def _main():
'''Main function.
'''
args = _parse_input_arguments()
# read the mesh
#print "Reading the mesh...",
mesh, point_data, _ = \
voropy.reader.read(args.filename, timestep=args.timestep)
psi0 = point_data['psi'][:, 0] + 1j * point_data['psi'][:, 1]
# build the model evaluator
mu = 1.0e-1
g = 1.0
num_nodes = len(mesh.node_coords)
V = -np.ones(num_nodes)
modeleval = pynosh.modelevaluator_nls.NlsModelEvaluator(
mesh, V=V, A=point_data['A']
)
# Get the preconditioner for in matrix form
keo = modeleval._get_keo(mu)
if g > 0.0:
if modeleval.mesh.control_volumes is None:
modeleval.mesh.compute_control_volumes(
variant=modeleval.cv_variant
)
alpha = g * 2.0 * (psi0.real**2 + psi0.imag**2) \
* modeleval.mesh.control_volumes.reshape(psi0.shape)
num_unknowns = len(psi0)
from scipy import sparse
prec = keo \
+ sparse.spdiags(alpha, [0], num_unknowns, num_unknowns)
else:
prec = keo
# https://code.google.com/p/pyamg/source/browse/trunk/Examples/SolverDiagnostics/solver_diagnostics.py
solver_diagnostics(
prec,
fname='solver_diagnostic',
definiteness='positive',
symmetry='hermitian'
)
return
def _main():
"""Main function.
"""
args = _parse_input_arguments()
# read the mesh
# print "Reading the mesh...",
mesh, point_data, _ = meshplex.reader.read(args.filename, timestep=args.timestep)
psi0 = point_data["psi"][:, 0] + 1j * point_data["psi"][:, 1]
# build the model evaluator
mu = 1.0e-1
g = 1.0
num_nodes = len(mesh.node_coords)
V = -np.ones(num_nodes)
modeleval = pynosh.modelevaluator_nls.NlsModelEvaluator(
mesh, V=V, A=point_data["A"]
)
# Get the preconditioner for in matrix form
keo = modeleval._get_keo(mu)
if g > 0.0:
if modeleval.mesh.control_volumes is None:
modeleval.mesh.compute_control_volumes(variant=modeleval.cv_variant)
alpha = (
g
* 2.0
* (psi0.real ** 2 + psi0.imag ** 2)
* modeleval.mesh.control_volumes.reshape(psi0.shape)
)
num_unknowns = len(psi0)
from scipy import sparse
prec = keo + sparse.spdiags(alpha, [0], num_unknowns, num_unknowns)
else:
prec = keo
# https://code.google.com/p/pyamg/source/browse/trunk/Examples/SolverDiagnostics/solver_diagnostics.py
solver_diagnostics(
prec, fname="solver_diagnostic", definiteness="positive", symmetry="hermitian"
)
return
type='FE', epsilon=0.001, theta=2 * np.pi / 16.0)
A = pyamg.gallery.stencil_grid(stencil, (50, 50), format='csr')
choice = eval(input('\nThere are four different test problems. Enter \n' +
'1: Isotropic diffusion example\n' +
'2: Anisotropic diffusion example\n' +
'3: Elasticity example\n' +
'4: Nonsymmetric flow example\n\n '))
choice = int(choice)
if choice == 1:
# Try a basic isotropic diffusion problem from finite differences
# --> Only use V-cycles by specifying cycle_list
# --> Don't specify symmetry and definiteness and allow for auto-detection
A = pyamg.gallery.poisson((50, 50), format='csr')
solver_diagnostics(A, fname='iso_diff_diagnostic', cycle_list=['V'])
# To run the best solver found above, uncomment next two lines
# from iso_diff_diagnostic import iso_diff_diagnostic
# iso_diff_diagnostic(A)
if choice == 2:
# Try a basic rotated anisotropic diffusion problem from bilinear finite elements
# --> Only use V-cycles by specifying cycle_list
# --> Specify symmetry and definiteness (the safest option)
# --> Choose the rootnode_solver
stencil = pyamg.gallery.diffusion.diffusion_stencil_2d(
type='FE', epsilon=0.001, theta=2 * np.pi / 16.0)
A = pyamg.gallery.stencil_grid(stencil, (50, 50), format='csr')
solver_diagnostics(A, fname='rot_ani_diff_diagnostic',
cycle_list=['V'],
