endomorphic_operator.py 2.06 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/>.
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#
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# Copyright(C) 2013-2018 Max-Planck-Society
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#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
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from __future__ import absolute_import, division, print_function
from ..compat import *
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from .linear_operator import LinearOperator
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import numpy as np
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class EndomorphicOperator(LinearOperator):
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    """ NIFTy class for endomorphic operators.
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    The  NIFTy EndomorphicOperator class is a class derived from the
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    LinearOperator. By definition, domain and target are the same in
    EndomorphicOperator.
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    """

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    @property
    def target(self):
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        """DomainTuple : returns :attr:`domain`

        Returns `self.domain`, because this is also the target domain
        for endomorphic operators."""
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        return self.domain
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    def draw_sample(self, from_inverse=False, dtype=np.float64):
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        """Generate a zero-mean sample

        Generates a sample from a Gaussian distribution with zero mean and
        covariance given by the operator.

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        Parameters
        ----------
        from_inverse : bool (default : False)
            if True, the sample is drawn from the inverse of the operator
        dtype : numpy datatype (default : numpy.float64)
            the data type to be used for the sample

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        Returns
        -------
        Field
            A sample from the Gaussian of given covariance.
        """
        raise NotImplementedError