def test_mgrowth():
"""
Compare the modified growth function computed by CCL against the exact
result for a particular modification of the growth rate.
"""
# Define differential growth rate arrays
nz_mg = 128
z_mg = np.zeros(nz_mg)
df_mg = np.zeros(nz_mg)
for i in range(0, nz_mg):
z_mg[i] = 4. * (i + 0.0) / (nz_mg - 1.)
df_mg[i] = 0.1 / (1. + z_mg[i])
# Define two test cosmologies, without and with modified growth respectively
p1 = ccl.Parameters(Omega_c=0.25,
Omega_b=0.05,
Omega_k=0.,
N_nu_rel=0.,
N_nu_mass=0.,
m_nu=0.,
w0=-1.,
wa=0.,
h=0.7,
A_s=2.1e-9,
n_s=0.96)
p2 = ccl.Parameters(Omega_c=0.25,
Omega_b=0.05,
Omega_k=0.,
N_nu_rel=0.,
N_nu_mass=0.,
m_nu=0.,
w0=-1.,
wa=0.,
h=0.7,
A_s=2.1e-9,
n_s=0.96,
z_mg=z_mg,
df_mg=df_mg)
cosmo1 = ccl.Cosmology(p1)
cosmo2 = ccl.Cosmology(p2)
# We have included a growth modification \delta f = K*a, with K==0.1
# (arbitrarily). This case has an analytic solution, given by
# D(a) = D_0(a)*exp(K*(a-1)). Here we compare the growth computed by CCL
# with the analytic solution.
a = 1. / (1. + z_mg)
d1 = ccl.growth_factor(cosmo1, a)
d2 = ccl.growth_factor(cosmo2, a)
f1 = ccl.growth_rate(cosmo1, a)
f2 = ccl.growth_rate(cosmo2, a)
f2r = f1 + 0.1 * a
d2r = d1 * np.exp(0.1 * (a - 1.))
# Check that ratio of calculated and analytic results is within tolerance
assert_allclose(d2r / d2, np.ones(d2.size), rtol=GROWTH_TOLERANCE)
assert_allclose(f2r / f2, np.ones(f2.size), rtol=GROWTH_TOLERANCE)
def get_cosmo(self, dic_par):
omega_c = dic_par['omega_c']
omega_b = dic_par['omega_b']
omega_k = dic_par['omega_k']
omega_nu = dic_par['omega_nu']
if 'w' in dic_par:
w = dic_par['w']
else:
w = -1.
if 'wa' in dic_par:
wa = dic_par['wa']
else:
wa = 0.
h0 = dic_par['h0']
has_sigma8 = ('sigma_8' in dic_par)
has_A_s = ('A_s' in dic_par)
n_s = dic_par['n_s']
if has_sigma8 and has_A_s:
raise ValueError("Specifying both sigma8 and A_s: pick one")
elif has_sigma8:
sigma8 = dic_par['sigma_8']
params = ccl.Parameters(Omega_c=omega_c,
Omega_b=omega_b,
Omega_k=omega_k,
Omega_n=omega_nu,
w0=w,
wa=wa,
sigma8=sigma8,
n_s=n_s,
h=h0)
elif has_A_s:
A_s = dic_par['A_s']
params = ccl.Parameters(Omega_c=omega_c,
Omega_b=omega_b,
Omega_k=omega_k,
Omega_n=omega_nu,
w0=w,
wa=wa,
A_s=A_s,
n_s=n_s,
h=h0)
else:
raise ValueError("Need either sigma 8 or A_s in pyccl.")
cosmo = ccl.Cosmology(
params,
transfer_function=dic_par['transfer_function'],
matter_power_spectrum=dic_par['matter_power_spectrum'])
return cosmo
def reference_models():
"""
Create a set of reference Cosmology() objects.
"""
# Standard LCDM model
p1 = ccl.Parameters(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
A_s=1e-10,
n_s=0.96)
cosmo1 = ccl.Cosmology(p1)
# LCDM model with curvature
p2 = ccl.Parameters(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
A_s=1e-10,
n_s=0.96,
Omega_k=0.05)
cosmo2 = ccl.Cosmology(p2)
# wCDM model
p3 = ccl.Parameters(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
A_s=1e-10,
n_s=0.96,
w0=-0.95,
wa=0.05)
cosmo3 = ccl.Cosmology(p3)
# BBKS Pk
p4 = ccl.Parameters(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
sigma8=0.8,
n_s=0.96)
cosmo4 = ccl.Cosmology(p4, transfer_function='bbks')
# E&H Pk
p5 = ccl.Parameters(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
sigma8=0.8,
n_s=0.96)
cosmo5 = ccl.Cosmology(p5, transfer_function='eisenstein_hu')
# Return (only do one cosmology for now, for speed reasons)
return [cosmo1, cosmo4, cosmo5] # cosmo2, cosmo3
def compare_distances_mnu_hiz(z, chi_bench,dm_bench, Omega_v, w0, wa, Neff_mnu, mnu):
"""
Compare distances calculated by pyccl with the distances in the benchmark
file.
"""
# Set Omega_K in a consistent way
Omega_k = 1.0 - Omega_c - Omega_b - Omega_v
# Create new Parameters and Cosmology objects
p = ccl.Parameters(Omega_c=Omega_c, Omega_b=Omega_b, Neff=Neff,
h=h, A_s=A_s, n_s=n_s, Omega_k=Omega_k,
w0=w0, wa=wa, m_nu=mnu)
cosmo = ccl.Cosmology(p)
# Calculate distance using pyccl
a = 1. / (1. + z)
chi = ccl.comoving_radial_distance(cosmo, a)
# Compare to benchmark data
assert_allclose(chi, chi_bench, atol=1e-12, rtol=DISTANCES_TOLERANCE_MNU)
#compare distance moudli where a!=1
a_not_one = (a!=1).nonzero()
dm = ccl.distance_modulus(cosmo,a[a_not_one])
assert_allclose(dm, dm_bench[a_not_one], atol=1e-3, rtol = DISTANCES_TOLERANCE_MNU)
def compare_distances(z, chi_bench, Omega_v, w0, wa):
"""
Compare distances calculated by pyccl with the distances in the benchmark
file.
"""
# Set Omega_K in a consistent way
Omega_k = 1.0 - Omega_c - Omega_b - Omega_n - Omega_v
# Create new Parameters and Cosmology objects
p = ccl.Parameters(Omega_c=Omega_c,
Omega_b=Omega_b,
Omega_n=Omega_n,
h=h,
A_s=A_s,
n_s=n_s,
Omega_k=Omega_k,
w0=w0,
wa=wa)
p.parameters.Omega_g = 0. # Hack to set to same value used for benchmarks
cosmo = ccl.Cosmology(p)
# Calculate distance using pyccl
a = 1. / (1. + z)
chi = ccl.comoving_radial_distance(cosmo, a) * h
# Compare to benchmark data
assert_allclose(chi, chi_bench, atol=1e-12, rtol=DISTANCES_TOLERANCE)
def test_cosmology_init():
"""
Check that Cosmology objects can only be constructed in a valid way.
"""
# Create test cosmology object
params = ccl.Parameters(Omega_c=0.25, Omega_b=0.05, h=0.7, A_s=2.1e-9,
n_s=0.96)
# Make sure error raised if incorrect type of Parameters object passed
assert_raises(TypeError, ccl.Cosmology, params=params.parameters)
assert_raises(TypeError, ccl.Cosmology, params="x")
# Make sure error raised if wrong config type passed
assert_raises(TypeError, ccl.Cosmology, params=params, config="string")
# Make sure error raised if invalid transfer/power spectrum etc. type passed
assert_raises(KeyError, ccl.Cosmology, params=params,
matter_power_spectrum='x')
assert_raises(KeyError, ccl.Cosmology, params=params,
transfer_function='x')
assert_raises(KeyError, ccl.Cosmology, params=params,
baryons_power_spectrum='x')
assert_raises(KeyError, ccl.Cosmology, params=params,
mass_function='x')
assert_raises(KeyError, ccl.Cosmology, params=params,
halo_concentration='x')
def compare_distances(z, chi_bench,dm_bench, Omega_v, w0, wa):
"""
Compare distances calculated by pyccl with the distances in the benchmark
file.
This test is only valid when radiation is explicitly set to 0.
"""
# Set Omega_K in a consistent way
Omega_k = 1.0 - Omega_c - Omega_b - Omega_v
# Create new Parameters and Cosmology objects
p = ccl.Parameters(Omega_c=Omega_c, Omega_b=Omega_b, Neff = Neff,
h=h, A_s=A_s, n_s=n_s, Omega_k=Omega_k,
w0=w0, wa=wa)
p.parameters.Omega_g = 0. # Hack to set to same value used for benchmarks
cosmo = ccl.Cosmology(p)
# Calculate distance using pyccl
a = 1. / (1. + z)
chi = ccl.comoving_radial_distance(cosmo, a) * h
# Compare to benchmark data
assert_allclose(chi, chi_bench, atol=1e-12, rtol=DISTANCES_TOLERANCE)
#compare distance moudli where a!=1
a_not_one = (a!=1).nonzero()
dm = ccl.distance_modulus(cosmo,a[a_not_one])
assert_allclose(dm, dm_bench[a_not_one], atol=1e-3, rtol = DISTANCES_TOLERANCE*10)
def main(dic_par):
cosmo = ccl.Cosmology(
ccl.Parameters(
Omega_c=dic_par['omega_c'],
Omega_b=dic_par['omega_b'],
h=dic_par['h0'],
sigma8=dic_par['sigma_8'],
n_s=dic_par['n_s'],
),
transfer_function=dic_par['transfer_function'],
matter_power_spectrum=dic_par['matter_power_spectrum'])
tracers, cltracers = getTracers(cosmo, dic_par)
binning = getBinning(tracers)
binning_sacc = sacc.SACC(tracers, binning)
theories = getTheories(cosmo, binning_sacc, cltracers)
mean = getTheoryVec(binning_sacc, theories)
precision, covmatrix = getPrecisionMatrix(binning_sacc, theories)
chol = la.cholesky(covmatrix)
csacc = sacc.SACC(tracers, binning, mean, precision)
csacc.printInfo()
## generate mean sim
generate_sim("sims/sim_mean.sacc",
csacc,
add_random=False,
store_precision=True,
cholesky=chol)
#Generate file containing only noiseless realization and precision matrix
nsim = 10
for i in np.arange(nsim):
generate_sim("sims/sim_%03d.sacc" % i, csacc, cholesky=chol)
def compare_growth(z, gfac_bench, Omega_v, w0, wa):
"""
Compare growth factor calculated by pyccl with the values in the benchmark
file. This test only works if radiation is explicitly set to 0.
"""
# Set Omega_K in a consistent way
Omega_k = 1.0 - Omega_c - Omega_b - Omega_v
# Create new Parameters and Cosmology objects
p = ccl.Parameters(Omega_c=Omega_c,
Omega_b=Omega_b,
Neff=Neff,
m_nu=m_nu,
h=h,
A_s=A_s,
n_s=n_s,
Omega_k=Omega_k,
w0=w0,
wa=wa)
p.parameters.Omega_g = 0. # Hack to set to same value used for benchmarks
cosmo = ccl.Cosmology(p)
# Calculate distance using pyccl
a = 1. / (1. + z)
gfac = ccl.growth_factor_unnorm(cosmo, a)
# Compare to benchmark data
assert_allclose(gfac, gfac_bench, atol=1e-12, rtol=GROWTH_TOLERANCE)
def test_parameters_set():
"""
Check that Parameters object allows parameters to be set in a sensible way.
"""
params = ccl.Parameters(Omega_c=0.25,
Omega_b=0.05,
h=0.7,
A_s=2.1e-9,
n_s=0.96)
# Check that values of sigma8 and A_s won't be misinterpreted by the C code
assert_raises(ValueError,
ccl.Parameters,
Omega_c=0.25,
Omega_b=0.05,
h=0.7,
A_s=2e-5,
n_s=0.96)
assert_raises(ValueError,
ccl.Parameters,
Omega_c=0.25,
Omega_b=0.05,
h=0.7,
sigma8=9e-6,
n_s=0.96)
# Check that error is raised when unrecognized parameter requested
assert_raises(KeyError, lambda: params['wibble'])
def reference_models_nu():
"""
Create a set of reference cosmological models with massive neutrinos.
This is separate because certain functionality is not yes implemented
for massive neutrino cosmologies so will throw errors.
"""
# Emulator Pk w/neutrinos list
p1 = ccl.Parameters(Omega_c=0.27,
Omega_b=0.022 / 0.67**2,
h=0.67,
sigma8=0.8,
n_s=0.96,
Neff=3.04,
m_nu=[0.02, 0.02, 0.02])
cosmo1 = ccl.Cosmology(p1,
transfer_function='emulator',
matter_power_spectrum='emu')
# Emulator Pk with neutrinos, force equalize
#p2 = ccl.Parameters(Omega_c=0.27, Omega_b=0.022/0.67**2, h=0.67, sigma8=0.8,
# n_s=0.96, Neff=3.04, m_nu=0.11)
#cosmo2 = ccl.Cosmology(p1, transfer_function='emulator',
# matter_power_spectrum='emu', emulator_neutrinos='equalize')
return [cosmo1]
def calc(zs, Om, w=None):
if w == None:
p6 = ccl.Parameters(Omega_c=Om - 0.05,
Omega_b=0.05,
h=0.7,
sigma8=0.9,
n_s=0.96)
else:
p6 = ccl.Parameters(Omega_c=Om - 0.05,
Omega_b=0.05,
h=0.7,
sigma8=0.9,
n_s=0.96,
w0=w)
cosmo = ccl.Cosmology(p6)
sfs = 1. / (1 + zs)
return ccl.background.growth_factor(cosmo, sfs)
def get_cosmo(self, dic_par):
Omega_c = dic_par.get('Omega_c', 0.255)
Omega_b = dic_par.get('Omega_b', 0.045)
Omega_k = dic_par.get('Omega_k', 0.0)
mnu = dic_par.get('mnu', 0.06)
w = dic_par.get('w', -1.0)
wa = dic_par.get('wa', 0.0)
h0 = dic_par.get('h0', 0.67)
n_s = dic_par.get('n_s', 0.96)
has_sigma8 = ('sigma_8' in dic_par)
has_A_s = ('A_s' in dic_par)
if has_sigma8 and has_A_s:
raise ValueError("Specifying both sigma8 and A_s: pick one")
elif has_A_s:
A_s = dic_par['A_s']
params = ccl.Parameters(Omega_c=Omega_c,
Omega_b=Omega_b,
Omega_k=Omega_k,
w0=w,
wa=wa,
A_s=A_s,
n_s=n_s,
h=h0)
else:
sigma8 = dic_par.get('sigma_8', 0.8)
params = ccl.Parameters(Omega_c=Omega_c,
Omega_b=Omega_b,
Omega_k=Omega_k,
w0=w,
wa=wa,
sigma8=sigma8,
n_s=n_s,
h=h0)
transfer_function = dic_par.get('transfer_function', 'boltzmann_class')
matter_power_spectrum = dic_par.get('matter_power_spectrum', 'halofit')
cosmo = ccl.Cosmology(
params,
transfer_function=dic_par['transfer_function'],
matter_power_spectrum=dic_par['matter_power_spectrum'])
return cosmo
def reference_models():
"""
Create a set of reference Cosmology() objects.
"""
# Standard LCDM model
p1 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10, n_s=0.96)
cosmo1 = ccl.Cosmology(p1)
# LCDM model with curvature
p2 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10,
n_s=0.96, Omega_k=0.05)
cosmo2 = ccl.Cosmology(p2)
# wCDM model
p3 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10,
n_s=0.96, w0=-0.95, wa=0.05)
cosmo3 = ccl.Cosmology(p3)
# BBKS Pk
p4 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, sigma8=0.8, n_s=0.96)
cosmo4 = ccl.Cosmology(p4, transfer_function='bbks')
# E&H Pk
p5 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, sigma8=0.8, n_s=0.96)
cosmo5 = ccl.Cosmology(p5, transfer_function='eisenstein_hu')
# Emulator Pk
p6 = ccl.Parameters(Omega_c=0.27, Omega_b=0.022/0.67**2, h=0.67, sigma8=0.8,
n_s=0.96, Neff=3.04, m_nu=0.)
cosmo6 = ccl.Cosmology(p6, transfer_function='emulator',
matter_power_spectrum='emu')
# Baryons Pk
p8 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10, n_s=0.96)
cosmo8 = ccl.Cosmology(p8, baryons_power_spectrum='bcm')
# Baryons Pk with choice of BCM parameters other than default
p9 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10, n_s=0.96,
bcm_log10Mc=math.log10(1.7e14), bcm_etab=0.3, bcm_ks=75.)
cosmo9 = ccl.Cosmology(p9, baryons_power_spectrum='bcm')
# Emulator Pk w/neutrinos force equalize
#p10 = ccl.Parameters(Omega_c=0.27, Omega_b=0.022/0.67**2, h=0.67, sigma8=0.8,
# n_s=0.96, Neff=3.04, m_nu=[0.02, 0.02, 0.02])
#cosmo10 = ccl.Cosmology(p7, transfer_function='emulator',
# matter_power_spectrum='emu', emulator_neutrinos='equalize')
# Return (do a few cosmologies, for speed reasons)
return [cosmo1, cosmo4, cosmo5, cosmo9] # cosmo2, cosmo3, cosmo6
def get_CCL_beta(M, z):
a = 1. / (1 + z)
d = 1.0001
import pyccl as ccl
params = ccl.Parameters(Omega_c=cos['om'] - cos['ob'],
Omega_b=cos['ob'],
h=cos['h'],
sigma8=cos['s8'],
n_s=cos['ns'])
cosmo = ccl.Cosmology(params)
dndM1 = ccl.massfunc(cosmo, M, a) / M
dndM2 = ccl.massfunc(cosmo, M * d, a) / (M * d)
return np.log(dndM2 / dndM1) / np.log(d)
def compare_class_distances(z, chi_bench, Neff=3.0, m_nu=0.0, Omega_k=0.0):
"""
Compare distances calculated by pyccl with the distances in the CLASS
benchmark file.
"""
# Create new Parameters and Cosmology objects
p = ccl.Parameters(Omega_c=Omega_c,
Omega_b=Omega_b,
Neff=Neff,
h=h,
A_s=A_s,
n_s=n_s,
Omega_k=Omega_k,
m_nu=m_nu)
cosmo = ccl.Cosmology(p)
# Calculate distance using pyccl
a = 1. / (1. + z)
chi = ccl.comoving_radial_distance(cosmo, a)
# Compare to benchmark data
assert_allclose(chi, chi_bench, rtol=DISTANCES_TOLERANCE_CLASS)
def compare_class_distances(z, chi_bench, dm_bench, Neff=3.0, m_nu=0.0,
Omega_k=0.0, w0=-1.0, wa=0.0):
"""
Compare distances calculated by pyccl with the distances in the CLASS
benchmark file.
"""
# Create new Parameters and Cosmology objects
p = ccl.Parameters(Omega_c=Omega_c, Omega_b=Omega_b, Neff=Neff,
h=h, A_s=A_s, n_s=n_s, Omega_k=Omega_k, m_nu=m_nu,
w0=w0, wa=wa)
cosmo = ccl.Cosmology(p)
# Calculate distance using pyccl
a = 1. / (1. + z)
chi = ccl.comoving_radial_distance(cosmo, a)
# Compare to benchmark data
assert_allclose(chi, chi_bench, rtol=DISTANCES_TOLERANCE_CLASS)
# Compare distance moudli where a!=1
a_not_one = a != 1
dm = ccl.distance_modulus(cosmo, a[a_not_one])
assert_allclose(dm, dm_bench[a_not_one], rtol=DISTANCES_TOLERANCE_CLASS)
def reference_models():
"""
Create a set of reference Cosmology() objects.
"""
# Standard LCDM model
p1 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10, n_s=0.96)
cosmo1 = ccl.Cosmology(p1)
# LCDM model with curvature
p2 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10,
n_s=0.96, Omega_k=0.05)
cosmo2 = ccl.Cosmology(p2)
# wCDM model
p3 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10,
n_s=0.96, w0=-0.95, wa=0.05)
cosmo3 = ccl.Cosmology(p3)
# BBKS Pk
p4 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, sigma8=0.8, n_s=0.96)
cosmo4 = ccl.Cosmology(p4,transfer_function='bbks')
# E&H Pk
p5 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, sigma8=0.8, n_s=0.96)
cosmo5 = ccl.Cosmology(p5,transfer_function='eisenstein_hu')
# Baryons Pk
p6 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10, n_s=0.96)
cosmo6 = ccl.Cosmology(p6,baryons_power_spectrum='bcm')
# Baryons Pk with choice of BCM parameters other than default
p7 = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10, n_s=0.96,
bcm_log10Mc=math.log10(1.7e14), bcm_etab=0.3, bcm_ks=75.)
cosmo7 = ccl.Cosmology(p7,baryons_power_spectrum='bcm')
# Return
return [cosmo1,cosmo4,cosmo5,cosmo7] # cosmo2, cosmo3, cosmo6
q = [line.split()][0][4]
if isfloat(ra):
dla_ra.append(float(ra))
dla_dec.append(float(dec))
dla_z.append(float(d))
qso_z.append(float(q))
dz = .01
rdshift = np.arange(0, 7.2, dz)
n_dla = stats.gaussian_kde(dla_z, bw_method=0.1)(rdshift)
n_qso = stats.gaussian_kde(qso_z, bw_method=0.1)(rdshift)
#set ccl parameters
parameters = ccl.Parameters(Omega_c=0.27,
Omega_b=0.045,
h=0.69,
sigma8=0.83,
n_s=0.96)
cosmo = ccl.Cosmology(parameters)
lens = ccl.ClTracerCMBLensing(cosmo)
#use ccl to predict cls
bias_qso = np.ones(rdshift.size)
bias_dla = np.ones(rdshift.size)
b = nmt.NmtBin(2048, nlb=bsize)
ell_arr = b.get_effective_ells()
source_dla = ccl.ClTracerNumberCounts(cosmo,
False,
False,
'omega_cdm': 0.11910,
'tau_reio': 0.0865,
'P_k_max_h/Mpc': 20.,
'YHe': 0.245370,
'N_ncdm': 0,
'z_pk': ", ".join(map(str, np.concatenate((zlow, zhigh))))
}
OmegaH = 1e-3
OmegaC = (params['omega_b'] + params['omega_cdm']) / params['h']**2
OmegaB = (params['omega_b']) / params['h']**2
h = params['h']
cosmo = ccl.Cosmology(ccl.Parameters(
Omega_c=OmegaC,
Omega_b=OmegaB,
h=h,
A_s=params['A_s'],
n_s=params['n_s'],
),
matter_power_spectrum='linear')
def Tspin(z, OmegaH, OmegaM):
## Formula from Tzu-Ching, in mK, arXiv:0709.3672
return 0.3 * (OmegaH / 1e-3) * np.sqrt(
(1 + z) / (2.5) * 0.29 / (OmegaM + (1. - OmegaM) / (1 + z)**3))
# first let's compute background values at our zs
for name, zs in [("zlow", zlow)]: #,("zhigh",zhigh)]:
f = open("background_%s.dat" % (name), 'w')
#
# This takes an input sacc files, replaces measurements with CCL theory predictions + scatter and
# resaves
#
import sacc
import sys
import numpy as np
import scipy.linalg as la
try:
import pyccl as ccl
except:
print("Need CCL!")
sys.exit(1)
p = ccl.Parameters(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=1e-10, n_s=0.96)
ccl_cosmo = ccl.Cosmology(p)
s = sacc.SACC.loadFromHDF("test.sacc")
#
# first, let's convert sacc tracers to CCL tracers
#
def sacc2ccl_tracer(t):
if len(t.z) != 1:
toret = ccl.ClTracerNumberCounts(ccl_cosmo,
has_rsd=False,
has_magnification=False,
n=(t.z, t.Nz),
bias=(t.z, t.extraColumn('b')),
mag_bias=(t.z, np.zeros(len(t.z))))
def getTheories(ccl_cosmo,s,ctracers) :
theo={}
for t1i,t2i,ells,_ in s.sortTracers():
cls=ccl.angular_cl(ccl_cosmo,ctracers[t1i],ctracers[t2i],ells)
theo[(t1i,t2i)]=cls
theo[(t2i,t1i)]=cls
return theo
def getTheoryVec(s, cls_theory):
vec=np.zeros((s.size(),))
for t1i,t2i,ells,ndx in s.sortTracers():
vec[ndx]=cls_theory[(t1i,t2i)]
return sacc.MeanVec(vec)
print 'Setting up cosmology'
cosmo = ccl.Cosmology(ccl.Parameters(Omega_c=0.266,Omega_b=0.049,h=hhub,sigma8=0.8,n_s=0.96,),matter_power_spectrum='linear',transfer_function='eisenstein_hu')
#Compute grid scale
zmax=2.5
ngrid=3072
a_grid=2*ccl.comoving_radial_distance(cosmo,1./(1+zmax))*(1+2./ngrid)/ngrid*hhub
print "Grid smoothing : %.3lf Mpc/h"%a_grid
print 'Reading SACC file'
#SACC File with the N(z) to analyze
binning_sacc = sacc.SACC.loadFromHDF('../test/catalog0.sacc')
#Bias file (it can also be included in the SACC file in the line before)
bias_tab = astropy.table.Table.read('../test/bz_lsst.txt',format='ascii')
tracers = binning_sacc.tracers
print 'Got ',len(tracers),' tracers'
cltracers=[ccl.ClTracerNumberCounts(cosmo,False,False,n=(t.z,t.Nz),bias=(bias_tab['col1'],bias_tab['col2']),r_smooth=0.5*a_grid) for t in tracers]
