theo_unlen = cmblens.theory.load_theory_cls('cosmo2017_10K_acc3_unlensed_cmb', unit=unit)
L    = theo_unlen['l']
clkk = theo_unlen['clkk']
clpp = theo_unlen['clpp']

def get_delensed_amanda(tt_unlen, te_unlen, ee_unlen, lpp_theo, clpp_theo, L, N_L, klmin, klmax, load_dls=True, unit='uk', lmax_ret=8000):
    import camb.correlations
    import numpy as np

    kappa_lmin = int(klmin)
    kappa_lmax = int(klmax)

    reload(np)  # necessary to call this function multiple times (?)

    # want theory to start at l = 0
    lpp_th = np.zeros(len(lpp_theo)+int(lpp_theo[0]))
    lpp_th[0] = 0.
    lpp_th[1] = 1.
    lpp_th[int(lpp_theo[0]):] = np.array(lpp_theo)
    # fill in unlensed theory and lensing potential
    clpp_th = np.zeros(len(lpp_th))
    tt_th = np.zeros(len(lpp_th))
    te_th = np.zeros(len(lpp_th))
    ee_th = np.zeros(len(lpp_th))
    idx = int(lpp_theo[0])
    clpp_th[idx:] = np.array(clpp_theo)
    tt_th[idx:] = np.array(tt_unlen)
    te_th[idx:] = np.array(te_unlen)
    ee_th[idx:] = np.array(ee_unlen)
    # fill in the noise curve so it also starts at L=0 and is same size as theory
    NL = np.zeros(lpp_th.shape)
    idx = int(L[0])
    NL[:idx] = np.inf
    if len(N_L) >= len(NL) + idx:
        NL[idx:] = np.array(N_L[:len(NL)+idx])
    else:
        NL[idx:len(N_L)+idx] = np.array(N_L)
        NL[len(N_L)+idx:] = np.inf
    L = lpp_th.copy()

    def _weiner1d_filter(signal, noise):
        return (signal/(signal+noise))*signal

    # get residual lensing potential
    clpp_filt      = _weiner1d_filter(clpp_th, NL)
    loc            = np.where(L > kappa_lmax)
    if len(loc[0]) > 0:
        if loc[0][0] > len(clpp_filt):
            clpp_filt[loc[0][0]:] = 0.
    loc            = np.where(L < kappa_lmin)
    clpp_filt[loc] = 0.

    clpp_res = clpp_th-clpp_filt

    # create array to hold delensed spectra
    dls = np.zeros((len(lpp_th),4))
    dls[:,0] = tt_th
    dls[:,1] = ee_th
    dls[:,2] = np.zeros(len(tt_th))
    dls[:,3] = te_th

    reload(camb)
    reload(camb.correlations)
    camb_ret = camb.correlations.lensed_cls(dls, clpp_res, delta_cls=False, sampling_factor=2.)

    ret         = {}
    ret['dltt'] = camb_ret[:,0]
    ret['dlee'] = camb_ret[:,1]
    ret['dlbb'] = camb_ret[:,2]
    ret['dlte'] = camb_ret[:,3]

    return ret['dltt'][1:], ret['dlte'][1:], ret['dlee'][1:]

L_n, cln0_mv    = cln0['l'], cln0[PCOMB.MV].copy()
amanda_dl = get_delensed_amanda(theo_unlen['dltt'],theo_unlen['dlte'],theo_unlen['dlee'],L, clpp,L, cln0_mv, kappa_lmin, kappa_lmax)