def testTimeTransfers(self): from camb import initialpower pars = camb.set_params(H0=69, YHe=0.22, lmax=2000, lens_potential_accuracy=1, ns=0.96, As=2.5e-9) results1 = camb.get_results(pars) cl1 = results1.get_total_cls() pars = camb.set_params(H0=69, YHe=0.22, lmax=2000, lens_potential_accuracy=1) results = camb.get_transfer_functions(pars, only_time_sources=True) inflation_params = initialpower.InitialPowerLaw() inflation_params.set_params(ns=0.96, As=2.5e-9) results.power_spectra_from_transfer(inflation_params) cl2 = results.get_total_cls() np.testing.assert_allclose(cl1, cl2, rtol=1e-4) inflation_params.set_params(ns=0.96, As=1.9e-9) results.power_spectra_from_transfer(inflation_params) inflation_params.set_params(ns=0.96, As=2.5e-9) results.power_spectra_from_transfer(inflation_params) cl2 = results.get_total_cls() np.testing.assert_allclose(cl1, cl2, rtol=1e-4) 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, only_time_sources=True) 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) 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))
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 testAssigments(self): ini = os.path.join(os.path.dirname(__file__), '..', 'inifiles', 'planck_2018.ini') if os.path.exists(ini): pars = camb.read_ini(ini) self.assertTrue(np.abs(camb.get_background(pars).cosmomc_theta() * 100 / 1.040909 - 1) < 2e-5) pars = camb.CAMBparams() pars.set_cosmology(H0=68.5, ombh2=0.022, mnu=0, omch2=0.1) self.assertAlmostEqual(pars.omegam, (0.022 + 0.1) / 0.685 ** 2) with self.assertRaises(AttributeError): # noinspection PyPropertyAccess pars.omegam = 1 pars.InitPower.set_params(ns=0.01) data = camb.CAMBdata() data.Params = pars self.assertEqual(data.Params.InitPower.ns, pars.InitPower.ns) d = dark_energy.DarkEnergyFluid(w=-0.95) pars.DarkEnergy = d self.assertEqual(pars.DarkEnergy.w, -0.95) pars.DarkEnergy = dark_energy.AxionEffectiveFluid(w_n=0.4) data.Params = pars self.assertEqual(pars.DarkEnergy.w_n, 0.4) pars.z_outputs = [0.1, 0.4] self.assertEqual(pars.z_outputs[1], 0.4) pars.z_outputs[0] = 0.3 self.assertEqual(pars.z_outputs[0], 0.3) pars.z_outputs = pars.z_outputs pars.z_outputs = [] pars.z_outputs = None self.assertFalse(len(pars.z_outputs)) with self.assertRaises(TypeError): pars.DarkEnergy = initialpower.InitialPowerLaw() pars.NonLinear = model.NonLinear_both printstr = str(pars) self.assertTrue('Want_CMB_lensing = True' in printstr and "NonLinear = NonLinear_both" in printstr) pars.NonLinear = model.NonLinear_lens self.assertTrue(pars.NonLinear == model.NonLinear_lens) with self.assertRaises(ValueError): pars.NonLinear = 4 pars.nu_mass_degeneracies = np.zeros(3) self.assertTrue(len(pars.nu_mass_degeneracies) == 3) pars.nu_mass_degeneracies = [1, 2, 3] self.assertTrue(pars.nu_mass_degeneracies[1] == 2) pars.nu_mass_degeneracies[1] = 5 self.assertTrue(pars.nu_mass_degeneracies[1] == 5) with self.assertRaises(CAMBParamRangeError): pars.nu_mass_degeneracies = np.zeros(7) pars.nu_mass_eigenstates = 0 self.assertFalse(len((pars.nu_mass_degeneracies[:1]))) pars = camb.set_params(**{'InitPower.ns': 1.2, 'WantTransfer': True}) self.assertEqual(pars.InitPower.ns, 1.2) self.assertTrue(pars.WantTransfer) pars.DarkEnergy = None from camb.sources import GaussianSourceWindow pars = camb.CAMBparams() pars.SourceWindows = [GaussianSourceWindow(), GaussianSourceWindow(redshift=1)] self.assertEqual(pars.SourceWindows[1].redshift, 1) pars.SourceWindows[0].redshift = 2 self.assertEqual(pars.SourceWindows[0].redshift, 2) self.assertTrue(len(pars.SourceWindows) == 2) pars.SourceWindows[0] = GaussianSourceWindow(redshift=3) self.assertEqual(pars.SourceWindows[0].redshift, 3) self.assertTrue('redshift = 3.0' in str(pars)) pars.SourceWindows = pars.SourceWindows[0:1] self.assertTrue(len(pars.SourceWindows) == 1) pars.SourceWindows = [] self.assertTrue(len(pars.SourceWindows) == 0)
def testAssigments(self): ini = os.path.join(os.path.dirname(__file__), '..', 'inifiles', 'planck_2018.ini') if os.path.exists(ini): pars = camb.read_ini(ini) self.assertTrue( np.abs( camb.get_background(pars).cosmomc_theta() * 100 / 1.040909 - 1) < 2e-5) pars = camb.CAMBparams() pars.set_cosmology(H0=68.5, ombh2=0.022, mnu=0, omch2=0.1) self.assertAlmostEqual(pars.omegam, (0.022 + 0.1) / 0.685**2) with self.assertRaises(AttributeError): # noinspection PyPropertyAccess pars.omegam = 1 pars.InitPower.set_params(ns=0.01) data = camb.CAMBdata() data.Params = pars self.assertEqual(data.Params.InitPower.ns, pars.InitPower.ns) d = dark_energy.DarkEnergyFluid(w=-0.95) pars.DarkEnergy = d self.assertEqual(pars.DarkEnergy.w, -0.95) pars.DarkEnergy = dark_energy.AxionEffectiveFluid(w_n=0.4) data.Params = pars self.assertEqual(pars.DarkEnergy.w_n, 0.4) pars.z_outputs = [0.1, 0.4] self.assertEqual(pars.z_outputs[1], 0.4) pars.z_outputs[0] = 0.3 self.assertEqual(pars.z_outputs[0], 0.3) pars.z_outputs = pars.z_outputs pars.z_outputs = [] pars.z_outputs = None # noinspection PyTypeChecker self.assertFalse(len(pars.z_outputs)) with self.assertRaises(TypeError): pars.DarkEnergy = initialpower.InitialPowerLaw() pars.NonLinear = model.NonLinear_both printstr = str(pars) self.assertTrue('Want_CMB_lensing = True' in printstr and "NonLinear = NonLinear_both" in printstr) pars.NonLinear = model.NonLinear_lens self.assertTrue(pars.NonLinear == model.NonLinear_lens) with self.assertRaises(ValueError): pars.NonLinear = 4 pars.nu_mass_degeneracies = np.zeros(3) self.assertTrue(len(pars.nu_mass_degeneracies) == 3) pars.nu_mass_degeneracies = [1, 2, 3] self.assertTrue(pars.nu_mass_degeneracies[1] == 2) pars.nu_mass_degeneracies[1] = 5 self.assertTrue(pars.nu_mass_degeneracies[1] == 5) with self.assertRaises(CAMBParamRangeError): pars.nu_mass_degeneracies = np.zeros(7) pars.nu_mass_eigenstates = 0 self.assertFalse(len((pars.nu_mass_degeneracies[:1]))) pars = camb.set_params(**{'InitPower.ns': 1.2, 'WantTransfer': True}) self.assertEqual(pars.InitPower.ns, 1.2) self.assertTrue(pars.WantTransfer) pars.DarkEnergy = None pars = camb.set_params(**{ 'H0': 67, 'ombh2': 0.002, 'r': 0.1, 'Accuracy.AccurateBB': True }) self.assertEqual(pars.Accuracy.AccurateBB, True) from camb.sources import GaussianSourceWindow pars = camb.CAMBparams() pars.SourceWindows = [ GaussianSourceWindow(), GaussianSourceWindow(redshift=1) ] self.assertEqual(pars.SourceWindows[1].redshift, 1) pars.SourceWindows[0].redshift = 2 self.assertEqual(pars.SourceWindows[0].redshift, 2) self.assertTrue(len(pars.SourceWindows) == 2) pars.SourceWindows[0] = GaussianSourceWindow(redshift=3) self.assertEqual(pars.SourceWindows[0].redshift, 3) self.assertTrue('redshift = 3.0' in str(pars)) pars.SourceWindows = pars.SourceWindows[0:1] self.assertTrue(len(pars.SourceWindows) == 1) pars.SourceWindows = [] self.assertTrue(len(pars.SourceWindows) == 0) params = camb.get_valid_numerical_params() self.assertEqual( params, { 'ombh2', 'deltazrei', 'omnuh2', 'tau', 'omk', 'zrei', 'thetastar', 'nrunrun', 'meffsterile', 'nnu', 'ntrun', 'HMCode_A_baryon', 'HMCode_eta_baryon', 'HMCode_logT_AGN', 'cosmomc_theta', 'YHe', 'wa', 'cs2', 'H0', 'mnu', 'Alens', 'TCMB', 'ns', 'nrun', 'As', 'nt', 'r', 'w', 'omch2' }) params2 = camb.get_valid_numerical_params( dark_energy_model='AxionEffectiveFluid') self.assertEqual(params2.difference(params), {'fde_zc', 'w_n', 'zc', 'theta_i'})