wiener_filter_curvature.py 2.24 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 __future__ import absolute_import, division, print_function
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from ..compat import *
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from ..operators.inversion_enabler import InversionEnabler
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from ..operators.sampling_enabler import SamplingEnabler
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from ..operators.sandwich_operator import SandwichOperator
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PEP8  
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def WienerFilterCurvature(R, N, S, iteration_controller=None,
                          iteration_controller_sampling=None):
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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
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    iteration_controller : IterationController
        The iteration controller to use during numerical inversion via
        ConjugateGradient.
    iteration_controller_sampling : IterationController
        The iteration controller to use for sampling.
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    """
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    M = SandwichOperator.make(R, N.inverse)
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    if iteration_controller_sampling is not None:
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        op = SamplingEnabler(M, S.inverse, iteration_controller_sampling,
                             S.inverse)
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    else:
        op = M + S.inverse
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    return InversionEnabler(op, iteration_controller, S.inverse)