def METHOD_NAME(model):
    data = np.loadtxt("./benchmarks/data/haloprofile_%s_colossus.txt" % model)
    a = 1.0
    concentration = 5
    halomass = 6e13
    halomassdef = 200
    rmin = 0.01
    rmax = 100
    r = np.exp(
        np.log(rmin) +
        np.log(rmax/rmin) * np.arange(data.shape[0]) / (data.shape[0]-1))
    mdef = ccl.halos.MassDef(halomassdef, 'matter')
    c = ccl.halos.ConcentrationConstant(c=concentration, mass_def=mdef)
    if model == 'nfw':
        p = ccl.halos.HaloProfileNFW(mass_def=mdef, concentration=c,
                                     truncated=False)
        prof = p.real(COSMO, r, halomass, a)
    elif model == 'projected_nfw':
        p = ccl.halos.HaloProfileNFW(mass_def=mdef, concentration=c,
                                     truncated=False, projected_analytic=True)
        prof = p.projected(COSMO, r, halomass, a)
    elif model == 'einasto':
        mdef = ccl.halos.MassDef(halomassdef, 'matter')
        p = ccl.halos.HaloProfileEinasto(mass_def=mdef, concentration=c,
                                         truncated=False)
        prof = p.real(COSMO, r, halomass, a)
    elif model == 'hernquist':
        p = ccl.halos.HaloProfileHernquist(mass_def=mdef, concentration=c,
                                           truncated=False)
        prof = p.real(COSMO, r, halomass, a)
    tol = np.clip(np.abs(HALOPROFILE_TOLERANCE * data[:, 1]), 1e-12, np.inf)
    err = np.abs(prof - data[:, 1])
    assert np.all(err <= tol)