#!/usr/bin/env python3
"""Test the general MG solver with a variable coeffcient Helmholtz
problem. This ensures we didn't screw up the base functionality here.
Here we solve::
alpha phi + div . ( beta grad phi ) = f
with::
alpha = 1.0
beta = 2.0 + cos(2.0*pi*x)*cos(2.0*pi*y)
f = (-16.0*pi**2*cos(2*pi*x)*cos(2*pi*y) - 16.0*pi**2 + 1.0)*sin(2*pi*x)*sin(2*pi*y)
This has the exact solution::
phi = sin(2.0*pi*x)*sin(2.0*pi*y)
on [0,1] x [0,1]
We use Dirichlet BCs on phi. For beta, we do not have to impose the
same BCs, since that may represent a different physical quantity.
Here we take beta to have Neumann BCs. (Dirichlet BCs for beta will
force it to 0 on the boundary, which is not correct here)
"""
from __future__ import print_function
import os
import numpy as np
import matplotlib.pyplot as plt
import compare
import mesh.boundary as bnd
import mesh.patch as patch
import multigrid.general_MG as MG
from util import msg, io
# the analytic solution
[docs]def true(x, y):
return np.sin(2.0*np.pi*x)*np.sin(2.0*np.pi*y)
# the coefficients
[docs]def alpha(x, y):
return np.ones_like(x)
[docs]def beta(x, y):
return 2.0 + np.cos(2.0*np.pi*x)*np.cos(2.0*np.pi*y)
[docs]def gamma_x(x, y):
return np.zeros_like(x)
[docs]def gamma_y(x, y):
return np.zeros_like(x)
# the righthand side
[docs]def f(x, y):
return (-16.0*np.pi**2*np.cos(2*np.pi*x)*np.cos(2*np.pi*y) -
16.0*np.pi**2 + 1.0)*np.sin(2*np.pi*x)*np.sin(2*np.pi*y)
[docs]def test_general_poisson_dirichlet(N, store_bench=False, comp_bench=False,
make_plot=False, verbose=1, rtol=1.e-12):
"""
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_alphabeta_only_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, 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
if __name__ == "__main__":
N = [16, 32, 64]
err = []
plot = False
store = False
do_compare = False
for nx in N:
if nx == max(N):
plot = True
enorm = test_general_poisson_dirichlet(nx, make_plot=plot,
store_bench=store, comp_bench=do_compare)
err.append(enorm)
# plot the convergence
N = np.array(N, dtype=np.float64)
err = np.array(err)
plt.clf()
plt.loglog(N, err, "x", color="r")
plt.loglog(N, err[0]*(N[0]/N)**2, "--", color="k")
plt.xlabel("N")
plt.ylabel("error")
fig = plt.gcf()
fig.set_size_inches(7.0, 6.0)
plt.tight_layout()
plt.savefig("mg_general_alphabeta_only_converge.png")