO_HOME"] + "/multigrid/tests/"
my_data = a.get_solution_object()
if store_bench:
my_data.write("{}/{}".format(bench_dir, bench))
# do we do a comparison?
if comp_bench:
compare_file = "{}/{}".format(bench_dir, bench)
msg.warning("comparing to: %s " % (compare_file))
bench = io.read(compare_file)
result = compare.compare(my_data, bench, rtol)
if result == 0:
msg.success("results match benchmark to within relative tolerance of {}\n".format(rtol))
else:
msg.warning("ERROR: " + compare.errors[result] + "\n")
return result
# normal return -- error wrt true solution
return enorm
