def _dict_to_di(name, dd):
di = DofInfo(name)
for key in keys:
val = dd[key]
di.append_raw(key, val)
return di
def make_global_lcbc_operator(lcbc_ops, adi, new_only=False):
"""
Assemble all LCBC operators into a single matrix.
Returns
-------
mtx_lc : csr_matrix
The global LCBC operator in the form of a CSR matrix.
lcdi : DofInfo
The global active LCBC-constrained DOF information.
new_only : bool
If True, the operator columns will contain only new DOFs.
"""
n_dof = adi.ptr[-1]
eq_lcbc = nm.zeros((n_dof,), dtype=nm.int32)
n_dof_new = 0
n_free = {}
n_new = {}
for var_name, lcbc_op in lcbc_ops.iteritems():
if lcbc_op is None: continue
indx = adi.indx[var_name]
eq_lcbc[indx] = lcbc_op.eq_lcbc
n_free[var_name] = len(nm.where(lcbc_op.eq_lcbc == 0)[0])
n_new[var_name] = nm.sum(lcbc_op.n_transformed_dof)
n_dof_new += n_new[var_name]
if n_dof_new == 0:
return None, None
ii = nm.nonzero(eq_lcbc)[0]
n_constrained = ii.shape[0]
n_dof_free = n_dof - n_constrained
n_dof_reduced = n_dof_free + n_dof_new
output('dofs: total %d, free %d, constrained %d, new %d'\
% (n_dof, n_dof_free, n_constrained, n_dof_new))
output(' -> reduced %d' % (n_dof_reduced))
lcdi = DofInfo('lcbc_active_state_dof_info')
fdi = DofInfo('free_dof_info')
ndi = DofInfo('new_dof_info')
for var_name in adi.var_names:
nf = n_free.get(var_name, adi.n_dof[var_name])
nn = n_new.get(var_name, 0)
fdi.append_raw(var_name, nf)
ndi.append_raw(var_name, nn)
lcdi.append_raw(var_name, nn + nf)
assert_(lcdi.ptr[-1] == n_dof_reduced)
rows = []
cols = []
data = []
for var_name, lcbc_op in lcbc_ops.iteritems():
if lcbc_op is None: continue
if new_only:
offset = ndi.indx[var_name].start
else:
offset = lcdi.indx[var_name].start + fdi.n_dof[var_name]
for ii, op in enumerate(lcbc_op):
indx = nm.where(eq_lcbc == lcbc_op.markers[ii])[0]
icols = nm.arange(offset + lcbc_op.ics[ii],
offset + lcbc_op.ics[ii+1])
if isinstance(op.mtx, sp.spmatrix):
lr, lc, lv = sp.find(op.mtx)
rows.append(indx[lr])
cols.append(icols[lc])
data.append(lv)
else:
irs, ics = nm.meshgrid(indx, icols)
rows.append(irs.ravel())
cols.append(ics.ravel())
data.append(op.mtx.T.ravel())
rows = nm.concatenate(rows)
cols = nm.concatenate(cols)
data = nm.concatenate(data)
if new_only:
mtx_lc = sp.coo_matrix((data, (rows, cols)),
shape=(n_dof, n_dof_new))
else:
mtx_lc = sp.coo_matrix((data, (rows, cols)),
shape=(n_dof, n_dof_reduced))
ir = nm.where(eq_lcbc == 0)[0]
ic = nm.empty((n_dof_free,), dtype=nm.int32)
for var_name in adi.var_names:
ii = nm.arange(fdi.n_dof[var_name], dtype=nm.int32)
ic[fdi.indx[var_name]] = lcdi.indx[var_name].start + ii
mtx_lc2 = sp.coo_matrix((nm.ones((ir.shape[0],)), (ir, ic)),
shape=(n_dof, n_dof_reduced), dtype=nm.float64)
mtx_lc = mtx_lc + mtx_lc2
mtx_lc = mtx_lc.tocsr()
return mtx_lc, lcdi
def _dict_to_di(name, dd):
di = DofInfo(name)
for key, val in six.iteritems(dd):
di.append_raw(key, val)
return di
def make_global_lcbc_operator(lcbc_ops, adi, new_only=False):
"""
Assemble all LCBC operators into a single matrix.
Returns
-------
mtx_lc : csr_matrix
The global LCBC operator in the form of a CSR matrix.
lcdi : DofInfo
The global active LCBC-constrained DOF information.
new_only : bool
If True, the operator columns will contain only new DOFs.
"""
n_dof = adi.ptr[-1]
eq_lcbc = nm.zeros((n_dof, ), dtype=nm.int32)
n_dof_new = 0
n_free = {}
n_new = {}
for var_name, lcbc_op in lcbc_ops.iteritems():
if lcbc_op is None: continue
indx = adi.indx[var_name]
eq_lcbc[indx] = lcbc_op.eq_lcbc
n_free[var_name] = len(nm.where(lcbc_op.eq_lcbc == 0)[0])
n_new[var_name] = nm.sum(lcbc_op.n_transformed_dof)
n_dof_new += n_new[var_name]
if n_dof_new == 0:
return None, None
ii = nm.nonzero(eq_lcbc)[0]
n_constrained = ii.shape[0]
n_dof_free = n_dof - n_constrained
n_dof_reduced = n_dof_free + n_dof_new
output('dofs: total %d, free %d, constrained %d, new %d'\
% (n_dof, n_dof_free, n_constrained, n_dof_new))
output(' -> reduced %d' % (n_dof_reduced))
lcdi = DofInfo('lcbc_active_state_dof_info')
fdi = DofInfo('free_dof_info')
ndi = DofInfo('new_dof_info')
for var_name in adi.var_names:
nf = n_free.get(var_name, adi.n_dof[var_name])
nn = n_new.get(var_name, 0)
fdi.append_raw(var_name, nf)
ndi.append_raw(var_name, nn)
lcdi.append_raw(var_name, nn + nf)
assert_(lcdi.ptr[-1] == n_dof_reduced)
rows = []
cols = []
data = []
for var_name, lcbc_op in lcbc_ops.iteritems():
if lcbc_op is None: continue
if new_only:
offset = ndi.indx[var_name].start
else:
offset = lcdi.indx[var_name].start + fdi.n_dof[var_name]
for ii, op in enumerate(lcbc_op):
indx = nm.where(eq_lcbc == lcbc_op.markers[ii])[0]
icols = nm.arange(offset + lcbc_op.ics[ii],
offset + lcbc_op.ics[ii + 1])
if isinstance(op.mtx, sp.spmatrix):
lr, lc, lv = sp.find(op.mtx)
rows.append(indx[lr])
cols.append(icols[lc])
data.append(lv)
else:
irs, ics = nm.meshgrid(indx, icols)
rows.append(irs.ravel())
cols.append(ics.ravel())
data.append(op.mtx.T.ravel())
rows = nm.concatenate(rows)
cols = nm.concatenate(cols)
data = nm.concatenate(data)
if new_only:
mtx_lc = sp.coo_matrix((data, (rows, cols)), shape=(n_dof, n_dof_new))
else:
mtx_lc = sp.coo_matrix((data, (rows, cols)),
shape=(n_dof, n_dof_reduced))
ir = nm.where(eq_lcbc == 0)[0]
ic = nm.empty((n_dof_free, ), dtype=nm.int32)
for var_name in adi.var_names:
ii = nm.arange(fdi.n_dof[var_name], dtype=nm.int32)
ic[fdi.indx[var_name]] = lcdi.indx[var_name].start + ii
mtx_lc2 = sp.coo_matrix((nm.ones((ir.shape[0], )), (ir, ic)),
shape=(n_dof, n_dof_reduced),
dtype=nm.float64)
mtx_lc = mtx_lc + mtx_lc2
mtx_lc = mtx_lc.tocsr()
return mtx_lc, lcdi
def _dict_to_di(name, dd):
di = DofInfo(name)
for key, val in dd.iteritems():
di.append_raw(key, val)
return di