How to use the proximity.ProxPSF function in proximity

filter_norm = np.array([[norm(b) * c * np.ones(psf.shape[1:])
                            for b, c in zip(a,
                            kwargs['psf_wave_thresh_factor'])]
                            for a in filter_conv])

    noise_est = sigma_mad(psf)
    print(' - PSF noise est:', noise_est)

    kwargs['psf_reweight'] = cwbReweight(noise_est * filter_norm)
    if list(kwargs['psf_reweight'].weights.shape) != kwargs['dual_shape']:
        raise ValueError('The shape of the input weights must match the dual '
                         'shape.')

    from proximity import ProxPSF
    psf_prox_primal = ProxPSF(kwargs['grad_psf_op']._psf0)
    kwargs['psf_prox_dual'] = SparseThreshold(kwargs['linear_op'],
                                              kwargs['psf_reweight'].weights)

    psf_cost = (costObj([kwargs['grad_psf_op'], psf_prox_primal,
                kwargs['psf_prox_dual']],
                tolerance=kwargs['convergence'],
                cost_interval=kwargs['cost_window'],
                plot_output=kwargs['output'],
                verbose=not kwargs['quiet']))
    psf_cost._check_cost = lambda: False
    kwargs['cost_op']._check_cost = lambda: False

    kwargs['optimisation_psf'] = (Condat(kwargs['grad_psf_op'].delta_psf,
                                  np.zeros(kwargs['dual_shape']),
                                  kwargs['grad_psf_op'],
                                  psf_prox_primal,