diagonal_operator.py 5.35 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 division
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import numpy as np
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from ..field import Field
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from ..domain_tuple import DomainTuple
from .endomorphic_operator import EndomorphicOperator
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from .. import utilities
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from .. import dobj
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class DiagonalOperator(EndomorphicOperator):
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    """ NIFTy class for diagonal operators.
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    The NIFTy DiagonalOperator class is a subclass derived from the
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    EndomorphicOperator. It multiplies an input field pixel-wise with its
    diagonal.
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    Parameters
    ----------
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    diagonal : Field
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        The diagonal entries of the operator.
    domain : Domain, tuple of Domain or DomainTuple, optional
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        The domain on which the Operator's input Field lives.
        If None, use the domain of "diagonal".
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    spaces : int or tuple of int, optional
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        The elements of "domain" on which the operator acts.
        If None, it acts on all elements.
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    """

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    def __init__(self, diagonal, domain=None, spaces=None):
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        super(DiagonalOperator, self).__init__()
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        if not isinstance(diagonal, Field):
            raise TypeError("Field object required")
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        if domain is None:
            self._domain = diagonal.domain
        else:
            self._domain = DomainTuple.make(domain)
        if spaces is None:
            self._spaces = None
            if diagonal.domain != self._domain:
                raise ValueError("domain mismatch")
        else:
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            self._spaces = utilities.parse_spaces(spaces, len(self._domain))
            if len(self._spaces) != len(diagonal.domain):
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                raise ValueError("spaces and domain must have the same length")
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            # if nspc==len(self.diagonal.domain),
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            # we could do some optimization
            for i, j in enumerate(self._spaces):
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                if diagonal.domain[i] != self._domain[j]:
                    raise ValueError("domain mismatch")
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            if self._spaces == tuple(range(len(self._domain))):
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                self._spaces = None  # shortcut

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        self._diagonal = diagonal.lock()
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        if self._spaces is not None:
            active_axes = []
            for space_index in self._spaces:
                active_axes += self._domain.axes[space_index]

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            if self._spaces[0] == 0:
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                self._ldiag = self._diagonal.local_data
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            else:
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                self._ldiag = self._diagonal.to_global_data()
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            locshape = dobj.local_shape(self._domain.shape, 0)
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            self._reshaper = [shp if i in active_axes else 1
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                              for i, shp in enumerate(locshape)]
            self._ldiag = self._ldiag.reshape(self._reshaper)

        else:
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            self._ldiag = self._diagonal.local_data
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    def apply(self, x, mode):
        self._check_input(x, mode)
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        if mode == self.TIMES:
            return Field(x.domain, val=x.val*self._ldiag)
        elif mode == self.ADJOINT_TIMES:
            if np.issubdtype(self._ldiag.dtype, np.floating):
                return Field(x.domain, val=x.val*self._ldiag)
            else:
                return Field(x.domain, val=x.val*self._ldiag.conj())
        elif mode == self.INVERSE_TIMES:
            return Field(x.domain, val=x.val/self._ldiag)
        else:
            if np.issubdtype(self._ldiag.dtype, np.floating):
                return Field(x.domain, val=x.val/self._ldiag)
            else:
                return Field(x.domain, val=x.val/self._ldiag.conj())
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    @property
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    def diagonal(self):
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        """ Returns the diagonal of the Operator."""
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        return self._diagonal
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    @property
    def domain(self):
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        return self._domain
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    @property
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    def capability(self):
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        return self._all_ops

    @property
    def inverse(self):
        return DiagonalOperator(1./self._diagonal, self._domain, self._spaces)

    @property
    def adjoint(self):
        return DiagonalOperator(self._diagonal.conjugate(), self._domain,
                                self._spaces)
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    def process_sample(self, sample):
        if np.issubdtype(self._ldiag.dtype, np.complexfloating):
            raise ValueError("cannot draw sample from complex-valued operator")

        res = Field.empty_like(sample)
        res.local_data[()] = sample.local_data * np.sqrt(self._ldiag)
        return res

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    def draw_sample(self, dtype=np.float64):
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        if np.issubdtype(self._ldiag.dtype, np.complexfloating):
            raise ValueError("cannot draw sample from complex-valued operator")
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        res = Field.from_random(random_type="normal", domain=self._domain,
                                dtype=dtype)
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        res.local_data[()] *= np.sqrt(self._ldiag)
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        return res