sandwich_operator.py 2.62 KB
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# 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) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

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import numpy as np

from .diagonal_operator import DiagonalOperator
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from .endomorphic_operator import EndomorphicOperator
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from .scaling_operator import ScalingOperator
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class SandwichOperator(EndomorphicOperator):
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    """Operator which is equivalent to the expression `bun.adjoint*cheese*bun`.
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    """

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    def __init__(self, bun, cheese, op, _callingfrommake=False):
        if not _callingfrommake:
            raise NotImplementedError
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        super(SandwichOperator, self).__init__()
        self._bun = bun
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        self._cheese = cheese
        self._op = op

    @staticmethod
    def make(bun, cheese=None):
        """Build a SandwichOperator (or something simpler if possible)

        Parameters
        ----------
        bun: LinearOperator
            the bun part
        cheese: EndomorphicOperator
            the cheese part
        """
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        if cheese is None:
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            cheese = ScalingOperator(1., bun.target)
            op = bun.adjoint*bun
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        else:
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            op = bun.adjoint*cheese*bun

        # if our sandwich is diagonal, we can return immediately
        if isinstance(op, (ScalingOperator, DiagonalOperator)):
            return op
        return SandwichOperator(bun, cheese, op, _callingfrommake=True)
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    @property
    def domain(self):
        return self._op.domain

    @property
    def capability(self):
        return self._op.capability

    def apply(self, x, mode):
        return self._op.apply(x, mode)

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    def draw_sample(self, from_inverse=False, dtype=np.float64):
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        # Inverse samples from general sandwiches is not possible
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        if from_inverse:
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            raise NotImplementedError(
                "cannot draw from inverse of this operator")
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        # Samples from general sandwiches
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        return self._bun.adjoint_times(
            self._cheese.draw_sample(from_inverse, dtype))