def test_issymmetric(self):
net = op.network.Cubic(shape=[5, 5, 5])
am = net.create_adjacency_matrix(triu=False)
assert topotools.issymmetric(am)
am = net.create_adjacency_matrix(triu=True)
assert not topotools.issymmetric(am)
am = am.T
assert not topotools.issymmetric(am)
# Now test non-coo AM's
am = net.create_adjacency_matrix(triu=False, fmt="lil")
assert topotools.issymmetric(am)
am = net.create_adjacency_matrix(triu=True, fmt="lil")
assert not topotools.issymmetric(am)
am = am.T
assert not topotools.issymmetric(am)
# Now test non-triangular AM
im = net.create_incidence_matrix()
assert not topotools.issymmetric(im)
def _solve(self, A=None, b=None):
r"""
Sends the A and b matrices to the specified solver, and solves for *x*
given the boundary conditions, and source terms based on the present
value of *x*. This method does NOT iterate to solve for non-linear
source terms or march time steps.
Parameters
----------
A : sparse matrix
The coefficient matrix in sparse format. If not specified, then
it uses the ``A`` matrix attached to the object.
b : ND-array
The RHS matrix in any format. If not specified, then it uses
the ``b`` matrix attached to the object.
Notes
-----
The solver used here is specified in the ``settings`` attribute of the
algorithm.
"""
if A is None:
A = self.A
if A is None:
raise Exception('The A matrix has not been built yet')
if b is None:
b = self.b
if b is None:
raise Exception('The b matrix has not been built yet')
if self.settings['solver'] == 'petsc':
# Check if petsc is available
petsc = importlib.util.find_spec('petsc4py')
if not petsc:
raise Exception('petsc is not installed')
if not self.settings['petsc_solver']:
self.settings['petsc_solver'] = 'cg'
if not self.settings['petsc_precondioner']:
self.settings['petsc_precondioner'] = 'jacobi'
if not self.settings['petsc_atol']:
self.settings['petsc_atol'] = 1e-06
if not self.settings['petsc_rtol']:
self.settings['petsc_rtol'] = 1e-06
if not self.settings['petsc_max_it']:
self.settings['petsc_max_it'] = 1000
# Define the petsc linear system converting the scipy objects
ls = sls(A=A.tocsr(), b=b)
ls.settings.update({
'solver':
self.settings['petsc_solver'],
'preconditioner':
self.settings['petsc_precondioner'],
'atol':
self.settings['petsc_atol'],
'rtol':
self.settings['petsc_rtol'],
'max_it':
self.settings['petsc_max_it']
})
x = sls.solve(ls)
del (ls) # Clean
else:
A = A.tocsr()
A.indices = A.indices.astype(np.int64)
A.indptr = A.indptr.astype(np.int64)
solver = getattr(sprs.linalg, self.settings['solver'])
if 'tol' in inspect.getfullargspec(solver)[0]:
# If an iterative solver is used, set tol
norm_A = sprs.linalg.norm(self._A)
norm_b = np.linalg.norm(self._b)
tol = min(norm_A, norm_b) * 1e-06
x = solver(A=A, b=b, tol=tol)
else:
sym = issymmetric(A)
if (sym and self.settings['solver'] == 'spsolve_triangular'):
solver = getattr(sprs.linalg, self.settings['solver'])
x = solver(A=sprs.tril(A), b=b)
else:
x = solver(A=A, b=b)
if type(x) == tuple:
x = x[0]
return x