def add_jacobian(self, name, surfs, ind_u, ind_v, ndim=1):
num = self._num
size = self._size
str_indices = self._str_indices
topo = self._topo
bspline = self._bspline
vec = self.vec
jac = self.jac
surfs = numpy.array(surfs, int) + 1
npt = surfs.shape[0]
nnz = BSElib.computesnnz(npt, num['surf'], num['group'],
topo['surf_group'], bspline['order'], surfs)
quant = ['', '_du', '_dv']
der_u = [0, 1, 0]
der_v = [0, 0, 1]
for ind in xrange(3):
data, rows, cols \
= BSElib.computesmtx(der_u[ind], der_v[ind],
nnz, npt,
num['surf'], num['group'],
str_indices['cp'], topo['surf_group'],
bspline['order'], bspline['num_cp'],
surfs, ind_u, ind_v)
qname = name + quant[ind]
jac['d(' + qname + ')/d(cp_str)'] \
= scipy.sparse.csr_matrix((data, (rows-1, cols-1)),
shape=(npt,
size['cp_str']))
vec[qname] = BSEvecUns(qname, npt, ndim, self.hidden)
def add_jacobian(self, name, surfs, ind_u, ind_v, ndim=1):
num = self._num
size = self._size
str_indices = self._str_indices
topo = self._topo
bspline = self._bspline
vec = self.vec
jac = self.jac
surfs = numpy.array(surfs, int) + 1
npt = surfs.shape[0]
nnz = BSElib.computesnnz(npt, num['surf'], num['group'],
topo['surf_group'], bspline['order'],
surfs)
quant = ['', '_du', '_dv']
der_u = [0, 1, 0]
der_v = [0, 0, 1]
for ind in xrange(3):
data, rows, cols \
= BSElib.computesmtx(der_u[ind], der_v[ind],
nnz, npt,
num['surf'], num['group'],
str_indices['cp'], topo['surf_group'],
bspline['order'], bspline['num_cp'],
surfs, ind_u, ind_v)
qname = name + quant[ind]
jac['d(' + qname + ')/d(cp_str)'] \
= scipy.sparse.csr_matrix((data, (rows-1, cols-1)),
shape=(npt,
size['cp_str']))
vec[qname] = BSEvecUns(qname, npt, ndim, self.hidden)
def add_jacobian(self, name, surfs, ind_u, ind_v, ndim=1):
num = self._num
size = self._size
str_indices = self._str_indices
topo = self._topo
bspline = self._bspline
vec = self.vec
jac = self.jac
surfs = numpy.array(surfs, int) + 1
npt = surfs.shape[0]
nnz = BSElib.computesnnz(npt, num['surf'], num['group'],
topo['surf_group'], bspline['order'],
surfs)
data, rows, cols \
= BSElib.computesmtx(0, 0, nnz, npt,
num['surf'], num['group'],
str_indices['cp'], topo['surf_group'],
bspline['order'], bspline['num_cp'],
surfs, ind_u, ind_v)
jac['d(' + name + ')/d(cp_str)'] \
= scipy.sparse.csr_matrix((data, (rows-1, cols-1)),
shape=(npt,
size['cp_str']))
vec[name] = BSEvecUns(name, npt, ndim)
