def test_petsc4py_matrix(pushpop_parameters):
"Test PETScMatrix <-> petsc4py.PETSc.Mat conversions"
parameters["linear_algebra_backend"] = "PETSc"
# Assemble a test matrix
mesh = UnitSquareMesh(4, 4)
V = FunctionSpace(mesh, "Lagrange", 1)
u, v = TrialFunction(V), TestFunction(V)
a = u * v * dx
A1 = assemble(a)
# Test conversion dolfin.PETScMatrix -> petsc4py.PETSc.Mat
A1 = as_backend_type(A1)
M1 = A1.mat()
# Copy and scale matrix with petsc4py
M2 = M1.copy()
M2.scale(2.0)
# Test conversion petsc4py.PETSc.Mat -> PETScMatrix
A2 = PETScMatrix(M2)
assert (A1.array() * 2.0 == A2.array()).all()
def test_petsc4py_matrix(pushpop_parameters):
"Test PETScMatrix <-> petsc4py.PETSc.Mat conversions"
parameters["linear_algebra_backend"] = "PETSc"
# Assemble a test matrix
mesh = UnitSquareMesh(4, 4)
V = FunctionSpace(mesh, "Lagrange", 1)
u, v = TrialFunction(V), TestFunction(V)
a = u*v*dx
A1 = assemble(a)
# Test conversion dolfin.PETScMatrix -> petsc4py.PETSc.Mat
A1 = as_backend_type(A1)
M1 = A1.mat()
# Copy and scale matrix with petsc4py
M2 = M1.copy()
M2.scale(2.0)
# Test conversion petsc4py.PETSc.Mat -> PETScMatrix
A2 = PETScMatrix(M2)
assert (A1.array()*2.0 == A2.array()).all()
if __name__ == '__main__':
from dolfin import *
from weak_bcs.la_solve import my_eigvalsh
mesh_c = UnitSquareMesh(1, 1)
mesh_f = adapt(mesh_c)
Vc = FunctionSpace(mesh_c, 'CG', 1)
Vf = FunctionSpace(mesh_f, 'CG', 1)
Jmat = injection_matrix(Vc, Vf)
J = PETScMatrix(Jmat)
f = Expression('x[0]+x[1]', degree=1)
x = interpolate(f, Vc).vector().get_local()
y = J.array().dot(x) # Inject coarse into fine
y0 = interpolate(f, Vf).vector().get_local()
print(np.linalg.norm(y0 - y))
# fc = sum(ff_i*c_i
# c_i = dof_fi(phic_j)
out = None
for n in (2, 4, 8, 16, 32):
mesh_c = UnitSquareMesh(n, n)
A, B, W = stokes_iir(mesh_c)
A = ii_convert(A).array()
B = ii_convert(B).array()
lmin, lmax = np.sort(np.abs(my_eigvalsh(A, B)))[[0, -1]]
