def xstep(self):
        """The xstep of the baseline consensus class from which this
        class is derived is re-used to implement the xstep of the
        modified algorithm by replacing ``self.ZSf``, which is constant
        in the baseline algorithm, with a quantity derived from the
        additional variables ``self.Y1`` and ``self.U1``. It is also
        necessary to set the penalty parameter to unity for the duration
        of the x step.
        """

        self.YU1[:] = self.Y1 - self.U1
        self.ZSf = np.conj(self.Zf) * (self.Sf + sl.rfftn(
            self.YU1, None, self.cri.axisN))
        rho = self.rho
        self.rho = 1.0
        super(ConvCnstrMODMaskDcpl_Consensus, self).xstep()
        self.rho = rho
def xstep ( self ) : self . YU1 [ : ] = self . Y1 - self . U1 self . ZSf = np . conj ( self . Zf ) * ( self . Sf + sl . rfftn ( self . YU1 , None , self . cri . axisN ) ) rho = self . rho self . rho = 1.0 super ( ConvCnstrMODMaskDcpl_Consensus , self ) . xstep ( ) self . rho = rho
The xstep of the baseline consensus class from which this
        class is derived is re-used to implement the xstep of the
        modified algorithm by replacing ``self.ZSf``, which is constant
        in the baseline algorithm, with a quantity derived from the
        additional variables ``self.Y1`` and ``self.U1``. It is also
        necessary to set the penalty parameter to unity for the duration
        of the x step.