def apply_inverse(self, V, ind=None, mu=None, least_squares=False):
assert V in self.range
assert V.check_ind(ind)
assert not self.functional and not self.vector
if V.dim == 0:
if self.source.dim == 0 and least_squares:
return ListVectorArray(
[NumpyVector(np.zeros(0), copy=False) for _ in range(V.len_ind(ind))], subtype=self.source.subtype
)
else:
raise InversionError
options = (
self.solver_options.get("inverse") if self.solver_options else "least_squares" if least_squares else None
)
if options and not least_squares:
solver_type = options if isinstance(options, str) else options["type"]
if solver_type.startswith("least_squares"):
self.logger.warn('Least squares solver selected but "least_squares == False"')
if ind is None:
vectors = V._list
elif isinstance(ind, Number):
vectors = [V._list[ind]]
else:
vectors = (V._list[i] for i in ind)
try:
return ListVectorArray(
[
NumpyVector(
_apply_inverse(self._matrix, v._array.reshape((1, -1)), options=options).ravel(), copy=False
)
for v in vectors
],
subtype=self.source.subtype,
)
except InversionError as e:
if least_squares and options:
solver_type = options if isinstance(options, str) else options["type"]
if not solver_type.startswith("least_squares"):
msg = (
str(e)
+ "\nNote: linear solver was selected for solving least squares problem (maybe not invertible?)"
)
raise InversionError(msg)
raise e
def apply_inverse(self, V, ind=None, mu=None, least_squares=False):
assert V in self.range
assert V.check_ind(ind)
assert not self.functional and not self.vector
if V.dim == 0:
if self.source.dim == 0 and least_squares:
return ListVectorArray([NumpyVector(np.zeros(0), copy=False) for _ in range(V.len_ind(ind))],
subtype=self.source.subtype)
else:
raise InversionError
options = (self.solver_options.get('inverse') if self.solver_options else
'least_squares' if least_squares else
None)
if options and not least_squares:
solver_type = options if isinstance(options, str) else options['type']
if solver_type.startswith('least_squares'):
self.logger.warn('Least squares solver selected but "least_squares == False"')
if ind is None:
vectors = V._list
elif isinstance(ind, Number):
vectors = [V._list[ind]]
else:
vectors = (V._list[i] for i in ind)
try:
return ListVectorArray([NumpyVector(_apply_inverse(self._matrix, v._array.reshape((1, -1)),
options=options).ravel(),
copy=False)
for v in vectors],
subtype=self.source.subtype)
except InversionError as e:
if least_squares and options:
solver_type = options if isinstance(options, str) else options['type']
if not solver_type.startswith('least_squares'):
msg = str(e) \
+ '\nNote: linear solver was selected for solving least squares problem (maybe not invertible?)'
raise InversionError(msg)
raise e
def apply_inverse(self, U, ind=None, mu=None, options=None):
assert U in self.range
assert U.check_ind(ind)
assert not self.functional and not self.vector
if U.dim == 0:
if (self.source.dim == 0
or isinstance(options, str) and options.startswith('least_squares')
or isinstance(options, dict) and options['type'].startswith('least_squares')):
return ListVectorArray([NumpyVector(np.zeros(0), copy=False) for _ in range(U.len_ind(ind))],
subtype=self.source.subtype)
else:
raise InversionError
if ind is None:
vectors = U._list
elif isinstance(ind, Number):
vectors = [U._list[ind]]
else:
vectors = (U._list[i] for i in ind)
return ListVectorArray([NumpyVector(_apply_inverse(self._matrix, v._array, options=options), copy=False)
for v in vectors],
subtype=self.source.subtype)
