def xi_dd(r,z,h,Omega_DM,ombh2,w,mnu,smoothing_scale):
# returns the matter-matter correlation function (dimensionless)
# at comoving separation r in Mpc/h, and using the given smoothing scale in Mpc/h
H0 = h*100
omch2 = Omega_DM * h**2
Omega_b = ombh2 * h**(-2)
Omega_M = Omega_DM + Omega_b
pars = camb.CAMBparams() #Set up a new set of parameters for CAMB
pars.set_cosmology(H0=H0, ombh2=ombh2, omch2=omch2, mnu=mnu, omk=0, tau=0.06)
pars.InitPower.set_params(ns=0.965, r=0)
pars.set_dark_energy(w)
pars.set_for_lmax(2500, lens_potential_accuracy=0);
results = camb.get_results(pars)
pars.set_matter_power(redshifts=[z], kmax=2.0)
pars.NonLinear = model.NonLinear_none #compute the linear power spectrum (i.e. w/o halofit)
#pars.NonLinear = model.NonLinear_both #using halofit
results = camb.get_results(pars) #computes the power spectra
kh, dummy, pk = results.get_matter_power_spectrum(minkh=1e-3, maxkh=2, npoints = 10000)
curlyP = kh**3 * pk / (2.0*math.pi**2)
dlnk = log(kh[1])-log(kh[0])
temp=0
for i in range(0,size(kh)):
temp = temp + dlnk * sinc(kh[i]*r) * curlyP[0][i] * window(kh[i]*smoothing_scale)**2
return temp
def testInstances(self):
pars = camb.set_params(H0=69.1, ombh2=0.032, omch2=0.122, As=3e-9, ns=0.91, omk=0.013,
redshifts=[0.], kmax=0.5)
data = camb.get_background(pars)
res1 = data.angular_diameter_distance(0.7)
drag1 = data.get_derived_params()['rdrag']
pars2 = camb.set_params(H0=65, ombh2=0.022, omch2=0.122, As=3e-9, ns=0.91)
data2 = camb.get_background(pars2)
res2 = data2.angular_diameter_distance(1.7)
drag2 = data2.get_derived_params()['rdrag']
self.assertAlmostEqual(res1, data.angular_diameter_distance(0.7))
self.assertAlmostEqual(res2, data2.angular_diameter_distance(1.7))
self.assertAlmostEqual(drag1, data.get_derived_params()['rdrag'])
self.assertEqual(pars2.InitPower.ns, data2.Params.InitPower.ns)
data2.calc_background(pars)
self.assertEqual(pars.InitPower.ns, data2.Params.InitPower.ns)
self.assertAlmostEqual(res1, data2.angular_diameter_distance(0.7))
data3 = camb.get_results(pars2)
cl3 = data3.get_lensed_scalar_cls(1000)
self.assertAlmostEqual(res2, data3.angular_diameter_distance(1.7))
self.assertAlmostEqual(drag2, data3.get_derived_params()['rdrag'], places=3)
self.assertAlmostEqual(drag1, data.get_derived_params()['rdrag'], places=3)
pars.set_for_lmax(3000, lens_potential_accuracy=1)
camb.get_results(pars)
del data3
data4 = camb.get_results(pars2)
cl4 = data4.get_lensed_scalar_cls(1000)
self.assertTrue(np.allclose(cl4, cl3))
def xi_dv(r, z, h, Omega_DM, ombh2, w, mnu, smoothing_scale):
# comoving matter-velocity correlation function in km/s at comoving distance r (in units Mpc/h) and redshift z
# the power spectrum is smoothed with a top-hat window function with the specified smoothing scale in Mpc/h.
# w is the dark energy eq. of state
# mnu is the sum of the neutrino masses in eV
H0 = h*100
omch2 = Omega_DM * h**2
Omega_b = ombh2 * h**(-2)
Omega_M = Omega_DM + Omega_b
pars = camb.CAMBparams() #Set up a new set of parameters for CAMB
pars.set_cosmology(H0=H0, ombh2=ombh2, omch2=omch2, mnu=mnu, omk=0, tau=0.06)
pars.InitPower.set_params(ns=0.965, r=0)
pars.set_dark_energy(w)
pars.set_for_lmax(2500, lens_potential_accuracy=0);
results = camb.get_results(pars)
pars.set_matter_power(redshifts=[z], kmax=2.0)
pars.NonLinear = model.NonLinear_none #compute the linear power spectrum (i.e. w/o halofit)
#pars.NonLinear = model.NonLinear_both #using halofit
results = camb.get_results(pars) #compute the power spectra
kh, dummy, pk = results.get_matter_power_spectrum(minkh=1e-3, maxkh=2, npoints = 10000)
curlyP = kh**3 * pk / (2.0*math.pi**2)
# pk in Mpc^3/h^3
# kh in h/Mpc
# curlyP is dimensionless
dlnk = log(kh[1])-log(kh[0])
temp = 0
norm = -a(z)*Hred(Omega_M,z)*growth_rate(Omega_M,z)
# units of norm: km/s/(Mpc/h)
for i in range(0,size(kh)):
temp = temp + dlnk * j1(kh[i]*r) * curlyP[0][i]/kh[i] * window(kh[i]*smoothing_scale)**2
return norm*temp
def setup(self):
"""
Make a CAMB test run to confirm that everything works for the default
parametrization.
"""
self.CAMBparams.set_cosmology(**self.cosmo_constants)
self.CAMBparams.InitPower.set_params(**self.init_constants)
camb.get_results(self.CAMBparams)
def __call__(self, ctx):
p = ctx.getParams()
try:
cosmo_params = {}
for k,v in self.cosmo_mapping.items():
cosmo_params[k] = p[v]
cosmo_params.update(self.cosmo_constants)
self.CAMBparams.set_cosmology(**cosmo_params)
init_params = {}
for k,v in self.init_mapping.items():
init_params[k] = p[v]
init_params.update(self.init_constants)
self.CAMBparams.InitPower.set_params(**init_params)
results = camb.get_results(self.CAMBparams)
Tcmb = self.CAMBparams.TCMB*1e6
powers = results.get_cmb_power_spectra(lmax = self.lmax)['total']
powers *= (Tcmb * Tcmb)
# The convention in CosmoHammer is to have the spectra in units
# microK^2 and starting from ell=2
ctx.add(CL_TT_KEY, powers[2:,0])
ctx.add(CL_TE_KEY, powers[2:,3])
ctx.add(CL_EE_KEY, powers[2:,1])
ctx.add(CL_BB_KEY, powers[2:,2])
except camb.baseconfig.CAMBError:
getLogger().warn("CAMBError catched. Used params [%s]"%( ", ".join([str(i) for i in p]) ) )
raise LikelihoodComputationException()
def theoryCls(self, LMAX):
"""get the theoretical :math:`C_l` values for the current cosmology using CLASS code.
The cosmological parameters are set from the current :class:`.cosmology`.
The power sepctra are also set as variables in the cosmology class.
If the include_polarization switch is set to True, then it also sets
"""
# using camb
pars = camb.CAMBparams()
pars.set_cosmology(H0=self.cosmology.H0, ombh2=self.cosmology.Ob0*self.cosmology.h**2.0,
omch2=self.cosmology.Oc0*self.cosmology.h**2.0, omk=0,
tau=self.cosmology.tau, mnu=self.cosmology.m_nu[-1])
pars.InitPower.set_params(As=self.cosmology.As, ns=self.cosmology.n, r=self.cosmology.r)
pars.set_for_lmax(LMAX)
if (self.cosmology.r > 0.0):
pars.WantTensors = True
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(pars)
totCL = powers['total']
self.cosmology.TTCls = totCL[:LMAX+1,0]
self.cosmology.ells=np.arange(LMAX+1)
if (self.include_polarization):
self.cosmology.TECls = totCL[:LMAX+1,3]
self.cosmology.BBCls = totCL[:LMAX+1,2]
self.cosmology.EECls = totCL[:LMAX+1,1]
return self.cosmology.TTCls
def getHubbleCosmology(theta,params,precision = 0.00001,H0init = 70.,H0max = 100.,H0min = 40.):
'''
Finds H0 for given theta and other cosmo params{} using a bisection search
'''
H0 = H0init
err = precision*2.
pars = camb.CAMBparams()
pars.set_accuracy(AccuracyBoost=2.0, lSampleBoost=2.0, lAccuracyBoost=2.0)
#pars.set_accuracy(AccuracyBoost=3.0, lSampleBoost=3.0, lAccuracyBoost=3.0)
pars.set_dark_energy(w=params['w'])
pars.InitPower.set_params(As=params['As'],ns=params['ns'],r=params['r'])
#no need for this: pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=1, max_eta_k=2*params['lmax'])
print "Searching for H0 through bisection..."
j = 0
while np.abs(err)>precision:
pars.set_cosmology(H0=H0, ombh2=params['ombh2'], omch2=params['omch2'], tau=params['tau'],mnu=params['mnu'],nnu=params['nnu'],omk=params['omk'])
results = camb.get_results(pars)
HundredTheta = 100.*results.cosmomc_theta()
err = HundredTheta-theta
if err<0.:
H0min = H0
H0 = (H0+H0max)/2.
elif err>0.:
H0max = H0
H0 = (H0+H0min)/2.
j+=1
print "Found H0 = ", H0, " in ", j , " iterations."
return H0
def testDarkEnergy(self):
pars = camb.CAMBparams()
pars.set_cosmology(H0=71)
pars.InitPower.set_params(ns=0.965, r=0)
for m in ['fluid', 'ppf']:
pars.set_dark_energy(w=-0.7, wa=0.2, dark_energy_model=m)
C1 = camb.get_results(pars).get_cmb_power_spectra()
a = np.logspace(-5, 0, 1000)
w = -0.7 + 0.2 * (1 - a)
pars2 = pars.copy()
pars2.set_dark_energy_w_a(a, w, dark_energy_model=m)
C2 = camb.get_results(pars2).get_cmb_power_spectra()
for f in ['lens_potential', 'lensed_scalar']:
self.assertTrue(np.allclose(C1[f][2:, 0], C2[f][2:, 0]))
pars3 = pars2.copy()
self.assertAlmostEqual(-0.7, pars3.DarkEnergy.w)
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)
cls = data.get_cmb_power_spectra(pars)
cls_tot = data.get_total_cls(2000)
cls_scal = data.get_unlensed_scalar_cls(2000)
cls_tensor = data.get_tensor_cls(2000)
cls_lensed = data.get_lensed_scalar_cls(2000)
cls_phi = data.get_lens_potential_cls(2000)
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], 232.08,1)
def testSources(self):
from camb.sources import GaussianSourceWindow, SplinedSourceWindow
pars = camb.CAMBparams()
pars.set_cosmology(H0=64, mnu=0)
pars.set_for_lmax(1200)
pars.Want_CMB = False
pars.SourceWindows = [
GaussianSourceWindow(redshift=0.17, source_type='counts', bias=1.2, sigma=0.04, dlog10Ndm=-0.2),
GaussianSourceWindow(redshift=0.5, source_type='lensing', sigma=0.07, dlog10Ndm=0)]
pars.SourceTerms.limber_windows = True
results = camb.get_results(pars)
cls = results.get_source_cls_dict()
zs = np.arange(0, 0.5, 0.02)
W = np.exp(-(zs - 0.17) ** 2 / 2 / 0.04 ** 2) / np.sqrt(2 * np.pi) / 0.04
pars.SourceWindows[0] = SplinedSourceWindow(bias=1.2, dlog10Ndm=-0.2, z=zs, W=W)
results = camb.get_results(pars)
cls2 = results.get_source_cls_dict()
self.assertTrue(np.allclose(cls2["W1xW1"][2:1200], cls["W1xW1"][2:1200], rtol=1e-3))
pars.SourceWindows = [GaussianSourceWindow(redshift=1089, source_type='lensing', sigma=30)]
results = camb.get_results(pars)
cls = results.get_source_cls_dict()
PP = cls['PxP']
ls = np.arange(0, PP.shape[0])
self.assertTrue(np.allclose(PP / 4 * (ls * (ls + 1)), cls['W1xW1'], rtol=1e-3))
self.assertTrue(np.allclose(PP / 2 * np.sqrt(ls * (ls + 1)), cls['PxW1'], rtol=1e-3))
# test something sharp with redshift distortions (tricky..)
from scipy import signal
zs = np.arange(1.9689, 2.1057, (2.1057 - 1.9689) / 2000)
W = signal.tukey(len(zs), alpha=0.1)
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122)
pars.InitPower.set_params(As=2e-9, ns=0.965)
pars.set_for_lmax(4000)
pars.SourceWindows = [SplinedSourceWindow(z=zs, W=W, source_type='counts')]
pars.SourceTerms.counts_redshift = True
results = camb.get_results(pars)
cls = results.get_source_cls_dict()
self.assertAlmostEqual(np.sum(cls['PxW1'][10:3000:20]), 0.00020001, places=5)
self.assertAlmostEqual(np.sum(cls['W1xW1'][10:3000:20]), 2.26348, places=3)
self.assertAlmostEqual(np.sum(cls['W1xW1'][10]), 0.0001097, places=6)
def __call__(self, fit_params):
self.pars.set_cosmology(H0=fit_params['H0'],
ombh2=fit_params['ombh2'],
omch2=fit_params['omch2'],
tau=fit_params['tau']
)
self.pars.InitPower.set_params(As=fit_params['As'],
ns=fit_params['ns'])
powers = camb.get_results(self.pars).get_cmb_power_spectra(self.pars)
return powers['lensed_scalar'][:self.lmax,0]*(1e6)**2 * 2.725**2
def __call__(self, fit_params):
self.pars.set_cosmology(H0=None, cosmomc_theta=fit_params['theta']/100.,
ombh2=fit_params['ombh2'],
omch2=fit_params['ommh2']-fit_params['ombh2'],
tau=fit_params['tau']
)
self.pars.InitPower.set_params(As=fit_params['clamp']*1e-9*exp(2*fit_params['tau']),
ns=fit_params['ns'])
powers = camb.get_results(self.pars).get_cmb_power_spectra(self.pars)
return powers['lensed_scalar'][:self.lmax,0]*(1e6)**2 * 2.725**2
def PKL_Camb_SingleRedshift(self, kk, zz=0.0): pars = camb.CAMBparams() pars.set_cosmology(H0=self.h*100, ombh2=self.Omega_b*self.h**2, \ omch2=(self.Omega_m-self.Omega_b)*self.h**2) pars.set_dark_energy(w=self.w0) #re-set defaults pars.InitPower.set_params(ns=self.n_s) pars.set_matter_power(redshifts=[zz], kmax=10.0) results = camb.get_results(pars) kh, z, pk = \ results.get_matter_power_spectrum(minkh=1e-4,maxkh=10.0,npoints=500) s8 = np.array(results.get_sigma8()) pklin = pk[0]*(self.Sigma_8/s8)**2 return np.interp(kk, kh, pklin)
def get_nonlin_power(self, z=0., KMAX=2.0, nl=True):
self.cambparams.set_matter_power(redshifts=[z], kmax=KMAX)
if (nl):
self.cambparams.NonLinear = camb.model.NonLinear_both
else:
self.cambparams.NonLinear = camb.model.NonLinear_none
self.cambresults = camb.get_results(self.cambparams)
kh_nonlin, z_nonlin, pk_nonlin = self.cambresults.get_matter_power_spectrum(minkh=1E-5, maxkh=1.5, npoints=500)
# now that we have the power spectrum; interpolate
kh_lin, z_lin, pk_lin = self.cambresults.get_linear_matter_power_spectrum()
self.camb_power_nonlin = interp1d(kh_nonlin, pk_nonlin[0,:])
self.camb_power_lin = interp1d(kh_lin, pk_lin[0,:])
return self.camb_power_nonlin
def getPowerCamb(params,spec='lensed_scalar',AccuracyBoost=False):
'''
Get Cls from CAMB
'''
pars = camb.CAMBparams()
if AccuracyBoost:
#pars.set_accuracy(AccuracyBoost=2.0, lSampleBoost=2.0, lAccuracyBoost=2.0)
pars.set_accuracy(AccuracyBoost=3.0, lSampleBoost=3.0, lAccuracyBoost=3.0)
pars.set_cosmology(H0=params['H0'], ombh2=params['ombh2'], omch2=params['omch2'], tau=params['tau'],mnu=params['mnu'],nnu=params['nnu'],omk=params['omk'],num_massive_neutrinos=int(params['num_massive_neutrinos']),TCMB=params['TCMB'])
pars.set_dark_energy(w=params['w'])
pars.InitPower.set_params(As=params['As'],ns=params['ns'],r=params['r'],pivot_scalar=params['s_pivot'], pivot_tensor=params['t_pivot'])
pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=1, max_eta_k=2*params['lmax'])
#pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=2.0, max_eta_k=50000.0)
pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=3.0, max_eta_k=50*params['lmax'])
#WantTensors
if params['r']:
pars.WantTensors = True
results = camb.get_results(pars)
powers =results.get_cmb_power_spectra(pars)
if spec=='tensor':
CL = results.get_tensor_cls(int(params['lmax']))*1.e12*params['TCMB']**2.
else:
CL=powers[spec]*1.e12*params['TCMB']**2.
lensArr = results.get_lens_potential_cls(lmax=int(params['lmax']))
clphi = lensArr[:,0]
cltphi = lensArr[:,1]
j=0.
CLS = []
# numpify this!
for row in CL:
if j>params['lmax']: break
raw = row*2.*np.pi/(j*(j+1.))
rawkk = clphi[j]* (2.*np.pi/4.)
#rawkt = cltphi[j]*2.*np.pi/4.
# correct KT (l^2*ClphiT and dimensional - uK)
rawkt = cltphi[j]* (2.*np.pi/2.) / np.sqrt(j*(j+1.)) * params['TCMB']*1.e6
CLS.append(np.append(raw,(rawkk,rawkt)))
j+=1.
CLS = np.nan_to_num(np.array(CLS))
return CLS
def test_nonlin_camb_power():
import camb
logT_AGN = 7.93
Omega_c = 0.25
Omega_b = 0.05
A_s = 2.1e-9
n_s = 0.97
h = 0.7
# Needs to be set for good agreements between CCL and CAMB
T_CMB = 2.725
p = camb.CAMBparams(WantTransfer=True,
NonLinearModel=camb.nonlinear.Halofit(
halofit_version="mead2020_feedback",
HMCode_logT_AGN=logT_AGN))
# This affects k_min
p.WantCls = False
p.DoLensing = False
p.Want_CMB = False
p.Want_CMB_lensing = False
p.Want_cl_2D_array = False
p.set_cosmology(H0=h*100, omch2=Omega_c*h**2, ombh2=Omega_b*h**2,
mnu=0.0, TCMB=T_CMB)
p.share_delta_neff = False
p.InitPower.set_params(As=A_s, ns=n_s)
z = [0.0, 0.5, 1.0, 1.5]
p.set_matter_power(redshifts=z, kmax=10.0, nonlinear=True)
p.set_for_lmax(5000)
r = camb.get_results(p)
k, z, pk_nonlin_camb = r.get_nonlinear_matter_power_spectrum(
hubble_units=False, k_hunit=False)
ccl_cosmo = ccl.Cosmology(
Omega_c=Omega_c, Omega_b=Omega_b, h=h, m_nu=0.0,
A_s=A_s, n_s=n_s,
transfer_function="boltzmann_camb",
matter_power_spectrum="camb",
extra_parameters={"camb": {"halofit_version": "mead2020_feedback",
"HMCode_logT_AGN": logT_AGN}})
for z_, pk_camb in zip(z, pk_nonlin_camb):
pk_nonlin_ccl = ccl.nonlin_matter_power(ccl_cosmo, k, 1/(1+z_))
assert np.allclose(pk_camb, pk_nonlin_ccl, rtol=3e-5)
def get_power_spectrum(h,
omc,
omb: float = 0.0486,
redshift: float = 0,
mode: str = 'linear',
nkpoints: int = 200) -> Tuple[np.ndarray, np.ndarray]:
"""
Use CAMB to compute the power spectrum for the given parameters.
Args:
h: The reduced Hubble constant: h = H0 / (100 km / s / Mpc).
omc: Dark matter density parameter.
omb: Baryon density parameter.
redshift: Redshift at which to compute the power spectrum.
mode: Use linear Perturbation Theory or non-linear one.
nkpoints: Number of points (in k-space) of the spectrum.
Returns:
A tuple (kh, pk), where kh is an array of k/h, and pk[j] is the
value of the power spectrum at k/h[j] (for the given redshift).
"""
# Convert input parameters to quantities used for simulation
H0 = 100. * h
ombh2 = omb * h**2
omch2 = omc * h**2
# Set up a CAMB object and define the cosmology
pars = camb.CAMBparams()
pars.set_cosmology(H0=H0, ombh2=ombh2, omch2=omch2)
pars.InitPower.set_params()
pars.set_matter_power(redshifts=[redshift], kmax=2.0)
if mode == 'linear':
pars.NonLinear = model.NonLinear_none
elif mode == 'non-linear':
pars.NonLinear = model.NonLinear_both
else:
raise ValueError('mode must be either "linear" or "non-linear"!')
results = camb.get_results(pars)
results.calc_power_spectra(pars)
kh, z, pk = \
results.get_matter_power_spectrum(minkh=1e-2,
maxkh=1,
npoints=nkpoints)
return kh, pk[0].ravel() # 0 for only one redshift
def test_hillipop(self):
import camb
import planck_2020_hillipop
camb_cosmo = cosmo_params.copy()
camb_cosmo.update({"lmax": 2500, "lens_potential_accuracy": 1})
pars = camb.set_params(**camb_cosmo)
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(pars, CMB_unit="muK")
cl_dict = {k: powers["total"][:, v] for k, v in {"tt": 0, "ee": 1, "te": 3}.items()}
for mode, chi2 in chi2s.items():
_hlp = getattr(planck_2020_hillipop, mode)
my_lik = _hlp({"packages_path": packages_path})
loglike = my_lik.loglike(cl_dict, **{**calib_params, **nuisance_params[mode]})
self.assertLess( abs(-2 * loglike - chi2), 1)
def test_lollipop(self):
import camb
import planck_2020_lollipop
camb_cosmo = cosmo_params.copy()
camb_cosmo.update({"lmax": 145, "lens_potential_accuracy": 1})
pars = camb.set_params(**camb_cosmo)
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(pars, CMB_unit="muK", raw_cl=True)
cl_dict = {k: powers["total"][:, v] for k, v in {"ee": 1, "bb": 2}.items()}
for mode, chi2 in chi2s.items():
_llp = getattr(planck_2020_lollipop, mode)
my_lik = _llp({"packages_path": packages_path})
loglike = my_lik.loglike(cl_dict, **{})
self.assertLess( abs(-2 * loglike - chi2), 1)
def test_memory(self):
if platform.system() != "Windows":
import gc
import resource
last_usage = -1
for i in range(3):
pars = camb.CAMBparams()
pars.set_cosmology(H0=70, ombh2=0.022, omch2=0.12, mnu=0.06, omk=0, tau=0.17)
results = camb.get_results(pars)
del pars, results
gc.collect()
usage = round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.0, 1)
if 0 < last_usage != usage:
print('Memory usage: %2.2f KB vs %2.2f KB' % (usage, last_usage))
raise Exception("Apparent memory leak")
last_usage = usage
def get_spectrum(pars,lmax=2000):
H0=pars[0]
ombh2=pars[1]
omch2=pars[2]
As=pars[3]
ns=pars[4]
tau=pars[5]
pars=camb.CAMBparams()
pars.set_cosmology(H0=H0,ombh2=ombh2,omch2=omch2,mnu=0.06,omk=0,tau=tau)
pars.InitPower.set_params(As=As,ns=ns,r=0)
pars.set_for_lmax(lmax,lens_potential_accuracy=0)
results=camb.get_results(pars)
powers=results.get_cmb_power_spectra(pars,CMB_unit='muK')
cmb=powers['total']
tt=cmb[:,0] #you could return the full power spectrum here if you wanted to do say EE
return tt
def compute_g_star(cosmo, z_star):
""" Compute logarithmic derivative of Hubble expansion, normalized to EdS:
g(z) = dln H(z) / dln(1+z)^3/2 = 3/2 (1+z)/H(z) dH/dz """
results = camb.get_results(cosmo)
# compute derivative of Hubble
dz = z_star / 100.0
z_minus = z_star - dz
z_plus = z_star + dz
H_minus = results.hubble_parameter(z=z_minus)
H_plus = results.hubble_parameter(z=z_plus)
dHdz = (H_plus - H_minus) / (z_plus - z_minus)
# compute hubble at z_star, and return g(z_star)
H_star = results.hubble_parameter(z=z_star)
g_star = dHdz / H_star * (1 + z_star) * 2 / 3
return g_star
def get_galaxy_power(l,z=0.7,b=1.):
lmax = np.max([np.max(l),2000])
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122)
pars.InitPower.set_params(As=2e-9, ns=0.965)
pars.set_for_lmax(lmax, lens_potential_accuracy=1)
pars.Want_CMB = False
pars.NonLinear = model.NonLinear_both
pars.SourceWindows = [GaussianSourceWindow(redshift=0.7, source_type='counts', sigma=0.1,dlog10Ndm=-0.2, bias = 1.0)]
results = camb.get_results(pars)
cls = results.get_source_cls_dict()
l_model = np.arange(2,lmax+1)
cl_model = cls['W1xW1'][2:lmax+1]
# Interpolate this onto the provided grid.
f = interp1d(l_model,cl_model,kind='cubic')
return f(l)
def load_theory(pars, lpad=9000):
'''
All ell and 2pi factors are stripped off.
'''
import camb
from camb import model
uSuffix = "unlensed_total"
lSuffix = "total"
results = camb.get_results(pars)
cmbmat = results.get_cmb_power_spectra(
pars,
lmax=lpad,
spectra=['total', 'unlensed_total', 'lens_potential'],
raw_cl=True,
CMB_unit='muK')
theory = TheorySpectra()
for i, pol in enumerate(['TT', 'EE', 'BB', 'TE']):
cls = cmbmat[lSuffix][:, i]
ells = np.arange(0, len(cls), 1)
theory.loadCls(ells,
cls,
pol,
lensed=True,
interporder="linear",
lpad=lpad,
fill_zero=True)
cls = cmbmat[uSuffix][:, i]
ells = np.arange(0, len(cls), 1)
theory.loadCls(ells,
cls,
pol,
lensed=False,
interporder="linear",
lpad=lpad,
fill_zero=True)
lensArr = cmbmat['lens_potential']
cldd = lensArr[:, 0]
ells = np.arange(0, len(cldd), 1)
clkk = cldd.copy()
clkk[1:] = clkk[1:] / 4. * (ells[1:] * (ells[1:] + 1))**2.
theory.loadGenericCls(ells, clkk, "kk", lpad=lpad, fill_zero=True)
theory.dimensionless = False
return theory
def getHubbleCosmology(theta,
params,
precision=0.00001,
H0init=70.,
H0max=100.,
H0min=40.):
'''
Finds H0 for given theta and other cosmo params{} using a bisection search
'''
H0 = H0init
err = precision * 2.
pars = camb.CAMBparams()
pars.set_accuracy(AccuracyBoost=2.0, lSampleBoost=2.0, lAccuracyBoost=2.0)
#pars.set_accuracy(AccuracyBoost=3.0, lSampleBoost=3.0, lAccuracyBoost=3.0)
pars.set_dark_energy(w=params['w'],
wa=params['wa'],
dark_energy_model='ppf')
pars.InitPower.set_params(As=params['As'], ns=params['ns'], r=params['r'])
#no need for this: pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=1, max_eta_k=2*params['lmax'])
print "Searching for H0 through bisection..."
j = 0
while np.abs(err) > precision:
pars.set_cosmology(H0=H0,
ombh2=params['ombh2'],
omch2=params['omch2'],
tau=params['tau'],
mnu=params['mnu'],
nnu=params['nnu'],
omk=params['omk'])
results = camb.get_results(pars)
HundredTheta = 100. * results.cosmomc_theta()
err = HundredTheta - theta
if err < 0.:
H0min = H0
H0 = (H0 + H0max) / 2.
elif err > 0.:
H0max = H0
H0 = (H0 + H0min) / 2.
j += 1
print "Found H0 = ", H0, " in ", j, " iterations."
return H0
def get_spectrum(pars,lmax=2000):
#print('pars are ',pars)
H0=pars[0] #Hubble Constant
ombh2=pars[1] #Physical Baryon Density
omch2=pars[2] #Cold Dark Matter Density
As=pars[3] #Primordial Amplitude of Fluctuations
ns=pars[4] #Slope of the Primordial Power Law
tau=pars[5] #Optical Depth
pars=camb.CAMBparams()
pars.set_cosmology(H0=H0,ombh2=ombh2,omch2=omch2,mnu=0.06,omk=0,tau=tau)
pars.InitPower.set_params(As=As,ns=ns,r=0)
pars.set_for_lmax(lmax,lens_potential_accuracy=0)
results=camb.get_results(pars)
powers=results.get_cmb_power_spectra(pars,CMB_unit='muK')
cmb=powers['total']
tt=cmb[:,0] #you could return the full power spectrum here if you wanted to do say EE
return tt
def _generate_data(self):
self.logger.info(
f"Generating CAMB data with {self.om_resolution} x {self.h0_resolution}"
)
os.makedirs(self.data_dir, exist_ok=True)
import camb
pars = camb.CAMBparams()
pars.set_dark_energy(w=-1.0, dark_energy_model="fluid")
pars.InitPower.set_params(As=2.130e-9, ns=self.ns)
pars.set_matter_power(redshifts=[self.redshift, 0.0001],
kmax=self.k_max)
self.logger.info("Configured CAMB power and dark energy")
data = np.zeros(
(self.om_resolution, self.h0_resolution, 1 + 3 * self.k_num))
for i, omch2 in enumerate(self.omch2s):
for j, h0 in enumerate(self.h0s):
self.logger.debug("Generating %d:%d %0.3f %0.3f" %
(i, j, omch2, h0))
pars.set_cosmology(
H0=h0 * 100,
omch2=omch2,
mnu=0.0,
ombh2=self.omega_b * h0 * h0,
omk=0.0,
tau=0.063,
neutrino_hierarchy="degenerate",
num_massive_neutrinos=1,
)
pars.NonLinear = camb.model.NonLinear_none
results = camb.get_results(pars)
params = results.get_derived_params()
rdrag = params["rdrag"]
kh, z, pk_lin = results.get_matter_power_spectrum(
minkh=self.k_min, maxkh=self.k_max, npoints=self.k_num)
pars.NonLinear = camb.model.NonLinear_pk
results.calc_power_spectra(pars)
kh, z, pk_nonlin = results.get_matter_power_spectrum(
minkh=self.k_min, maxkh=self.k_max, npoints=self.k_num)
data[i, j, 0] = rdrag
data[i, j, 1:1 + self.k_num] = pk_lin[1, :]
data[i, j, 1 + self.k_num:] = pk_nonlin.flatten()
self.logger.info(f"Saving to {self.filename}")
np.save(self.filename, data)
return data
def get_spectrum(pars,y,lmax=2000):
#print('pars are ',pars)
H0=pars[0]
ombh2=pars[1]
omch2=pars[2]
tau=pars[3]
As=pars[4]
ns=pars[5]
pars=camb.CAMBparams()
pars.set_cosmology(H0=H0,ombh2=ombh2,omch2=omch2,mnu=0.06,omk=0,tau=tau)
pars.InitPower.set_params(As=As,ns=ns,r=0)
pars.set_for_lmax(lmax,lens_potential_accuracy=0)
results=camb.get_results(pars)
powers=results.get_cmb_power_spectra(pars,CMB_unit='muK')
cmb=powers['total']
tt=cmb[2:len(y)+2,0] #remove first two entries
return tt
def CAMBdemoCl():
"""
example code from http://camb.readthedocs.io/en/latest/CAMBdemo.html
"""
#Set up a new set of parameters for CAMB
#pars = camb.CAMBparams()
#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency
#pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.06, omk=0, tau=0.06)
#pars.InitPower.set_params(ns=0.965, r=0)
pars = getPars()
pars.set_for_lmax(2500, lens_potential_accuracy=0)
# this line for C_l, not P_k
#calculate results for these parameters
results = camb.get_results(pars)
#get dictionary of CAMB power spectra
powers = results.get_cmb_power_spectra(pars)
for name in powers:
print name
#plot the total lensed CMB power spectra versus unlensed, and fractional difference
totCL = powers['total']
unlensedCL = powers['unlensed_scalar']
print totCL.shape
#Python CL arrays are all zero based (starting at L=0), Note L=0,1 entries will be zero by default.
#The different CL are always in the order TT, EE, BB, TE (with BB=0 for unlensed scalar results).
ls = np.arange(totCL.shape[0])
fig, ax = plt.subplots(2, 2, figsize=(12, 12))
ax[0, 0].plot(ls, totCL[:, 0], color='k')
ax[0, 0].plot(ls, unlensedCL[:, 0], color='r')
ax[0, 0].set_title('TT')
ax[0, 1].plot(ls[2:], 1 - unlensedCL[2:, 0] / totCL[2:, 0])
ax[0, 1].set_title(r'$\Delta TT$')
ax[1, 0].plot(ls, totCL[:, 1], color='k')
ax[1, 0].plot(ls, unlensedCL[:, 1], color='r')
ax[1, 0].set_title(r'$EE$')
ax[1, 1].plot(ls, totCL[:, 3], color='k')
ax[1, 1].plot(ls, unlensedCL[:, 3], color='r')
ax[1, 1].set_title(r'$TE$')
for ax in ax.reshape(-1):
ax.set_xlim([2, 2500])
plt.show()
def calc_linear(self, z):
self.pars.set_matter_power(
redshifts=[z], kmax=4.0
) ## Note: non-linear corrections couples to smaller scales than you want
self.pars.NonLinear = model.NonLinear_none ## Linear spectrum
self.pkresults = camb.get_results(self.pars)
self.kh, self.z, self.linpk = self.pkresults.get_matter_power_spectrum(
minkh=1e-2, maxkh=2., npoints=200)
self.lpower_interpolatek = interp1d(self.kh,
self.linpk,
kind=splinetype,
bounds_error=False,
fill_value=0.0)
self.sig8 = self.pkresults.get_sigma8()
return self.lpower_interpolatek
def createCldata(oBar):
pars = camb.CAMBparams()
#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency
pars.set_cosmology(H0=67.5,
ombh2=oBar,
omch2=0.122,
mnu=0.06,
omk=0,
tau=0.06)
pars.InitPower.set_params(As=2e-9, ns=0.965, r=0)
pars.set_for_lmax(2500, lens_potential_accuracy=0)
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(pars, CMB_unit='muK')
totCL_new = powers['total']
ls = np.arange(totCL_new.shape[0])
np.savetxt("data/Cl_%.3f.dat" % oBar, (ls, totCL_new[:, 0]))
def get_cmb(params,lmax=2000):
H0,ombh2,omch2,tau,As,ns = params
params = camb.CAMBparams()
params.set_cosmology(H0=H0,ombh2=ombh2,omch2=omch2,mnu=0.06,omk=0,tau=tau)
params.InitPower.set_params(As=As,ns=ns,r=0)
params.set_for_lmax(lmax,lens_potential_accuracy=0)
results = camb.get_results(params)
powers = results.get_cmb_power_spectra(params,CMB_unit='muK')
cmb = powers['total']
cmb = cmb[:,0]
# Remove the monopole and dipole
cmb = cmb[2:]
return cmb[:1199]
def getBAOCamb(zrange,params,AccuracyBoost=False):
'''
Get BAO's fk=rs/D_V from CAMB. Return 1D array of fk for all redshifts z
'''
pars = camb.CAMBparams()
if AccuracyBoost:
pars.set_accuracy(AccuracyBoost=2.0, lSampleBoost=2.0, lAccuracyBoost=2.0)
#pars.set_accuracy(AccuracyBoost=3.0, lSampleBoost=3.0, lAccuracyBoost=3.0)
pars.set_cosmology(H0=params['H0'], ombh2=params['ombh2'], omch2=params['omch2'], tau=params['tau'],mnu=params['mnu'],nnu=params['nnu'],omk=params['omk'])
pars.set_dark_energy(w=params['w'])
pars.InitPower.set_params(As=params['As'],ns=params['ns'],r=params['r'])
#pars.set_for_lmax(lmax=int(params['lmax']), lens_potential_accuracy=1, max_eta_k=2*params['lmax'])
camb.set_z_outputs(zrange)
results = camb.get_results(pars)
fk = results.get_background_outputs() #results.get_BAO(zrange,pars)[:,0]
#print fk
fk = np.nan_to_num(fk[:,0])
return fk
def C_l(r, tau, ps='BB'):
pars.set_cosmology(H0=67.81, ombh2=0.02226, omch2=0.1197, mnu=0.06, omk=0, tau=tau)
pars.InitPower.set_params(ns=0.9677, r=r, As=2.13e-9)
pars.set_for_lmax(2500, lens_potential_accuracy=0)
pars.WantTensors = True
pars.AccurateReionization = 1
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(pars)
cl = powers['total']*(1e6*pars.TCMB)**2 # in uK_CMB^2
if ps == 'EE':
return cl[:200,1]
elif ps == 'BB':
return cl[:200,2]
elif ps == 'EB':
return cl[:200,1], cl[:200,2]
else:
return cl
def generate_cls():
lmin, lmax = 2, 2000 # \ell range where the monopole and dipole are zero
#Set Planck 2018 cosmological parameters
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.66,
ombh2=0.02242,
omch2=0.11933,
mnu=0.120,
nnu=2.99,
omk=0.0007,
tau=0.0561,
YHe=0.242)
pars.InitPower.set_params(As=np.exp(3.047) / 10**10, ns=0.9665)
#Set \ell max again
pars.set_for_lmax(lmax, lens_potential_accuracy=1)
pars.Want_CMB = True #Because we want the CMB
powers = camb.get_results(pars).get_cmb_power_spectra(pars, CMB_unit="muK")
l = np.arange(lmin, lmax)
#cl = powers['total'][lmin:lmax,0]/(l*(l+1))*2*np.pi
# Ordering TT, EE, BB, TE angular power expectra (CL).
# We add 2 zeros because the monopoles and the dipoles are zero
# *WARNING* this is very important, if not we get other simulation complitely different
cl_tt = np.zeros(2).tolist()
cl_tt.extend(
(powers['total'][lmin:lmax, 0] / (l * (l + 1)) * 2 * np.pi).tolist())
cl_tt = np.array(cl_tt) #Simulation of the CMB temperature CL
cl_ee = np.zeros(2).tolist()
cl_ee.extend(
(powers['total'][lmin:lmax, 1] / (l * (l + 1)) * 2 * np.pi).tolist())
cl_ee = np.array(cl_ee) #Simulation of the CMB E polarization CL
cl_bb = np.zeros(2).tolist()
cl_bb.extend(
(powers['total'][lmin:lmax, 2] / (l * (l + 1)) * 2 * np.pi).tolist())
cl_bb = np.array(cl_bb) #Simulation of the CMB B polarization CL
cl_te = np.zeros(2).tolist()
cl_te.extend(
(powers['total'][lmin:lmax, 3] / (l * (l + 1)) * 2 * np.pi).tolist())
cl_te = np.array(cl_te)
return cl_tt
def rspectrum(nu, r, sig, scaling=1.0):
"""
Calculates the CMB amplituded given a value of r and requested modes
"""
import camb
from camb import model, initialpower
import healpy as hp
#Set up a new set of parameters for CAMB
pars = camb.CAMBparams()
#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency
pars.set_cosmology(H0=67.5,
ombh2=0.022,
omch2=0.122,
mnu=0.06,
omk=0,
tau=0.06)
pars.InitPower.set_params(As=2e-9, ns=0.965, r=r)
lmax = 6000
pars.set_for_lmax(lmax, lens_potential_accuracy=0)
pars.WantTensors = True
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(
params=pars,
lmax=lmax,
CMB_unit='muK',
raw_cl=True,
)
l = np.arange(2, lmax + 1)
if sig == "TT":
cl = powers['unlensed_scalar']
signal = 0
elif sig == "EE":
cl = powers['unlensed_scalar']
signal = 1
elif sig == "BB":
cl = powers['tensor']
signal = 2
bl = hp.gauss_beam(40 / (180 / np.pi * 60), lmax, pol=True)
A = np.sqrt(
sum(4 * np.pi * cl[2:, signal] * bl[2:, signal]**2 / (2 * l + 1)))
return cmb(nu, A * scaling)
def a_s_to_sigma_8(cosmology_parameters):
pars = camb.CAMBparams()
pars.set_cosmology(
H0=cosmology_parameters['H0'],
ombh2=cosmology_parameters['Ob0'] * cosmology_parameters['h']**2,
omch2=cosmology_parameters['Oc0'] * cosmology_parameters['h']**2,
omk=0.,
mnu=cosmology_parameters['Mnu'],
standard_neutrino_neff=cosmology_parameters['Neff'],
nnu=cosmology_parameters['Neff'],
TCMB=cosmology_parameters['TCMB'])
pars.InitPower.set_params(ns = cosmology_parameters['ns'], \
As = cosmology_parameters['As'], \
pivot_scalar = cosmology_parameters['pivot_scalar'])
pars.NonLinear = model.NonLinear_none
pars.set_matter_power(redshifts=[0.], kmax=20.)
results = camb.get_results(pars)
return results.get_sigma8()[0]
def __init__(self, cosmo, sips, kmax=200., npoints=350, nonlinear=True,
zvec=zvec_default):
try:
import camb
except:
print "camb (http://camb.info/) could not be loaded. is it installed?"
raise
self.cosmo = cosmo
# Define the model parameters
par = camb.model.CAMBparams()
par.H0 = cosmo.H0
par.omegab = cosmo.omb
par.omegac = cosmo.omc
par.omegav = cosmo.oml
par.NonLinear = nonlinear
par.InitPower.set_params(As=sips.amp,
ns=sips.n_s,
nrun=sips.n_r,
pivot_scalar=sips.k_pivot)
par.set_matter_power(redshifts=zvec, kmax=kmax)
self.par = par
# Calculate the matter power spectrum
camb_data = camb.get_results(par)
p_kz = camb_data.get_matter_power_spectrum(minkh=1e-6, maxkh=kmax, npoints=npoints)
kh, z, pk = camb_data.get_matter_power_spectrum(minkh=1e-6, maxkh=kmax, npoints=npoints)
self.arr_z = z
self.arr_k = kh * (self.cosmo.h)
self.mat_p = pk / (self.cosmo.h)**3
for iz, z in enumerate(self.arr_z):
self.mat_p[iz, :] *= (1. + z)**2
self.spl_p = scipy.interpolate.RectBivariateSpline(self.arr_z, np.log(self.arr_k), self.mat_p, kx=3, ky=3, s=0)
self.kmin = self.arr_k[+0] * 0.999
self.kmax = self.arr_k[-1] * 1.001
self.zmin = np.min(zvec)
self.zmax = np.max(zvec)
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 get_bao_dr12(params=None,engine='camb',de='ppf',rs_fid = 147.78,zs=[0.38,0.51,0.61]):
import camb
params = map_params(params,engine=engine)
zs = np.asarray(zs)
if engine=='camb':
pars = set_camb_pars(params=params,de=de)
results = camb.get_results(pars)
rdrag = results.get_derived_params()['rdrag']
rs_rescale = 1 / rs_fid
rs = rdrag * rs_rescale
retdict = {}
retdict["DM_over_rs"] = np.zeros((zs.size,))
retdict["bao_Hz_rs"] = np.zeros((zs.size,))
for i,z in enumerate(zs):
retdict["DM_over_rs"][i] = (1 + z) * results.angular_diameter_distance(z) / rs
retdict["bao_Hz_rs"][i] = results.hubble_parameter(z) * rs
else:
raise NotImplementedError
return zs,retdict
def matterps2corr2esd(redshift, cosmology):
z = redshift
H_null, Ombh2, Omch2 = cosmology
pars = camb.CAMBparams()
pars.set_cosmology(H0=H_null, ombh2=Ombh2, omch2=Omch2)
pars.set_dark_energy()
pars.InitPower.set_params(ns=nps)
pars.set_matter_power(redshifts=[0.0, z], kmax=2.0)
#Linear spectra--------
pars.NonLinear = model.NonLinear_none
results = camb.get_results(pars)
kh, znon, pk = results.get_matter_power_spectrum(minkh=1e-4,
maxkh=1000.0,
npoints=1024)
xi = mcfit.P2xi(kh, l=0)
#Calculate corr from PS------
nxx = 50
Rmin = -2.0
Rmax = 2.5
rr = np.logspace(Rmin, Rmax, nxx)
rx, corrfunc = xi(pk, extrap=True)
corr = np.interp(rr, rx, corrfunc[0, :])
#Calculate ESD from corr------
nxx = 300
Rp = np.linspace(0.1, 300., nxx)
ESD = np.zeros(nxx)
for i in range(nxx):
tmp1 = integrate.quad(simR,
Rp[i] + 0.001,
90.0,
args=(rr, corr, Rp[i]),
epsabs=1e-4)
tmp2 = integrate.quad(simLR,
0.001,
Rp[i],
args=(rr, corr, Rp[i]),
epsabs=1e-4)
ESD[i] = (4.0 * tmp2[0] / Rp[i] / Rp[i] - 2.0 *
tmp1[0]) * omega_m * h #Luo et al 2017ApJ 836 38L EQ. 39-40
return {'Rp': Rp, 'ESD': ESD}
def getcorrCAMB(theta, data, centers):
Nz, be = np.histogram(data['z'], bins=8, range=(0.05, 0.15))
lmax = 3750
pars = camb.CAMBparams() # Set up the CAMB parameters
h = 0.675 # Planck value for h (Hubble parameter)
Ob = 0.044 # Planck value for Omega_b (Baryon energy density)
Om = theta[1] # Planck value for Omega_m (Matter energy density)
Oc = Om - Ob # Value for Omega_c (Cold dark matter energy density)
As = 2e-9 # Amplitude of initial fluctuations
ns = 0.965 # Scalar index
pars.set_cosmology(H0=100 * h, ombh2=Ob * h**2, omch2=Oc *
h**2) # This sets the cosmological parameters
pars.InitPower.set_params(
As=As, ns=ns) # This also sets the cosmological parameters
pars.set_for_lmax(lmax, lens_potential_accuracy=1) # Set the maximum ell
#set Want_CMB to true if you also want CMB spectra or correlations
pars.Want_CMB = False # We don't want the CMB
#NonLinear_both or NonLinear_lens will use non-linear corrections
pars.NonLinear = model.NonLinear_both # We want non-linear corrections
#Set up W(z) window functions, later labelled W1, W2.
zs = 0.5 * (be[1:] + be[:-1]) #z # Range of zs
W = Nz # Window function
pars.SourceWindows = [
SplinedSourceWindow(source_type='counts', bias=theta[0], z=zs, W=W)
] # Set up the window function
results = camb.get_results(pars)
cls = results.get_source_cls_dict()
ls = np.arange(2, lmax + 1)
angles = centers #np.logspace(-2, 1) # Angles from 0.01 to 10 deg
x = np.cos(np.radians(
angles)) # Convert them to radians and compute cosine to passs to CAMB
cls_in = np.array([
cls['W1xW1'][1:lmax + 1],
np.zeros(lmax),
np.zeros(lmax),
np.zeros(lmax)
]).T
#cl2corr needs TT (temperature/density), EE (E-mode), BB (B-mode), TE (Temperature-polarization cross correlation) -> we only care about TT
w_camb = camb.correlations.cl2corr(cls_in, x)
return w_camb
def get_cmb( Nside, Npix ):
pars = camb.CAMBparams()
#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency
pars.set_cosmology( H0 = 67.74, ombh2 = 0.0223, omch2 = 0.1188, omk = 0, tau = 0.066 ) ;
# Tensors on
pars.WantTensors = True
pars.InitPower.set_params( ns = 0.9667, r = 0.5 ) ;
# Power spectrum will be 3 * Nside - 1
pars.set_for_lmax( 3 * Nside - 1, lens_potential_accuracy=0);
#calculate results for these parameters
results = camb.get_results(pars) ;
#get dictionary of CAMB power spectra
cl_total = results.get_total_cls( 3 * Nside - 1 )
# Needs to be re-arranged
tmp = [ cl_total[ :, 0 ] , cl_total[ :, 1 ], cl_total[ :, 2 ], cl_total[ :, 3 ] ]
cmb_synfast = hp.synfast( tmp, Nside, new = True )
# K->micro K!
cmb_out = [ cmb_synfast[ 0 ][ Npix ] * 1e6, cmb_synfast[ 1 ][ Npix ] * 1e6, cmb_synfast[ 2 ][ Npix ] * 1e6 ]
return cmb_out
def A(x, pars, lmax=2000):
print(pars)
# Gets the spectrum for a set of parameters
H0, ombh2, omch2, As, ns, tau = pars
pars = camb.CAMBparams()
pars.set_cosmology(H0=H0,
ombh2=ombh2,
omch2=omch2,
mnu=0.06,
omk=0,
tau=tau)
pars.InitPower.set_params(As=As, ns=ns, r=0)
pars.set_for_lmax(lmax, lens_potential_accuracy=0)
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(pars, CMB_unit='muK')
cmb = powers['total']
tt = cmb[:, 0]
print('done')
return tt[x.astype(int)]
def testSymbolic(self):
if fast:
return
import camb.symbolic as s
monopole_source, ISW, doppler, quadrupole_source = s.get_scalar_temperature_sources()
temp_source = monopole_source + ISW + doppler + quadrupole_source
pars = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.95, omk=0.1)
data = camb.get_background(pars)
tau = np.linspace(1, 1200, 300)
ks = [0.001, 0.05, 1]
monopole2 = s.make_frame_invariant(s.newtonian_gauge(monopole_source), 'Newtonian')
Delta_c_N = s.make_frame_invariant(s.Delta_c, 'Newtonian')
Delta_c_N2 = s.make_frame_invariant(s.synchronous_gauge(Delta_c_N), 'CDM')
ev = data.get_time_evolution(ks, tau, ['delta_photon', s.Delta_g, Delta_c_N, Delta_c_N2,
monopole_source, monopole2,
temp_source, 'T_source'])
self.assertTrue(np.allclose(ev[:, :, 0], ev[:, :, 1]))
self.assertTrue(np.allclose(ev[:, :, 2], ev[:, :, 3]))
self.assertTrue(np.allclose(ev[:, :, 4], ev[:, :, 5]))
self.assertTrue(np.allclose(ev[:, :, 6], ev[:, :, 7]))
pars = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.95)
pars.set_accuracy(lSampleBoost=2)
try:
pars.set_custom_scalar_sources([monopole_source + ISW + doppler + quadrupole_source,
s.scalar_E_source], source_names=['T2', 'E2'],
source_ell_scales={'E2': 2})
data = camb.get_results(pars)
dic = data.get_cmb_unlensed_scalar_array_dict(CMB_unit='muK')
self.assertTrue(np.all(np.abs(dic['T2xT2'][2:2000] / dic['TxT'][2:2000] - 1) < 1e-3))
self.assertTrue(np.all(np.abs(dic['TxT2'][2:2000] / dic['TxT'][2:2000] - 1) < 1e-3))
# default interpolation errors much worse for E
self.assertTrue(np.all(np.abs(dic['E2xE2'][10:2000] / dic['ExE'][10:2000] - 1) < 2e-3))
self.assertTrue(np.all(np.abs(dic['E2xE'][10:2000] / dic['ExE'][10:2000] - 1) < 2e-3))
dic1 = data.get_cmb_power_spectra(CMB_unit='muK')
self.assertTrue(np.allclose(dic1['unlensed_scalar'][2:2000, 1], dic['ExE'][2:2000]))
finally:
pars.set_accuracy(lSampleBoost=1)
s.internal_consistency_checks()
def test_quintessence(self):
n = 3
# set zc and fde_zc
pars = camb.set_params(ombh2=0.022,
omch2=0.122,
thetastar=0.01044341764253,
dark_energy_model='EarlyQuintessence',
m=8e-53,
f=0.05,
n=n,
theta_i=3.1,
use_zc=True,
zc=1e4,
fde_zc=0.1)
camb.get_background(pars)
results = camb.get_results(pars)
self.assertAlmostEqual(results.get_derived_params()['thetastar'],
1.044341764253,
delta=1e-5)
def get_camb_results(pars,zs=None,fast_camb=False):
"""Call camb.get_results, the slowest function in CAMB calls.
- pars: CAMBparams object describing the cosmology
- zs (optional): redshifts where we want to evaluate the linear power
- fast_camb (optional): tune settings for fast computations"""
if zs is not None:
# check if we want to use fast version
if fast_camb:
set_fast_camb_options(pars)
kmax_Mpc=camb_fit_kmax_Mpc
else:
kmax_Mpc=camb_kmax_Mpc
# camb_extra_kmax will allow to evaluate the power later on at kmax
pars.set_matter_power(redshifts=zs,kmax=camb_extra_kmax*kmax_Mpc,
nonlinear=False,silent=True)
return camb.get_results(pars)
def testSymbolic(self):
if fast: return
import camb.symbolic as s
monopole_source, ISW, doppler, quadrupole_source = s.get_scalar_temperature_sources()
temp_source = monopole_source + ISW + doppler + quadrupole_source
pars = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.95, omk=0.1)
data = camb.get_background(pars)
tau = np.linspace(1, 1200, 300)
ks = [0.001, 0.05, 1]
monopole2 = s.make_frame_invariant(s.newtonian_gauge(monopole_source), 'Newtonian')
Delta_c_N = s.make_frame_invariant(s.Delta_c, 'Newtonian')
Delta_c_N2 = s.make_frame_invariant(s.synchronous_gauge(Delta_c_N), 'CDM')
ev = data.get_time_evolution(ks, tau, ['delta_photon', s.Delta_g, Delta_c_N, Delta_c_N2,
monopole_source, monopole2,
temp_source, 'T_source'])
self.assertTrue(np.allclose(ev[:, :, 0], ev[:, :, 1]))
self.assertTrue(np.allclose(ev[:, :, 2], ev[:, :, 3]))
self.assertTrue(np.allclose(ev[:, :, 4], ev[:, :, 5]))
self.assertTrue(np.allclose(ev[:, :, 6], ev[:, :, 7]))
pars = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.95)
pars.set_accuracy(lSampleBoost=2)
try:
pars.set_custom_scalar_sources([monopole_source + ISW + doppler + quadrupole_source,
s.scalar_E_source], source_names=['T2', 'E2'],
source_ell_scales={'E2': 2})
data = camb.get_results(pars)
dic = data.get_cmb_unlensed_scalar_array_dict(CMB_unit='muK')
self.assertTrue(np.all(np.abs(dic['T2xT2'][2:2000] / dic['TxT'][2:2000] - 1) < 1e-3))
self.assertTrue(np.all(np.abs(dic['TxT2'][2:2000] / dic['TxT'][2:2000] - 1) < 1e-3))
# default interpolation errors much worse for E
self.assertTrue(np.all(np.abs(dic['E2xE2'][10:2000] / dic['ExE'][10:2000] - 1) < 2e-3))
self.assertTrue(np.all(np.abs(dic['E2xE'][10:2000] / dic['ExE'][10:2000] - 1) < 2e-3))
dic1 = data.get_cmb_power_spectra(CMB_unit='muK')
self.assertTrue(np.allclose(dic1['unlensed_scalar'][2:2000, 1], dic['ExE'][2:2000]))
finally:
pars.set_accuracy(lSampleBoost=1)
s.internal_consistency_checks()
def PKNL_CAMB_MultipleRedshift(self, kk, zz): if len(zz)<2: print "zz must be a list, if you need to compute \ only for single redshift, use PowerSpectrumNonLinear_SingleRedshift" exit() pars = camb.CAMBparams() pars.set_cosmology(H0=self.h*100, ombh2=self.Omega_b*self.h**2, \ omch2=(self.Omega_m-self.Omega_b)*self.h**2) pars.set_dark_energy(w=self.w0) #re-set defaults pars.InitPower.set_params(ns=self.n_s) pars.set_matter_power(redshifts=zz, kmax=10.0) results = camb.get_results(pars) pars.NonLinear = camb.model.NonLinear_both results.calc_power_spectra(pars) kh_nonlin, z_nonlin, pk_nonlin = \ results.get_matter_power_spectrum(minkh=1e-4,maxkh=10.0,npoints=500) s8 = np.array(results.get_sigma8()) for i in range(len(s8)): pk_nonlin[:,i] = pk_nonlin[:,i]*(self.Sigma_8*np.interp(zz[i], self.z_gf, self.gff)/s8[i])**2 function = interp2d(kh_nonlin, z_nonlin, pk_nonlin) return np.transpose(function(kk, zz))
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, 0.17, 3.1])
pars.NonLinear = model.NonLinear_none
data = camb.get_results(pars)
# transfer = data.get_cmb_transfer_data()
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], 0.247, 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)
cls = data.get_cmb_power_spectra(pars)
cls_tot = data.get_total_cls(2000)
cls_scal = data.get_unlensed_scalar_cls(2000)
cls_tensor = data.get_tensor_cls(2000)
cls_lensed = data.get_lensed_scalar_cls(2000)
cls_phi = data.get_lens_potential_cls(2000)
def __init__(self, Omega_m=0.3175, Omega_b=0.049, h=0.6711, ns=0.9624, s8=None,
Mnu=0.0, As=2.13e-9, Omega_k=0.0,
pivot_scalar=0.05, pivot_tensor=0.05,
Nnu=3, hierarchy='degenerate', Neff=3.046, tau=None,
redshifts=[0, 0.5, 1, 2, 3], kmax=10.0, k_per_logint=50,
verbose=False):
Omega_c = Omega_m - Omega_b - Mnu/(93.14*h**2)
Omega_cb = Omega_c + Omega_b
pars = camb.CAMBparams()
# set accuracy of the calculation
pars.set_accuracy(AccuracyBoost=5.0, lSampleBoost=5.0,
lAccuracyBoost=5.0, HighAccuracyDefault=True,
DoLateRadTruncation=True)
# set value of the cosmological parameters
pars.set_cosmology(H0=h*100.0, ombh2=Omega_b*h**2, omch2=Omega_c*h**2,
mnu=Mnu, omk=Omega_k,
neutrino_hierarchy=hierarchy,
num_massive_neutrinos=Nnu, nnu=Neff,
tau=tau)
# set the value of the primordial power spectrum parameters
pars.InitPower.set_params(As=As, ns=ns,
pivot_scalar=pivot_scalar,
pivot_tensor=pivot_tensor)
# set redshifts, k-range and k-sampling
pars.set_matter_power(redshifts=redshifts, kmax=kmax,
k_per_logint=k_per_logint)
# compute results
results = camb.get_results(pars)
# get raw matter P(k) and transfer functions with weird k-binning
#k, zs, Pk = results.get_linear_matter_power_spectrum()
#Tk = (results.get_matter_transfer_data()).transfer_data
# interpolate to get Pmm, Pcc...etc
k,z,Pmm = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=500, var1=7, var2=7,
have_power_spectra=True,
params=None)
k,z,Pcc = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=500, var1=2, var2=2,
have_power_spectra=True,
params=None)
k,z,Pbb = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=500, var1=3, var2=3,
have_power_spectra=True,
params=None)
k,z,Pcb = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=500, var1=2, var2=3,
have_power_spectra=True,
params=None)
Pcb = (Omega_c**2*Pcc + Omega_b**2*Pbb +\
2.0*Omega_b*Omega_c*Pcb)/Omega_cb**2
k,z,Pnn = results.get_matter_power_spectrum(minkh=2e-5, maxkh=kmax,
npoints=500, var1=6, var2=6,
have_power_spectra=True,
params=None)
# rescale by sigma_8
s8_linear = results.get_sigma8()[-1]
if s8!=None and z[0]!=0.0:
raise Exception('To rescale by s8 we need to generate the linear Pk at z=0')
factor = (s8/s8_linear)**2
# get sigma_8 and Hz in km/s/(kpc/h)
self.s8 = np.array(results.get_sigma8())[::-1]*np.sqrt(factor)
self.Hz = np.array([results.hubble_parameter(red) for red in z])
self.z = z; self.k = k
self.Pkmm = Pmm*factor; self.Pknn = Pnn*factor
self.Pkcc = Pcc*factor; self.Pkbb = Pbb*factor; self.Pkcb = Pcb*factor
if verbose: print pars
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))
# In[20]: import camb from camb import model, initialpower #Set up a new set of parameters for CAMB pars = camb.CAMBparams() #This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.06, omk=0, tau=0.06) pars.InitPower.set_params(ns=0.965, r=0) pars.set_for_lmax(1100, lens_potential_accuracy=0); #calculate results for these parameters results = camb.get_results(pars) #get dictionary of CAMB power spectra powers =results.get_cmb_power_spectra(pars) unlencl = powers['unlensed_scalar'] cls0 = unlencl[:,0][2:1101] print(len(cls0)) # In[21]: # # Multiply by 1e10 to avoid underflow #
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)
h5f.create_dataset('reds', data=redssour)
h5f.create_dataset('adis', data=adis)
h5f.create_dataset('adistdim', data=adistdim)
h5f.close()
pars = camb.CAMBparams()
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.06, omk=0, tau=0.06)
pars.InitPower.set_params(ns=0.965, r=0)
pars.set_for_lmax(2500, lens_potential_accuracy=0);
path = '/Users/tansu/Desktop/infl/'
os.system('mkdir -p %s' % path)
#os.environ["FERM_IGAL_DATA_PATH"]
cambdata = camb.get_results(pars)
psec = cambdata.get_cmb_power_spectra(pars)
psectotl = psec['total']
pseculen = psec['unlensed_scalar']
ls = np.arange(psectotl.shape[0])
fig, ax = plt.subplots(2,2, figsize = (12,12))
ax[0,0].plot(ls,psectotl[:,0], color='k')
ax[0,0].plot(ls,pseculen[:,0], color='r')
ax[0,0].set_title('TT')
ax[0,1].plot(ls[2:], 1-pseculen[2:,0]/psectotl[2:,0]);
ax[0,1].set_title(r'$\Delta TT$')
ax[1,0].plot(ls,psectotl[:,1], color='k')
ax[1,0].plot(ls,pseculen[:,1], color='r')
ax[1,0].set_title(r'$EE$')
ax[1,1].plot(ls,psectotl[:,3], color='k')
def testInitialPower(self):
pars = camb.CAMBparams()
pars.set_cosmology(H0=67)
import ctypes
P = camb.InitialPowerLaw()
P2 = ctypes.pointer(P)
self.assertEqual(P.As, pars.InitPower.As)
As = 1.8e-9
ns = 0.8
P.set_params(As=As, ns=ns)
self.assertEqual(P.As, As)
self.assertEqual(P2.contents.As, As)
pars2 = camb.CAMBparams()
pars2.set_cosmology(H0=67)
pars2.InitPower.set_params(As=1.7e-9, ns=ns)
self.assertEqual(pars2.InitPower.As, 1.7e-9)
pars.set_initial_power(pars2.InitPower)
self.assertEqual(pars.InitPower.As, 1.7e-9)
pars.set_initial_power(P)
self.assertEqual(pars.InitPower.As, As)
ks = np.logspace(-5.5, 2, 1000)
pk = (ks / P.pivot_scalar) ** (ns - 1) * As
pars2.set_initial_power_table(ks, pk)
self.assertAlmostEqual(pars2.scalar_power(1.1), pars.scalar_power(1.1), delta=As * 1e-4)
sp = camb.SplinedInitialPower(ks=ks, PK=pk)
pars2.set_initial_power(sp)
self.assertAlmostEqual(pars2.scalar_power(1.1), pars.scalar_power(1.1), delta=As * 1e-4)
self.assertFalse(sp.has_tensors())
self.assertFalse(pars2.InitPower.has_tensors())
sp = camb.SplinedInitialPower()
sp.set_scalar_log_regular(10 ** (-5.5), 10. ** 2, pk)
pars2.set_initial_power(sp)
self.assertAlmostEqual(pars2.scalar_power(1.1), pars.scalar_power(1.1), delta=As * 1e-4)
sp.set_tensor_log_regular(10 ** (-5.5), 10. ** 2, pk)
pars2.set_initial_power(sp)
self.assertAlmostEqual(pars2.tensor_power(1.1), pars.scalar_power(1.1), delta=As * 1e-4)
self.assertTrue(sp.has_tensors())
sp.set_tensor_table([], [])
self.assertFalse(sp.has_tensors())
pars2.set_initial_power(sp)
results = camb.get_results(pars2)
cl = results.get_lensed_scalar_cls(CMB_unit='muK')
pars.InitPower.set_params(As=As, ns=ns)
results2 = camb.get_results(pars)
cl2 = results2.get_lensed_scalar_cls(CMB_unit='muK')
self.assertTrue(np.allclose(cl, cl2, rtol=1e-4))
P = camb.InitialPowerLaw(As=2.1e-9, ns=0.9)
pars2.set_initial_power(P)
pars.InitPower.set_params(As=2.1e-9, ns=0.9)
self.assertAlmostEqual(pars2.scalar_power(1.1), pars.scalar_power(1.1), delta=As * 1e-4)
def PK(k, As, ns):
return As * (k / 0.05) ** (ns - 1) * (1 + 0.1 * np.sin(10 * k))
pars.set_initial_power_function(PK, args=(3e-9, 0.95))
P = pars.scalar_power(ks)
np.testing.assert_almost_equal(P, PK(ks, 3e-9, 0.95), decimal=4)
def get_camb_results(self):
self.cambresults = camb.get_results(self.cambparams)
self.totalCl = self.cambresults.get_cmb_power_spectra(self.cambparams)['total']
return self.cambresults