def get_lensed_cls(theory,ells,clkk,lmax):
import camb.correlations as corr
ellrange = np.arange(0,lmax+2000,1)
mulfact = ellrange*(ellrange+1.)/2./np.pi
ucltt = theory.uCl('TT',ellrange)*mulfact
uclee = theory.uCl('EE',ellrange)*mulfact
uclbb = theory.uCl('BB',ellrange)*mulfact
uclte = theory.uCl('TE',ellrange)*mulfact
from scipy.interpolate import interp1d
clkkfunc = interp1d(ells,clkk)
clpp = clkkfunc(ellrange)*4./2./np.pi
cmbarr = np.vstack((ucltt,uclee,uclbb,uclte)).T
#print "Calculating lensed cls..."
lcls = corr.lensed_cls(cmbarr,clpp)
lmax = lmax+2000
cellrange = ellrange[:lmax].reshape((ellrange[:lmax].size,1)) #cellrange.ravel()[:lmax]
lclall = lcls[:lmax,:]
with np.errstate(divide='ignore', invalid='ignore'):
lclall = np.nan_to_num(lclall/cellrange/(cellrange+1.)*2.*np.pi)
cellrange = cellrange.ravel()
#clcltt = lcls[:lmax,0]
#clcltt = np.nan_to_num(clcltt/cellrange/(cellrange+1.)*2.*np.pi)
#print clcltt
lpad = lmax
dtheory = TheorySpectra()
with np.errstate(divide='ignore', invalid='ignore'):
mult = np.nan_to_num(1./mulfact)
ucltt *= mult
uclee *= mult
uclte *= mult
uclbb *= mult
#print cellrange.shape
#print ucltt.shape
dtheory.loadCls(cellrange,ucltt[:lmax],'TT',lensed=False,interporder="linear",lpad=lpad)
dtheory.loadCls(cellrange,uclte[:lmax],'TE',lensed=False,interporder="linear",lpad=lpad)
dtheory.loadCls(cellrange,uclee[:lmax],'EE',lensed=False,interporder="linear",lpad=lpad)
dtheory.loadCls(cellrange,uclbb[:lmax],'BB',lensed=False,interporder="linear",lpad=lpad)
dtheory.loadGenericCls(ells,clkk,"kk",lpad=lpad)
lcltt = lclall[:,0]
lclee = lclall[:,1]
lclbb = lclall[:,2]
lclte = lclall[:,3]
#lcltt *= mult
#lclee *= mult
#lclte *= mult
#lclbb *= mult
dtheory.loadCls(cellrange,lcltt,'TT',lensed=True,interporder="linear",lpad=lpad)
dtheory.loadCls(cellrange,lclte,'TE',lensed=True,interporder="linear",lpad=lpad)
dtheory.loadCls(cellrange,lclee,'EE',lensed=True,interporder="linear",lpad=lpad)
dtheory.loadCls(cellrange,lclbb,'BB',lensed=True,interporder="linear",lpad=lpad)
return dtheory
def get_N0_iter(qe_key, nlev_t, nlev_p, beam_fwhm, cls_unl, lmin_ivf, lmax_ivf, itermax, lmax_qlm=None):
"""Iterative lensing-N0 estimate
Calculates iteratively partially lensed spectra and lensing noise levels.
This uses the python camb package to get the partially lensed spectra.
This makes no assumption on response = 1 / noise hence is about twice as slow as it could be in standard cases.
Args:
qe_key: QE estimator key
nlev_t: temperature noise level (in :math:`\mu `K-arcmin)
nlev_p: polarisation noise level (in :math:`\mu `K-arcmin)
beam_fwhm: Gaussian beam full width half maximum in arcmin
cls_unl(dict): unlensed CMB power spectra
lmin_ivf: minimal CMB multipole used in the QE
lmax_ivf: maximal CMB multipole used in the QE
itermax: number of iterations to perform
lmax_qlm(optional): maximum lensing multipole to consider. Defaults to :math:`2 lmax_ivf`
Returns
Array of shape (itermax + 1, lmax_qlm + 1) with all iterated N0s. First entry is standard N0.
#FIXME: this is requiring the full camb python package for the lensed spectra calc.
"""
assert qe_key in ['p_p', 'p', 'ptt'], qe_key
try:
from camb.correlations import lensed_cls
except ImportError:
assert 0, "could not import camb.correlations.lensed_cls"
def cls2dls(cls):
"""Turns cls dict. into camb cl array format"""
keys = ['tt', 'ee', 'bb', 'te']
lmax = np.max([len(cl) for cl in cls.values()]) - 1
dls = np.zeros((lmax + 1, 4), dtype=float)
refac = np.arange(lmax + 1) * np.arange(1, lmax + 2, dtype=float) / (2. * np.pi)
for i, k in enumerate(keys):
cl = cls.get(k, np.zeros(lmax + 1, dtype=float))
sli = slice(0, min(len(cl), lmax + 1))
dls[sli, i] = cl[sli] * refac[sli]
cldd = np.copy(cls.get('pp', None))
if cldd is not None:
cldd *= np.arange(len(cldd)) ** 2 * np.arange(1, len(cldd) + 1, dtype=float) ** 2 / (2. * np.pi)
return dls, cldd
def dls2cls(dls):
"""Inverse operation to cls2dls"""
assert dls.shape[1] == 4
lmax = dls.shape[0] - 1
cls = {}
refac = 2. * np.pi * utils.cli( np.arange(lmax + 1) * np.arange(1, lmax + 2, dtype=float))
for i, k in enumerate(['tt', 'ee', 'bb', 'te']):
cls[k] = dls[:, i] * refac
return cls
if lmax_qlm is None:
lmax_qlm = 2 * lmax_ivf
lmax_qlm = min(lmax_qlm, 2 * lmax_ivf)
lmin_ivf = max(lmin_ivf, 1)
transfi2 = utils.cli(hp.gauss_beam(beam_fwhm / 180. / 60. * np.pi, lmax=lmax_ivf)) ** 2
llp2 = np.arange(lmax_qlm + 1, dtype=float) ** 2 * np.arange(1, lmax_qlm + 2, dtype=float) ** 2 / (2. * np.pi)
N0s = []
N0 = np.inf
for irr, it in utils.enumerate_progress(range(itermax + 1)):
dls_unl, cldd = cls2dls(cls_unl)
clwf = 0. if it == 0 else cldd[:lmax_qlm + 1] * utils.cli(cldd[:lmax_qlm + 1] + llp2 * N0[:lmax_qlm + 1])
cldd[:lmax_qlm + 1] *= (1. - clwf)
cls_plen = dls2cls(lensed_cls(dls_unl, cldd))
cls_ivfs = {}
if qe_key in ['ptt', 'p_p', 'p']:
cls_ivfs['tt'] = cls_plen['tt'][:lmax_ivf + 1] + (nlev_t * np.pi / 180. / 60.) ** 2 * transfi2
if qe_key in ['p_p', 'p']:
cls_ivfs['ee'] = cls_plen['ee'][:lmax_ivf + 1] + (nlev_p * np.pi / 180. / 60.) ** 2 * transfi2
cls_ivfs['bb'] = cls_plen['bb'][:lmax_ivf + 1] + (nlev_p * np.pi / 180. / 60.) ** 2 * transfi2
if qe_key in ['p']:
cls_ivfs['te'] = np.copy(cls_plen['te'][:lmax_ivf + 1])
cls_ivfs = utils.cl_inverse(cls_ivfs)
for cl in cls_ivfs.values():
cl[:lmin_ivf] *= 0.
fal = cls_ivfs
n_gg = get_nhl(qe_key, qe_key, cls_plen, cls_ivfs, lmax_ivf, lmax_ivf, lmax_out=lmax_qlm)[0]
r_gg = qresp.get_response(qe_key, lmax_ivf, 'p', cls_plen, cls_plen, fal, lmax_qlm=lmax_qlm)[0]
N0 = n_gg * utils.cli(r_gg ** 2)
N0s.append(N0)
return np.array(N0s)
def testPowers(self):
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.07, omk=0)
pars.set_dark_energy() # re-set defaults
pars.InitPower.set_params(ns=0.965, As=2e-9)
self.assertAlmostEqual(pars.scalar_power(1), 1.801e-9, 4)
self.assertAlmostEqual(pars.scalar_power([1, 1.5])[0], 1.801e-9, 4)
pars.set_matter_power(nonlinear=True)
self.assertEqual(pars.NonLinear, model.NonLinear_pk)
pars.set_matter_power(redshifts=[0., 0.17, 3.1], nonlinear=False)
data = camb.get_results(pars)
kh, z, pk = data.get_matter_power_spectrum(1e-4, 1, 20)
kh2, z2, pk2 = data.get_linear_matter_power_spectrum()
s8 = data.get_sigma8()
self.assertAlmostEqual(s8[0], 0.24686, 3)
self.assertAlmostEqual(s8[2], 0.80044, 3)
pars.NonLinear = model.NonLinear_both
data.calc_power_spectra(pars)
kh3, z3, pk3 = data.get_matter_power_spectrum(1e-4, 1, 20)
self.assertAlmostEqual(pk[-1][-3], 51.909, 2)
self.assertAlmostEqual(pk3[-1][-3], 57.697, 2)
self.assertAlmostEqual(pk2[-2][-4], 53.47, 2)
camb.set_feedback_level(0)
PKnonlin = camb.get_matter_power_interpolator(pars, nonlinear=True)
pars.set_matter_power(redshifts=[0, 0.09, 0.15, 0.42, 0.76, 1.5, 2.3, 5.5, 8.9], kmax=10, k_per_logint=5)
pars.NonLinear = model.NonLinear_both
results = camb.get_results(pars)
kh, z, pk = results.get_nonlinear_matter_power_spectrum()
pk_interp = PKnonlin.P(z, kh)
self.assertTrue(np.sum((pk / pk_interp - 1) ** 2) < 0.005)
camb.set_halofit_version('mead')
_, _, pk = results.get_nonlinear_matter_power_spectrum(params=pars, var1='delta_cdm', var2='delta_cdm')
self.assertAlmostEqual(pk[0][160], 824.6, delta=0.5)
lmax = 4000
pars.set_for_lmax(lmax)
cls = data.get_cmb_power_spectra(pars)
data.get_total_cls(2000)
data.get_unlensed_scalar_cls(2500)
data.get_tensor_cls(2000)
cls_lensed = data.get_lensed_scalar_cls(3000)
data.get_lens_potential_cls(2000)
# check lensed CL against python; will only agree well for high lmax as python has no extrapolation template
cls_lensed2 = correlations.lensed_cls(cls['unlensed_scalar'], cls['lens_potential'][:, 0], delta_cls=False)
self.assertTrue(np.all(np.abs(cls_lensed2[2:2000, 2] / cls_lensed[2:2000, 2] - 1) < 1e-3))
self.assertTrue(np.all(np.abs(cls_lensed2[2:3000, 0] / cls_lensed[2:3000, 0] - 1) < 1e-3))
self.assertTrue(np.all(np.abs(cls_lensed2[2:3000, 1] / cls_lensed[2:3000, 1] - 1) < 1e-3))
self.assertTrue(np.all(np.abs((cls_lensed2[2:3000, 3] - cls_lensed[2:3000, 3]) /
np.sqrt(cls_lensed2[2:3000, 0] * cls_lensed2[2:3000, 1])) < 1e-4))
corr, xvals, weights = correlations.gauss_legendre_correlation(cls['lensed_scalar'])
clout = correlations.corr2cl(corr, xvals, weights, 2500)
self.assertTrue(np.all(np.abs(clout[2:2300, 2] / cls['lensed_scalar'][2:2300, 2] - 1) < 1e-3))
def testPowers(self):
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.07, omk=0)
pars.set_dark_energy() # re-set defaults
pars.InitPower.set_params(ns=0.965, As=2e-9)
pars.NonLinearModel.set_params(halofit_version='takahashi')
self.assertAlmostEqual(pars.scalar_power(1), 1.801e-9, 4)
self.assertAlmostEqual(pars.scalar_power([1, 1.5])[0], 1.801e-9, 4)
pars.set_matter_power(nonlinear=True)
self.assertEqual(pars.NonLinear, model.NonLinear_pk)
pars.set_matter_power(redshifts=[0., 0.17, 3.1], silent=True, nonlinear=False)
data = camb.get_results(pars)
kh, z, pk = data.get_matter_power_spectrum(1e-4, 1, 20)
kh2, z2, pk2 = data.get_linear_matter_power_spectrum()
s8 = data.get_sigma8()
self.assertAlmostEqual(s8[0], 0.24686, 3)
self.assertAlmostEqual(s8[2], 0.80044, 3)
fs8 = data.get_fsigma8()
self.assertAlmostEqual(fs8[0], 0.2431, 3)
self.assertAlmostEqual(fs8[2], 0.424712, 3)
pars.NonLinear = model.NonLinear_both
data.calc_power_spectra(pars)
kh3, z3, pk3 = data.get_matter_power_spectrum(1e-4, 1, 20)
self.assertAlmostEqual(pk[-1][-3], 51.909, 2)
self.assertAlmostEqual(pk3[-1][-3], 57.709, 2)
self.assertAlmostEqual(pk2[-2][-4], 56.436, 2)
camb.set_feedback_level(0)
PKnonlin = camb.get_matter_power_interpolator(pars, nonlinear=True)
pars.set_matter_power(redshifts=[0, 0.09, 0.15, 0.42, 0.76, 1.5, 2.3, 5.5, 8.9],
silent=True, kmax=10, k_per_logint=5)
pars.NonLinear = model.NonLinear_both
results = camb.get_results(pars)
kh, z, pk = results.get_nonlinear_matter_power_spectrum()
pk_interp = PKnonlin.P(z, kh)
self.assertTrue(np.sum((pk / pk_interp - 1) ** 2) < 0.005)
PKnonlin2 = results.get_matter_power_interpolator(nonlinear=True, extrap_kmax=500)
pk_interp2 = PKnonlin2.P(z, kh)
self.assertTrue(np.sum((pk_interp / pk_interp2 - 1) ** 2) < 0.005)
pars.NonLinearModel.set_params(halofit_version='mead')
_, _, pk = results.get_nonlinear_matter_power_spectrum(params=pars, var1='delta_cdm', var2='delta_cdm')
self.assertAlmostEqual(pk[0][160], 824.6, delta=0.5)
lmax = 4000
pars.set_for_lmax(lmax)
cls = data.get_cmb_power_spectra(pars)
data.get_total_cls(2000)
data.get_unlensed_scalar_cls(2500)
data.get_tensor_cls(2000)
cls_lensed = data.get_lensed_scalar_cls(3000)
cphi = data.get_lens_potential_cls(2000)
# check lensed CL against python; will only agree well for high lmax as python has no extrapolation template
cls_lensed2 = correlations.lensed_cls(cls['unlensed_scalar'], cls['lens_potential'][:, 0], delta_cls=False)
np.testing.assert_allclose(cls_lensed2[2:2000, 2], cls_lensed[2:2000, 2], rtol=1e-3)
np.testing.assert_allclose(cls_lensed2[2:2000, 1], cls_lensed[2:2000, 1], rtol=1e-3)
np.testing.assert_allclose(cls_lensed2[2:2000, 0], cls_lensed[2:2000, 0], rtol=1e-3)
self.assertTrue(np.all(np.abs((cls_lensed2[2:3000, 3] - cls_lensed[2:3000, 3]) /
np.sqrt(cls_lensed2[2:3000, 0] * cls_lensed2[2:3000, 1])) < 1e-4))
corr, xvals, weights = correlations.gauss_legendre_correlation(cls['lensed_scalar'])
clout = correlations.corr2cl(corr, xvals, weights, 2500)
self.assertTrue(np.all(np.abs(clout[2:2300, 2] / cls['lensed_scalar'][2:2300, 2] - 1) < 1e-3))
pars = camb.CAMBparams()
pars.set_cosmology(H0=78, YHe=0.22)
pars.set_for_lmax(2000, lens_potential_accuracy=1)
pars.WantTensors = True
results = camb.get_transfer_functions(pars)
from camb import initialpower
cls = []
for r in [0, 0.2, 0.4]:
inflation_params = initialpower.InitialPowerLaw()
inflation_params.set_params(ns=0.96, r=r, nt=0)
results.power_spectra_from_transfer(inflation_params, silent=True)
cls += [results.get_total_cls(CMB_unit='muK')]
self.assertTrue(np.allclose((cls[1] - cls[0])[2:300, 2] * 2, (cls[2] - cls[0])[2:300, 2], rtol=1e-3))
# Check generating tensors and scalars together
pars = camb.CAMBparams()
pars.set_cosmology(H0=67)
lmax = 2000
pars.set_for_lmax(lmax, lens_potential_accuracy=1)
pars.InitPower.set_params(ns=0.96, r=0)
pars.WantTensors = False
results = camb.get_results(pars)
cl1 = results.get_total_cls(lmax, CMB_unit='muK')
pars.InitPower.set_params(ns=0.96, r=0.1, nt=0)
pars.WantTensors = True
results = camb.get_results(pars)
cl2 = results.get_lensed_scalar_cls(lmax, CMB_unit='muK')
ctensor2 = results.get_tensor_cls(lmax, CMB_unit='muK')
results = camb.get_transfer_functions(pars)
results.Params.InitPower.set_params(ns=1.1, r=1)
inflation_params = initialpower.InitialPowerLaw()
inflation_params.set_params(ns=0.96, r=0.05, nt=0)
results.power_spectra_from_transfer(inflation_params, silent=True)
cl3 = results.get_lensed_scalar_cls(lmax, CMB_unit='muK')
ctensor3 = results.get_tensor_cls(lmax, CMB_unit='muK')
self.assertTrue(np.allclose(ctensor2, ctensor3 * 2, rtol=1e-4))
self.assertTrue(np.allclose(cl1, cl2, rtol=1e-4))
# These are identical because all scalar spectra were identical (non-linear corrections change it otherwise)
self.assertTrue(np.allclose(cl1, cl3, rtol=1e-4))
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.07, omk=0)
pars.set_for_lmax(2500)
pars.min_l = 2
res = camb.get_results(pars)
cls = res.get_lensed_scalar_cls(2000)
pars.min_l = 1
res = camb.get_results(pars)
cls2 = res.get_lensed_scalar_cls(2000)
np.testing.assert_allclose(cls[2:, 0:2], cls2[2:, 0:2], rtol=1e-4)
self.assertAlmostEqual(cls2[1, 0], 1.30388e-10, places=13)
self.assertAlmostEqual(cls[1, 0], 0)
def testPowers(self):
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.07, omk=0)
pars.set_dark_energy() # re-set defaults
pars.InitPower.set_params(ns=0.965, As=2e-9)
pars.NonLinearModel.set_params(halofit_version='takahashi')
self.assertAlmostEqual(pars.scalar_power(1), 1.801e-9, 4)
self.assertAlmostEqual(pars.scalar_power([1, 1.5])[0], 1.801e-9, 4)
pars.set_matter_power(nonlinear=True)
self.assertEqual(pars.NonLinear, model.NonLinear_pk)
pars.set_matter_power(redshifts=[0., 0.17, 3.1], silent=True, nonlinear=False)
data = camb.get_results(pars)
kh, z, pk = data.get_matter_power_spectrum(1e-4, 1, 20)
kh2, z2, pk2 = data.get_linear_matter_power_spectrum()
s8 = data.get_sigma8()
self.assertAlmostEqual(s8[0], 0.24686, 3)
self.assertAlmostEqual(s8[2], 0.80044, 3)
pars.NonLinear = model.NonLinear_both
data.calc_power_spectra(pars)
kh3, z3, pk3 = data.get_matter_power_spectrum(1e-4, 1, 20)
self.assertAlmostEqual(pk[-1][-3], 51.909, 2)
self.assertAlmostEqual(pk3[-1][-3], 57.709, 2)
self.assertAlmostEqual(pk2[-2][-4], 56.436, 2)
camb.set_feedback_level(0)
PKnonlin = camb.get_matter_power_interpolator(pars, nonlinear=True)
pars.set_matter_power(redshifts=[0, 0.09, 0.15, 0.42, 0.76, 1.5, 2.3, 5.5, 8.9],
silent=True, kmax=10, k_per_logint=5)
pars.NonLinear = model.NonLinear_both
results = camb.get_results(pars)
kh, z, pk = results.get_nonlinear_matter_power_spectrum()
pk_interp = PKnonlin.P(z, kh)
self.assertTrue(np.sum((pk / pk_interp - 1) ** 2) < 0.005)
PKnonlin2 = results.get_matter_power_interpolator(nonlinear=True, extrap_kmax=500)
pk_interp2 = PKnonlin2.P(z, kh)
self.assertTrue(np.sum((pk_interp / pk_interp2 - 1) ** 2) < 0.005)
pars.NonLinearModel.set_params(halofit_version='mead')
_, _, pk = results.get_nonlinear_matter_power_spectrum(params=pars, var1='delta_cdm', var2='delta_cdm')
self.assertAlmostEqual(pk[0][160], 824.6, delta=0.5)
lmax = 4000
pars.set_for_lmax(lmax)
cls = data.get_cmb_power_spectra(pars)
data.get_total_cls(2000)
data.get_unlensed_scalar_cls(2500)
data.get_tensor_cls(2000)
cls_lensed = data.get_lensed_scalar_cls(3000)
cphi = data.get_lens_potential_cls(2000)
# check lensed CL against python; will only agree well for high lmax as python has no extrapolation template
cls_lensed2 = correlations.lensed_cls(cls['unlensed_scalar'], cls['lens_potential'][:, 0], delta_cls=False)
np.testing.assert_allclose(cls_lensed2[2:2000, 2], cls_lensed[2:2000, 2], rtol=1e-3)
np.testing.assert_allclose(cls_lensed2[2:2000, 1], cls_lensed[2:2000, 1], rtol=1e-3)
np.testing.assert_allclose(cls_lensed2[2:2000, 0], cls_lensed[2:2000, 0], rtol=1e-3)
self.assertTrue(np.all(np.abs((cls_lensed2[2:3000, 3] - cls_lensed[2:3000, 3]) /
np.sqrt(cls_lensed2[2:3000, 0] * cls_lensed2[2:3000, 1])) < 1e-4))
corr, xvals, weights = correlations.gauss_legendre_correlation(cls['lensed_scalar'])
clout = correlations.corr2cl(corr, xvals, weights, 2500)
self.assertTrue(np.all(np.abs(clout[2:2300, 2] / cls['lensed_scalar'][2:2300, 2] - 1) < 1e-3))
pars = camb.CAMBparams()
pars.set_cosmology(H0=78, YHe=0.22)
pars.set_for_lmax(2000, lens_potential_accuracy=1)
pars.WantTensors = True
results = camb.get_transfer_functions(pars)
from camb import initialpower
cls = []
for r in [0, 0.2, 0.4]:
inflation_params = initialpower.InitialPowerLaw()
inflation_params.set_params(ns=0.96, r=r, nt=0)
results.power_spectra_from_transfer(inflation_params, silent=True)
cls += [results.get_total_cls(CMB_unit='muK')]
self.assertTrue(np.allclose((cls[1] - cls[0])[2:300, 2] * 2, (cls[2] - cls[0])[2:300, 2], rtol=1e-3))
# Check generating tensors and scalars together
pars = camb.CAMBparams()
pars.set_cosmology(H0=67)
lmax = 2000
pars.set_for_lmax(lmax, lens_potential_accuracy=1)
pars.InitPower.set_params(ns=0.96, r=0)
pars.WantTensors = False
results = camb.get_results(pars)
cl1 = results.get_total_cls(lmax, CMB_unit='muK')
pars.InitPower.set_params(ns=0.96, r=0.1, nt=0)
pars.WantTensors = True
results = camb.get_results(pars)
cl2 = results.get_lensed_scalar_cls(lmax, CMB_unit='muK')
ctensor2 = results.get_tensor_cls(lmax, CMB_unit='muK')
results = camb.get_transfer_functions(pars)
results.Params.InitPower.set_params(ns=1.1, r=1)
inflation_params = initialpower.InitialPowerLaw()
inflation_params.set_params(ns=0.96, r=0.05, nt=0)
results.power_spectra_from_transfer(inflation_params, silent=True)
cl3 = results.get_lensed_scalar_cls(lmax, CMB_unit='muK')
ctensor3 = results.get_tensor_cls(lmax, CMB_unit='muK')
self.assertTrue(np.allclose(ctensor2, ctensor3 * 2, rtol=1e-4))
self.assertTrue(np.allclose(cl1, cl2, rtol=1e-4))
# These are identical because all scalar spectra were identical (non-linear corrections change it otherwise)
self.assertTrue(np.allclose(cl1, cl3, rtol=1e-4))
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.07, omk=0)
pars.set_for_lmax(2500)
pars.min_l = 2
res = camb.get_results(pars)
cls = res.get_lensed_scalar_cls(2000)
pars.min_l = 1
res = camb.get_results(pars)
cls2 = res.get_lensed_scalar_cls(2000)
np.testing.assert_allclose(cls[2:, 0:2], cls2[2:, 0:2], rtol=1e-4)
self.assertAlmostEqual(cls2[1, 0], 1.30388e-10, places=13)
self.assertAlmostEqual(cls[1, 0], 0)
def testPowers(self):
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.07, omk=0)
pars.set_dark_energy() # re-set defaults
pars.InitPower.set_params(ns=0.965, As=2e-9)
self.assertAlmostEqual(pars.scalar_power(1), 1.801e-9, 4)
self.assertAlmostEqual(pars.scalar_power([1, 1.5])[0], 1.801e-9, 4)
pars.set_matter_power(redshifts=[0., 0.17, 3.1])
pars.NonLinear = model.NonLinear_none
data = camb.get_results(pars)
kh, z, pk = data.get_matter_power_spectrum(1e-4, 1, 20)
kh2, z2, pk2 = data.get_linear_matter_power_spectrum()
s8 = data.get_sigma8()
self.assertAlmostEqual(s8[0], 0.24686, 4)
self.assertAlmostEqual(s8[2], 0.80044, 4)
pars.NonLinear = model.NonLinear_both
data.calc_power_spectra(pars)
kh3, z3, pk3 = data.get_matter_power_spectrum(1e-4, 1, 20)
self.assertAlmostEqual(pk[-1][-3], 51.909, 2)
self.assertAlmostEqual(pk3[-1][-3], 57.697, 2)
self.assertAlmostEqual(pk2[-2][-4], 53.47, 2)
camb.set_feedback_level(0)
PKnonlin = camb.get_matter_power_interpolator(pars, nonlinear=True)
pars.set_matter_power(redshifts=[0, 0.09, 0.15, 0.42, 0.76, 1.5, 2.3, 5.5, 8.9], kmax=10, k_per_logint=5)
pars.NonLinear = model.NonLinear_both
results = camb.get_results(pars)
kh, z, pk = results.get_nonlinear_matter_power_spectrum()
pk_interp = PKnonlin.P(z, kh)
self.assertTrue(np.sum((pk / pk_interp - 1) ** 2) < 0.005)
camb.set_halofit_version('mead')
_, _, pk = results.get_nonlinear_matter_power_spectrum(params=pars, var1='delta_cdm', var2='delta_cdm')
self.assertTrue(np.abs(pk[0][160] / 232.08 - 1) < 1e-3)
lmax = 4000
pars.set_for_lmax(lmax)
cls = data.get_cmb_power_spectra(pars)
cls_tot = data.get_total_cls(2000)
cls_scal = data.get_unlensed_scalar_cls(2500)
cls_tensor = data.get_tensor_cls(2000)
cls_lensed = data.get_lensed_scalar_cls(3000)
cls_phi = data.get_lens_potential_cls(2000)
# check lensed CL against python; will only agree well for high lmax as python has no extrapolation template
cls_lensed2 = correlations.lensed_cls(cls['unlensed_scalar'], cls['lens_potential'][:, 0], delta_cls=False)
self.assertTrue(np.all(np.abs(cls_lensed2[2:2000, 2] / cls_lensed[2:2000, 2] - 1) < 1e-3))
self.assertTrue(np.all(np.abs(cls_lensed2[2:3000, 0] / cls_lensed[2:3000, 0] - 1) < 1e-3))
self.assertTrue(np.all(np.abs(cls_lensed2[2:3000, 1] / cls_lensed[2:3000, 1] - 1) < 1e-3))
self.assertTrue(np.all(np.abs((cls_lensed2[2:3000, 3] - cls_lensed[2:3000, 3]) /
np.sqrt(cls_lensed2[2:3000, 0] * cls_lensed2[2:3000, 1])) < 1e-4))
corr, xvals, weights = correlations.gauss_legendre_correlation(cls['lensed_scalar'])
clout = correlations.corr2cl(corr, xvals, weights, 2500)
self.assertTrue(np.all(np.abs(clout[2:2300, 2] / cls['lensed_scalar'][2:2300, 2] - 1) < 1e-3))
def get_N0_iter(qe_key: str,
nlev_t: float,
nlev_p: float,
beam_fwhm: float,
cls_unl_fid: dict,
lmin_ivf,
lmax_ivf,
itermax,
cls_unl_dat=None,
lmax_qlm=None,
ret_delcls=False,
datnoise_cls: dict or None = None,
unlQE=False,
version='1'):
"""Iterative lensing-N0 estimate
Calculates iteratively partially lensed spectra and lensing noise levels.
This uses the python camb package to get the partially lensed spectra.
This makes no assumption on response = 1 / noise hence is about twice as slow as it could be in standard cases.
Args:
qe_key: QE estimator key
nlev_t: temperature noise level (in :math:`\mu `K-arcmin)
nlev_p: polarisation noise level (in :math:`\mu `K-arcmin)
beam_fwhm: Gaussian beam full width half maximum in arcmin
cls_unl_fid(dict): unlensed CMB power spectra
lmin_ivf: minimal CMB multipole used in the QE
lmax_ivf: maximal CMB multipole used in the QE
itermax: number of iterations to perform
lmax_qlm(optional): maximum lensing multipole to consider. Defaults to :math:`2 lmax_ivf`
ret_delcls(optional): returns the partially delensed CMB cls as well if set
datnoise_cls(optional): feeds in custom noise spectra to the data. The nlevs and beam only apply to the filtering in this case
Returns
Array of shape (itermax + 1, lmax_qlm + 1) with all iterated N0s. First entry is standard N0.
Note: This assumes the unlensed spectra are known
#FIXME: this is requiring the full camb python package for the lensed spectra calc.
"""
assert qe_key in ['p_p', 'p', 'ptt'], qe_key
try:
from camb.correlations import lensed_cls
except ImportError:
assert 0, "could not import camb.correlations.lensed_cls"
if lmax_qlm is None:
lmax_qlm = 2 * lmax_ivf
lmax_qlm = min(lmax_qlm, 2 * lmax_ivf)
lmin_ivf = max(lmin_ivf, 1)
transfi2 = utils.cli(
hp.gauss_beam(beam_fwhm / 180. / 60. * np.pi, lmax=lmax_ivf))**2
llp2 = np.arange(lmax_qlm + 1, dtype=float)**2 * np.arange(
1, lmax_qlm + 2, dtype=float)**2 / (2. * np.pi)
if datnoise_cls is None:
datnoise_cls = dict()
if qe_key in ['ptt', 'p']:
datnoise_cls['tt'] = (nlev_t * np.pi / 180. / 60.)**2 * transfi2
if qe_key in ['p_p', 'p']:
datnoise_cls['ee'] = (nlev_p * np.pi / 180. / 60.)**2 * transfi2
datnoise_cls['bb'] = (nlev_p * np.pi / 180. / 60.)**2 * transfi2
N0s_biased = []
N0s_unbiased = []
N1s_biased = []
N1s_unbiased = []
delcls_fid = []
delcls_true = []
N0_unbiased = np.inf
N1_unbiased = np.inf
dls_unl_fid, cldd_fid = cls2dls(cls_unl_fid)
cls_len_fid = dls2cls(lensed_cls(dls_unl_fid, cldd_fid))
if cls_unl_dat is None:
cls_unl_dat = cls_unl_fid
cls_len_true = cls_len_fid
else:
dls_unl_true, cldd_true = cls2dls(cls_unl_dat)
cls_len_true = dls2cls(lensed_cls(dls_unl_true, cldd_true))
cls_plen_true = cls_len_true
for irr, it in utils.enumerate_progress(range(itermax + 1)):
dls_unl_true, cldd_true = cls2dls(cls_unl_dat)
dls_unl_fid, cldd_fid = cls2dls(cls_unl_fid)
if it == 0:
rho_sqd_phi = 0.
else:
# The cross-correlation coefficient is identical for the Rfid-biased QE or the rescaled one
rho_sqd_phi = np.zeros(len(cldd_true))
rho_sqd_phi[:lmax_qlm + 1] = cldd_true[:lmax_qlm + 1] * utils.cli(
cldd_true[:lmax_qlm + 1] + llp2 *
(N0_unbiased[:lmax_qlm + 1] + N1_unbiased[:lmax_qlm + 1]))
if 'wE' in version:
assert qe_key in ['p_p']
if it == 0:
print('including imperfect knowledge of E in iterations')
slic = slice(lmin_ivf, lmax_ivf + 1)
rho_sqd_E = np.zeros(len(dls_unl_true[:, 1]))
rho_sqd_E[slic] = cls_unl_dat['ee'][slic] * utils.cli(
cls_plen_true['ee'][slic] + datnoise_cls['ee'][slic])
dls_unl_fid[:, 1] *= rho_sqd_E
dls_unl_true[:, 1] *= rho_sqd_E
cldd_fid *= rho_sqd_phi
cldd_true *= rho_sqd_phi
cls_plen_fid_resolved = dls2cls(lensed_cls(dls_unl_fid, cldd_fid))
cls_plen_true_resolved = dls2cls(
lensed_cls(dls_unl_true, cldd_true))
cls_plen_fid = {
ck: cls_len_fid[ck] - (cls_plen_fid_resolved[ck] -
cls_unl_fid[ck][:len(cls_len_fid[ck])])
for ck in cls_len_fid.keys()
}
cls_plen_true = {
ck:
cls_len_true[ck] - (cls_plen_true_resolved[ck] -
cls_unl_dat[ck][:len(cls_len_true[ck])])
for ck in cls_len_true.keys()
}
else:
cldd_true *= (1. - rho_sqd_phi) # The true residual lensing spec.
cldd_fid *= (1. - rho_sqd_phi
) # What I think the residual lensing spec is
cls_plen_fid = dls2cls(lensed_cls(dls_unl_fid, cldd_fid))
cls_plen_true = dls2cls(lensed_cls(dls_unl_true, cldd_true))
cls_filt = cls_plen_fid if not unlQE else cls_unl_fid
cls_w = cls_plen_fid if not unlQE else cls_unl_fid
cls_f = cls_plen_true
fal = {}
dat_delcls = {}
if qe_key in ['ptt', 'p']:
fal['tt'] = cls_filt['tt'][:lmax_ivf + 1] + (
nlev_t * np.pi / 180. / 60.)**2 * transfi2
dat_delcls['tt'] = cls_plen_true['tt'][:lmax_ivf +
1] + datnoise_cls['tt']
if qe_key in ['p_p', 'p']:
fal['ee'] = cls_filt['ee'][:lmax_ivf + 1] + (
nlev_p * np.pi / 180. / 60.)**2 * transfi2
fal['bb'] = cls_filt['bb'][:lmax_ivf + 1] + (
nlev_p * np.pi / 180. / 60.)**2 * transfi2
dat_delcls['ee'] = cls_plen_true['ee'][:lmax_ivf +
1] + datnoise_cls['ee']
dat_delcls['bb'] = cls_plen_true['bb'][:lmax_ivf +
1] + datnoise_cls['bb']
if qe_key in ['p']:
fal['te'] = np.copy(cls_filt['te'][:lmax_ivf + 1])
dat_delcls['te'] = np.copy(cls_plen_true['te'][:lmax_ivf + 1])
fal = utils.cl_inverse(fal)
for cl in fal.values():
cl[:lmin_ivf] *= 0.
for cl in dat_delcls.values():
cl[:lmin_ivf] *= 0.
cls_ivfs_arr = utils.cls_dot([fal, dat_delcls, fal])
cls_ivfs = dict()
for i, a in enumerate(['t', 'e', 'b']):
for j, b in enumerate(['t', 'e', 'b'][i:]):
if np.any(cls_ivfs_arr[i, j + i]):
cls_ivfs[a + b] = cls_ivfs_arr[i, j + i]
n_gg = get_nhl(qe_key,
qe_key,
cls_w,
cls_ivfs,
lmax_ivf,
lmax_ivf,
lmax_out=lmax_qlm)[0]
r_gg_true = qresp.get_response(qe_key,
lmax_ivf,
'p',
cls_w,
cls_f,
fal,
lmax_qlm=lmax_qlm)[0]
r_gg_fid = qresp.get_response(
qe_key, lmax_ivf, 'p', cls_w, cls_w, fal,
lmax_qlm=lmax_qlm)[0] if cls_f is not cls_w else r_gg_true
N0_biased = n_gg * utils.cli(
r_gg_fid**
2) # N0 of possibly biased (by Rtrue / Rfid) QE estimator
N0_unbiased = n_gg * utils.cli(
r_gg_true**2
) # N0 of QE estimator after rescaling by Rfid / Rtrue to make it unbiased
N0s_biased.append(N0_biased)
N0s_unbiased.append(N0_unbiased)
cls_plen_true['pp'] = cldd_true * utils.cli(
np.arange(len(cldd_true))**2 *
np.arange(1, len(cldd_true) + 1, dtype=float)**2 / (2. * np.pi))
cls_plen_fid['pp'] = cldd_fid * utils.cli(
np.arange(len(cldd_fid))**2 *
np.arange(1, len(cldd_fid) + 1, dtype=float)**2 / (2. * np.pi))
if 'wN1' in version:
if it == 0: print('Adding n1 in iterations')
from lensitbiases import n1_fft
from scipy.interpolate import UnivariateSpline as spl
lib = n1_fft.n1_fft(fal,
cls_w,
cls_f,
np.copy(cls_plen_true['pp']),
lminbox=50,
lmaxbox=5000,
k2l=None)
n1_Ls = np.arange(50, lmax_qlm + 1, 50)
if lmax_qlm not in n1_Ls: n1_Ls = np.append(n1_Ls, lmax_qlm)
n1 = np.array(
[lib.get_n1(qe_key, L, do_n1mat=False) for L in n1_Ls])
N1_biased = spl(n1_Ls,
n1_Ls**2 * (n1_Ls * 1. + 1)**2 * n1 /
r_gg_fid[n1_Ls]**2,
k=2,
s=0,
ext='zeros')(np.arange(len(N0_unbiased)))
N1_biased *= utils.cli(
np.arange(lmax_qlm + 1)**2 *
np.arange(1, lmax_qlm + 2, dtype=float)**2)
N1_unbiased = N1_biased * (r_gg_fid * utils.cli(r_gg_true))**2
else:
N1_biased = np.zeros(lmax_qlm + 1, dtype=float)
N1_unbiased = np.zeros(lmax_qlm + 1, dtype=float)
delcls_fid.append(cls_plen_fid)
delcls_true.append(cls_plen_true)
N1s_biased.append(N1_biased)
N1s_unbiased.append(N1_unbiased)
return (np.array(N0s_biased),
np.array(N0s_unbiased)) if not ret_delcls else (
(np.array(N0s_biased), np.array(N0s_unbiased), delcls_fid,
delcls_true))
def get_N0_iter(qe_key: str,
nlev_t: float or np.ndarray,
nlev_p: float or np.ndarray,
beam_fwhm: float,
cls_unl_fid: dict,
lmin_cmb,
lmax_cmb,
itermax,
cls_unl_dat=None,
lmax_qlm=None,
ret_delcls=False,
datnoise_cls: dict or None = None):
r"""Iterative lensing-N0 estimate
Calculates iteratively partially lensed spectra and lensing noise levels.
This uses the python camb package to get the partially lensed spectra.
At each iteration this takes out the resolved part of the lenses and recomputes a N0
Args:
qe_key: QE estimator key
nlev_t: temperature noise level (in :math:`\mu `K-arcmin) (an array can be passed for scale-dependent noise level)
nlev_p: polarisation noise level (in :math:`\mu `K-arcmin)(an array can be passed for scale-dependent noise level)
beam_fwhm: Gaussian beam full width half maximum in arcmin
cls_unl_fid(dict): unlensed CMB power spectra
lmin_cmb: minimal CMB multipole used in the QE
lmax_cmb: maximal CMB multipole used in the QE
itermax: number of iterations to perform
lmax_qlm(optional): maximum lensing multipole to consider. Defaults to 2 lmax_ivf
ret_delcls(optional): returns the partially delensed CMB cls as well if set
datnoise_cls(optional): feeds in custom noise spectra to the data. The nlevs and beam only apply to the filtering in this case
Returns
Array of shape (itermax + 1, lmax_qlm + 1) with all iterated N0s. First entry is standard N0.
Note:
this is requiring camb python package for the lensed spectra calc.
"""
assert qe_key in ['p_p', 'p', 'ptt'], qe_key
try:
from camb.correlations import lensed_cls
except ImportError:
assert 0, "could not import camb.correlations.lensed_cls"
if isinstance(lmax_cmb, dict):
lmaxs_ivf = lmax_cmb
print("Seeing lmax's:")
for s in lmaxs_ivf.keys():
print(s + ': ' + str(lmaxs_ivf[s]))
else:
lmaxs_ivf = {s: lmax_cmb for s in ['t', 'e', 'b']}
lmin_ivf = lmin_cmb
lmax_ivf = np.max(list(lmaxs_ivf.values()))
if lmax_qlm is None:
lmax_qlm = 2 * lmax_ivf
lmax_qlm = min(lmax_qlm, 2 * lmax_ivf)
lmin_ivf = max(lmin_ivf, 1)
transfi2 = utils.cli(
hp.gauss_beam(beam_fwhm / 180. / 60. * np.pi, lmax=lmax_ivf))**2
llp2 = np.arange(lmax_qlm + 1, dtype=float)**2 * np.arange(
1, lmax_qlm + 2, dtype=float)**2 / (2. * np.pi)
if datnoise_cls is None:
datnoise_cls = dict()
if qe_key in ['ptt', 'p']:
datnoise_cls['tt'] = (nlev_t * np.pi / 180. / 60.)**2 * transfi2
if qe_key in ['p_p', 'p']:
datnoise_cls['ee'] = (nlev_p * np.pi / 180. / 60.)**2 * transfi2
datnoise_cls['bb'] = (nlev_p * np.pi / 180. / 60.)**2 * transfi2
N0s_biased = []
N0s_unbiased = []
delcls_fid = []
delcls_true = []
N0_unbiased = np.inf
if cls_unl_dat is None:
cls_unl_dat = cls_unl_fid
for irr, it in utils.enumerate_progress(range(itermax + 1)):
dls_unl_true, cldd_true = cls2dls(cls_unl_dat)
dls_unl_fid, cldd_fid = cls2dls(cls_unl_fid)
if it == 0:
rho_sqd_phi = 0.
else:
# The cross-correlation coefficient is identical for the Rfid-biased QE or the rescaled one
rho_sqd_phi = np.zeros(len(cldd_true))
rho_sqd_phi[:lmax_qlm + 1] = cldd_true[:lmax_qlm + 1] * utils.cli(
cldd_true[:lmax_qlm + 1] + llp2 * N0_unbiased[:lmax_qlm + 1])
cldd_true *= (1. - rho_sqd_phi) # The true residual lensing spec.
cldd_fid *= (1. - rho_sqd_phi
) # What I think the residual lensing spec is
cls_plen_fid = dls2cls(lensed_cls(dls_unl_fid, cldd_fid))
cls_plen_true = dls2cls(lensed_cls(dls_unl_true, cldd_true))
cls_filt = cls_plen_fid
cls_f = cls_plen_true
fal = {}
dat_delcls = {}
if qe_key in ['ptt', 'p']:
fal['tt'] = cls_filt['tt'][:lmax_ivf + 1] + (
nlev_t * np.pi / 180. / 60.)**2 * transfi2
dat_delcls['tt'] = cls_plen_true['tt'][:lmax_ivf +
1] + datnoise_cls['ee']
if qe_key in ['p_p', 'p']:
fal['ee'] = cls_filt['ee'][:lmax_ivf + 1] + (
nlev_p * np.pi / 180. / 60.)**2 * transfi2
fal['bb'] = cls_filt['bb'][:lmax_ivf + 1] + (
nlev_p * np.pi / 180. / 60.)**2 * transfi2
dat_delcls['ee'] = cls_plen_true['ee'][:lmax_ivf +
1] + datnoise_cls['ee']
dat_delcls['bb'] = cls_plen_true['bb'][:lmax_ivf +
1] + datnoise_cls['bb']
if qe_key in ['p']:
fal['te'] = np.copy(cls_filt['te'][:lmax_ivf + 1])
dat_delcls['te'] = np.copy(cls_plen_true['te'][:lmax_ivf + 1])
for spec in fal.keys():
fal[spec][min(lmaxs_ivf[spec[0]], lmaxs_ivf[spec[1]]) + 1:] *= 0
for spec in dat_delcls.keys():
dat_delcls[spec][min(lmaxs_ivf[spec[0]], lmaxs_ivf[spec[1]]) +
1:] *= 0
fal = utils.cl_inverse(fal)
for cl in fal.values():
cl[:lmin_ivf] *= 0.
for cl in dat_delcls.values():
cl[:lmin_ivf] *= 0.
cls_ivfs = utils.cls_dot([fal, dat_delcls, fal], ret_dict=True)
cls_w = deepcopy(cls_plen_fid)
for spec in cls_w.keys(): # in principle not necessary
cls_w[spec][:lmin_ivf] *= 0.
cls_w[spec][min(lmaxs_ivf[spec[0]], lmaxs_ivf[spec[1]]) + 1:] *= 0
n_gg = nhl.get_nhl(qe_key,
qe_key,
cls_w,
cls_ivfs,
lmax_ivf,
lmax_ivf,
lmax_out=lmax_qlm)[0]
r_gg_true = qresp.get_response(qe_key,
lmax_ivf,
'p',
cls_w,
cls_f,
fal,
lmax_qlm=lmax_qlm)[0]
r_gg_fid = qresp.get_response(
qe_key, lmax_ivf, 'p', cls_w, cls_w, fal,
lmax_qlm=lmax_qlm)[0] if cls_f is not cls_w else r_gg_true
N0_biased = n_gg * utils.cli(
r_gg_fid**
2) # N0 of possibly biased (by Rtrue / Rfid) QE estimator
N0_unbiased = n_gg * utils.cli(
r_gg_true**2
) # N0 of QE estimator after rescaling by Rfid / Rtrue to make it unbiased
N0s_biased.append(N0_biased)
N0s_unbiased.append(N0_unbiased)
cls_plen_true['pp'] = cldd_true * utils.cli(
np.arange(len(cldd_true))**2 *
np.arange(1, len(cldd_true) + 1, dtype=float)**2 / (2. * np.pi))
cls_plen_fid['pp'] = cldd_fid * utils.cli(
np.arange(len(cldd_fid))**2 *
np.arange(1, len(cldd_fid) + 1, dtype=float)**2 / (2. * np.pi))
delcls_fid.append(cls_plen_fid)
delcls_true.append(cls_plen_true)
return (np.array(N0s_biased),
np.array(N0s_unbiased)) if not ret_delcls else (
(np.array(N0s_biased), np.array(N0s_unbiased), delcls_fid,
delcls_true))
