def test_ldc_bnd():
nx = 4
ny = nx
nz = nx
dim = 3
dof = 4
parameters = {'Reynolds Number': 100}
n = nx * ny * nz * dof
discretization = Discretization(parameters, nx, ny, nz, dim, dof)
atom = discretization.linear_part()
discretization.boundaries(atom)
A = discretization.assemble_jacobian(atom)
B = read_matrix('ldc_bnd_%sx%sx%s.txt' % (nx, ny, nz))
for i in range(n):
print(i)
print('Expected:')
print(B.jcoA[B.begA[i]:B.begA[i + 1]])
print(B.coA[B.begA[i]:B.begA[i + 1]])
print('Got:')
print(A.jcoA[A.begA[i]:A.begA[i + 1]])
print(A.coA[A.begA[i]:A.begA[i + 1]])
assert B.begA[i + 1] - B.begA[i] == A.begA[i + 1] - A.begA[i]
for j in range(B.begA[i], B.begA[i + 1]):
assert B.jcoA[j] == A.jcoA[j]
assert B.coA[j] == A.coA[j]
def test_bous_bnd():
nx = 4
ny = nx
nz = nx
dim = 3
dof = 5
parameters = {
'Reynolds Number': 1,
'Rayleigh Number': 100,
'Prandtl Number': 100,
'Problem Type': 'Rayleigh-Benard'
}
n = nx * ny * nz * dof
discretization = Discretization(parameters, nx, ny, nz, dim, dof)
atom = discretization.linear_part()
discretization.boundaries(atom)
A = discretization.assemble_jacobian(atom)
B = read_bous_matrix('bous_bnd_%sx%sx%s.txt' % (nx, ny, nz))
pytest.skip('The Prandtl number is currently applied in a different place')
for i in range(n):
print(i)
print('Expected:')
print(B.jcoA[B.begA[i]:B.begA[i + 1]])
print(B.coA[B.begA[i]:B.begA[i + 1]])
print('Got:')
print(A.jcoA[A.begA[i]:A.begA[i + 1]])
print(A.coA[A.begA[i]:A.begA[i + 1]])
assert B.begA[i + 1] - B.begA[i] == A.begA[i + 1] - A.begA[i]
for j in range(B.begA[i], B.begA[i + 1]):
assert B.jcoA[j] == A.jcoA[j]
assert B.coA[j] == pytest.approx(A.coA[j])
