def create_conn_graph(self, verbose=True):
"""
Create a graph of mesh connectivity.
Returns
-------
graph : csr_matrix
The mesh connectivity graph as a SciPy CSR matrix.
"""
from extmods.fem import raw_graph
shape = (self.n_nod, self.n_nod)
output('graph shape:', shape, verbose=verbose)
if nm.prod(shape) == 0:
output('no graph (zero size)!', verbose=verbose)
return None
output('assembling mesh graph...', verbose=verbose)
tt = time.clock()
ret, prow, icol = raw_graph(int(shape[0]), int(shape[1]),
len(self.conns), self.conns, self.conns )
output('...done in %.2f s' % (time.clock() - tt), verbose=verbose)
nnz = prow[-1]
output('graph nonzeros: %d (%.2e%% fill)' \
% (nnz, float(nnz) / nm.prod(shape)))
data = nm.ones((nnz,), dtype=nm.bool)
graph = sp.csr_matrix((data, icol, prow), shape)
return graph
def create_matrix_graph( self, var_names = None, vvar_names = None ):
"""
Create tangent matrix graph. Order of dof connectivities is not
important here...
"""
def _prepare_dc_lists( adof_conns, var_names = None ):
if var_names is None:
var_names = adof_conns.iterkeys()
gdcs = {}
for var_name in var_names:
adcs = adof_conns[var_name]
for ig, dc in adcs.volume_d_cs.iteritems():
## print dc
gdcs.setdefault( ig, [] ).append( dc )
return gdcs
shape = (self.avdi.ptr[-1], self.adi.ptr[-1])
output( 'matrix shape:', shape )
if nm.prod( shape ) == 0:
output( 'no matrix!' )
return None
cgdcs = _prepare_dc_lists( self.adof_conns, var_names )
## print cgdcs
## pause()
if self.has_virtual_d_cs:
rgdcs = _prepare_dc_lists( self.avdof_conns, vvar_names )
else:
rgdcs = cgdcs
##
# Make all permutations per element group.
rdcs = []
cdcs = []
for ig in rgdcs.iterkeys():
rgdc, cgdc = rgdcs[ig], cgdcs[ig]
for perm in la.cycle( [len( rgdc ), len( cgdc )] ):
rdcs.append( rgdc[perm[0]] )
cdcs.append( cgdc[perm[1]] )
# print ' ', perm, '->', rdcs[-1].shape, cdcs[-1].shape
output( 'assembling matrix graph...' )
tt = time.clock()
# shape = nm.array( shape, dtype = nm.long )
ret, prow, icol = raw_graph( int( shape[0] ), int( shape[1] ),
len( rdcs ), rdcs, cdcs )
output( '...done in %.2f s' % (time.clock() - tt) )
nnz = prow[-1]
output( 'matrix structural nonzeros: %d (%.2e%% fill)' \
% (nnz, float( nnz ) / nm.prod( shape ) ) )
## print ret, prow, icol, nnz
data = nm.zeros( (nnz,), dtype = self.dtype )
matrix = sp.csr_matrix( (data, icol, prow), shape )
## matrix.save( 'matrix', format = '%d %d %e\n' )
## pause()
return matrix
def create_matrix_graph(self, any_dof_conn=False, rdcs=None, cdcs=None, shape=None):
"""
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.
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)
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...")
tt = time.clock()
ret, prow, icol = raw_graph(int(shape[0]), int(shape[1]), len(rdcs), rdcs, cdcs)
output("...done in %.2f s" % (time.clock() - tt))
nnz = prow[-1]
output("matrix structural nonzeros: %d (%.2e%% fill)" % (nnz, float(nnz) / nm.prod(shape)))
## print ret, prow, icol, nnz
data = nm.zeros((nnz,), dtype=self.variables.dtype)
matrix = sp.csr_matrix((data, icol, prow), shape)
## matrix.save( 'matrix', format = '%d %d %e\n' )
## pause()
return matrix