Source code for cil.optimisation.operators.ZeroOperator
#  Copyright 2019 United Kingdom Research and Innovation
#  Copyright 2019 The University of Manchester
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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 ZeroOperator(LinearOperator):
    r'''ZeroOperator:  O: X -> Y,  maps any element of :math:`x\in X` into the zero element :math:`\in Y,  O(x) = O_{Y}`
        :param gm_domain: domain of the operator
        :param gm_range: range of the operator, default: same as domain
        Note:
        .. math::
                O^{*}: Y^{*} -> X^{*} \text{(Adjoint)}
                < O(x), y > = < x, O^{*}(y) >
     '''
    def __init__(self, domain_geometry, range_geometry=None):
        if range_geometry is None:
            range_geometry = domain_geometry.clone()
        super(ZeroOperator, self).__init__(domain_geometry=domain_geometry,
                                           range_geometry=range_geometry)
[docs]    def direct(self,x,out=None):
        '''Returns O(x)'''
        if out is None:
            return self.range_geometry().allocate(value=0)
        else:
            out.fill(self.range_geometry().allocate(value=0)) 
[docs]    def adjoint(self,x, out=None):
        '''Returns O^{*}(y)'''
        if out is None:
            return self.domain_geometry().allocate(value=0)
        else:
            out.fill(self.domain_geometry().allocate(value=0)) 
[docs]    def calculate_norm(self, **kwargs):
        '''Evaluates operator norm of ZeroOperator'''
        return 0