# Source code for nifty7.operators.inversion_enabler

```
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2020 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
from ..logger import logger
from ..minimization.conjugate_gradient import ConjugateGradient
from ..minimization.iteration_controllers import IterationController
from ..minimization.quadratic_energy import QuadraticEnergy
from ..sugar import full
from .endomorphic_operator import EndomorphicOperator
from .linear_operator import LinearOperator
[docs]class InversionEnabler(EndomorphicOperator):
"""Class which augments the capability of another operator object via
numerical inversion.
Parameters
----------
op : :class:`EndomorphicOperator`
The operator to be enhanced.
The InversionEnabler object will support the same operation modes as
`op`, and additionally the inverse set. The newly-added modes will
be computed by iterative inversion.
iteration_controller : :class:`IterationController`
The iteration controller to use for the iterative numerical inversion
done by a :class:`ConjugateGradient` object.
approximation : :class:`LinearOperator`, optional
if not None, this operator should be an approximation to `op`, which
supports the operation modes that `op` doesn't have. It is used as a
preconditioner during the iterative inversion, to accelerate
convergence.
"""
def __init__(self, op, iteration_controller, approximation=None):
# isinstance(op, EndomorphicOperator) does not suffice since op can be
# a ChainOperator
if not isinstance(op, LinearOperator):
raise TypeError('Operator needs to be linear.')
if op.domain is not op.target:
raise TypeError('Operator needs to be endomorphic.')
self._op = op
self._ic = iteration_controller
self._approximation = approximation
self._domain = op.domain
self._capability = self._addInverse[self._op.capability]
[docs] def apply(self, x, mode):
self._check_mode(mode)
if self._op.capability & mode:
return self._op.apply(x, mode)
x0 = full(x.domain, 0.)
invmode = self._modeTable[self.INVERSE_BIT][self._ilog[mode]]
invop = self._op._flip_modes(self._ilog[invmode])
prec = self._approximation
if prec is not None:
prec = prec._flip_modes(self._ilog[mode])
energy = QuadraticEnergy(x0, invop, x)
inverter = ConjugateGradient(self._ic)
r, stat = inverter(energy, preconditioner=prec)
if stat != IterationController.CONVERGED:
logger.warning("Error detected during operator inversion")
return r.position
[docs] def draw_sample_with_dtype(self, dtype, from_inverse=False):
return self._op.draw_sample_with_dtype(dtype, from_inverse)
```