Source code for cil.optimisation.operators.DiagonalOperator

#  Copyright 2020 United Kingdom Research and Innovation
#  Copyright 2020 The University of Manchester
#
#  Licensed under the Apache License, Version 2.0 (the "License");
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#      http://www.apache.org/licenses/LICENSE-2.0
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# Authors:
# CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt

import numpy as np
from cil.framework import ImageData
from cil.optimisation.operators import LinearOperator


[docs]
class DiagonalOperator(LinearOperator):

    r"""DiagonalOperator

    Performs an element-wise multiplication, i.e., `Hadamard Product <https://en.wikipedia.org/wiki/Hadamard_product_(matrices)#:~:text=In%20mathematics%2C%20the%20Hadamard%20product,elements%20i%2C%20j%20of%20the>`_
    of a :class:`DataContainer` `x` and :class:`DataContainer` `diagonal`, `d` .

    .. math:: (D\circ x) = \sum_{i,j}^{M,N} D_{i,j} x_{i, j}

    In matrix-vector interpretation, if `D` is a :math:`M\times N` dense matrix and is flattened, we have a :math:`M*N \times M*N` vector.
    A sparse diagonal matrix, i.e., :class:`DigaonalOperator` can be created if we add the vector above to the main diagonal.
    If the :class:`DataContainer` `x` is also flattened, we have a :math:`M*N` vector.
    Now, matrix-vector multiplcation is allowed and results to a :math:`(M*N,1)` vector. After reshaping we recover a :math:`M\times N` :class:`DataContainer`.

    Parameters
    ----------
    diagonal : DataContainer
        DataContainer with the same dimensions as the data to be operated on.
    domain_geometry : ImageGeometry
        Specifies the geometry of the operator domain. If 'None' will use the diagonal geometry directly. default=None .

    """
    def __init__(self, diagonal, domain_geometry=None):
        if domain_geometry is None:
            domain_geometry = diagonal.geometry.copy()
        super(DiagonalOperator, self).__init__(domain_geometry=domain_geometry,
                                    range_geometry=domain_geometry)
        self.diagonal = diagonal


[docs]
    def direct(self,x,out=None):
        "Returns :math:`D\circ x` "
        if out is None:
            return self.diagonal * x
        else:
            self.diagonal.multiply(x,out=out)
        return out



[docs]
    def adjoint(self,x, out=None):
        "Returns :math:`D^*\circ x` "
        return self.diagonal.conjugate().multiply(x,out=out)



[docs]
    def calculate_norm(self, **kwargs):
        r""" Returns the operator norm of DiagonalOperator which is the :math:`\infty` norm of `diagonal`

        .. math:: \|D\|_{\infty} = \max_{i}\{|D_{i}|\}
        """
        return self.diagonal.abs().max()
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Source code for cil.optimisation.operators.DiagonalOperator
