wiener_filter_curvature.py 2.31 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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from ..operators.endomorphic_operator import EndomorphicOperator
from ..operators.inversion_enabler import InversionEnabler
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import numpy as np
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PEP8    
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class WienerFilterCurvature(EndomorphicOperator):
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    """The curvature of the WienerFilterEnergy.

    This operator implements the second derivative of the
    WienerFilterEnergy used in some minimization algorithms or
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    for error estimates of the posterior maps. It is the
    inverse of the propagator operator.
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    Parameters
    ----------
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    R : LinearOperator
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        The response operator of the Wiener filter measurement.
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    N : EndomorphicOperator
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        The noise covariance.
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    S : DiagonalOperator
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        The prior signal covariance
    inverter : Minimizer
        The minimizer to use during numerical inversion
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    """

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    def __init__(self, R, N, S, inverter):
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        super(WienerFilterCurvature, self).__init__()
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        self.R = R
        self.N = N
        self.S = S
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        op = R.adjoint*N.inverse*R + S.inverse
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        self._op = InversionEnabler(op, inverter, S.times)
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    @property
    def domain(self):
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        return self._op.domain
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    @property
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    def capability(self):
        return self._op.capability
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    def apply(self, x, mode):
        return self._op.apply(x, mode)
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    def draw_inverse_sample(self, dtype=np.float64):
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        n = self.N.draw_sample(dtype)
        s = self.S.draw_sample(dtype)
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        d = self.R(s) + n
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        j = self.R.adjoint_times(self.N.inverse_times(d))
        m = self.inverse_times(j)
        return s - m