def create_matrix_graph(self,
any_dof_conn=False,
rdcs=None,
cdcs=None,
shape=None,
verbose=True):
"""
Create tangent matrix graph, i.e. preallocate and initialize the
sparse storage needed for the tangent matrix. Order of DOF
connectivities is not important.
Parameters
----------
any_dof_conn : bool
By default, only volume DOF connectivities are used, with
the exception of trace surface DOF connectivities. If True,
any kind of DOF connectivities is allowed.
rdcs, cdcs : arrays, optional
Additional row and column DOF connectivities, corresponding
to the variables used in the equations.
shape : tuple, optional
The required shape, if it is different from the shape
determined by the equations variables. This may be needed if
additional row and column DOF connectivities are passed in.
verbose : bool
If False, reduce verbosity.
Returns
-------
matrix : csr_matrix
The matrix graph in the form of a CSR matrix with
preallocated structure and zero data.
"""
if not self.variables.has_virtuals():
output('no matrix (no test variables)!')
return None
shape = get_default(shape, self.variables.get_matrix_shape())
output('matrix shape:', shape, verbose=verbose)
if nm.prod(shape) == 0:
output('no matrix (zero size)!')
return None
rdcs, cdcs = self.get_graph_conns(any_dof_conn=any_dof_conn,
rdcs=rdcs,
cdcs=cdcs)
if not len(rdcs):
output('no matrix (empty dof connectivities)!')
return None
output('assembling matrix graph...', verbose=verbose)
tt = time.clock()
nnz, prow, icol = create_mesh_graph(shape[0], shape[1], len(rdcs),
rdcs, cdcs)
output('...done in %.2f s' % (time.clock() - tt), verbose=verbose)
output('matrix structural nonzeros: %d (%.2e%% fill)' \
% (nnz, float(nnz) / nm.prod(shape)), verbose=verbose)
data = nm.zeros((nnz, ), dtype=self.variables.dtype)
matrix = sp.csr_matrix((data, icol, prow), shape)
return matrix
def create_matrix_graph(self, any_dof_conn=False, rdcs=None, cdcs=None,
shape=None, verbose=True):
"""
Create tangent matrix graph, i.e. preallocate and initialize the
sparse storage needed for the tangent matrix. Order of DOF
connectivities is not important.
Parameters
----------
any_dof_conn : bool
By default, only volume DOF connectivities are used, with
the exception of trace surface DOF connectivities. If True,
any kind of DOF connectivities is allowed.
rdcs, cdcs : arrays, optional
Additional row and column DOF connectivities, corresponding
to the variables used in the equations.
shape : tuple, optional
The required shape, if it is different from the shape
determined by the equations variables. This may be needed if
additional row and column DOF connectivities are passed in.
verbose : bool
If False, reduce verbosity.
Returns
-------
matrix : csr_matrix
The matrix graph in the form of a CSR matrix with
preallocated structure and zero data.
"""
if not self.variables.has_virtuals():
output('no matrix (no test variables)!')
return None
shape = get_default(shape, self.variables.get_matrix_shape())
output('matrix shape:', shape, verbose=verbose)
if nm.prod(shape) == 0:
output('no matrix (zero size)!')
return None
rdcs, cdcs = self.get_graph_conns(any_dof_conn=any_dof_conn,
rdcs=rdcs, cdcs=cdcs)
if not len(rdcs):
output('no matrix (empty dof connectivities)!')
return None
output('assembling matrix graph...', verbose=verbose)
tt = time.clock()
nnz, prow, icol = create_mesh_graph(shape[0], shape[1],
len(rdcs), rdcs, cdcs)
output('...done in %.2f s' % (time.clock() - tt), verbose=verbose)
output('matrix structural nonzeros: %d (%.2e%% fill)' \
% (nnz, float(nnz) / nm.prod(shape)), verbose=verbose)
data = nm.zeros((nnz,), dtype=self.variables.dtype)
matrix = sp.csr_matrix((data, icol, prow), shape)
return matrix
def surface_graph(surf_faces, n_nod):
nnz, prow, icol = create_mesh_graph(n_nod, n_nod, len(surf_faces),
surf_faces, surf_faces)
data = nm.empty((nnz, ), dtype=nm.int32)
data.fill(2)
return sp.csr_matrix((data, icol, prow), (n_nod, n_nod))
def surface_graph(surf_faces, n_nod):
nnz, prow, icol = create_mesh_graph(n_nod, n_nod, len(surf_faces),
surf_faces, surf_faces)
data = nm.empty((nnz,), dtype=nm.int32)
data.fill(2)
return sp.csr_matrix((data, icol, prow), (n_nod, n_nod))