def with_(self, **kwargs):
assert 'vector_operators' not in kwargs or not kwargs['vector_operators']
if 'operators' in kwargs and 'functionals' in kwargs:
operators = kwargs.pop('operators')
functionals = kwargs.pop('functionals')
assert set(operators.keys()) == {'operator'}
assert set(functionals.keys()) == {'rhs'}
operator = operators['operator']
rhs = functionals['rhs']
d = StationaryDiscretization(operator=operator, rhs=rhs, parameter_space=self.parameter_space)
return d.with_(**kwargs)
elif 'cache_region' in kwargs:
d = type(self)(self._impl)
d.unlock()
d.enable_caching(kwargs.pop('cache_region'))
d.lock()
return d.with_(**kwargs)
else:
d = type(self)(self._impl)
d.unlock()
for attr in ('solver_options', 'parameter_space'):
if attr in dir(self):
setattr(d, attr, getattr(self, attr))
if 'parameter_space' in kwargs:
d.parameter_space = kwargs.pop('parameter_space')
assert len(kwargs) == 0
d.lock()
return d
def with_(self, **kwargs):
assert 'operators' and 'functionals' in kwargs or kwargs.keys() == ['parameter_space']
assert 'vector_operators' not in kwargs or not kwargs['vector_operators']
if 'operators' in kwargs:
operators = kwargs.pop('operators')
functionals = kwargs.pop('functionals')
assert set(operators.keys()) == {'operator'}
assert set(functionals.keys()) == {'rhs'}
operator = operators['operator']
rhs = functionals['rhs']
assert all(op.source.type == NumpyVectorArray or op.source.type == BlockVectorArray for op in (operator, rhs))
assert all(op.range.type == NumpyVectorArray or op.range.type == BlockVectorArray for op in (operator, rhs))
d = StationaryDiscretization(operator=operator, rhs=rhs)
return d.with_(**kwargs)
else:
d = type(self)(self._impl)
d.unlock()
d.parameter_space = kwargs['parameter_space']
d.lock()
return d