def ctheta_2_cell(theta_deg, ctheta, lmax, normalization=1.):
### this is how camb recommends to prepare the x = cos(theta) values for integration
### These x values do not contain x=1 so we have. to do this case separately
x, w = np.polynomial.legendre.leggauss(lmax + 1)
xdeg = np.degrees(np.arccos(x))
### We first replace theta=0 by 0 and do that case separately
myctheta = ctheta.copy()
myctheta[0] = 0
### And now we fill the array that should include polarization (we put zeros there)
### with the values of our imput c(theta) interpolated at the x locations
allctheta = np.zeros((len(x), 4))
allctheta[:, 0] = np.interp(xdeg, theta_deg, myctheta)
### Here we call the camb function that does the transform to Cl
clth = cc.corr2cl(allctheta, x, w, lmax)
lll = np.arange(lmax + 1)
### the special case x=1 corresponds to theta=0 and add 2pi times c(theta=0) to the Cell
return lll, clth[:, 0] + ctheta[0] * normalization
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))
