def get_cl(dtype):
config = get_config(dtype)
cosmo_pars = config['cosmo']
cosmo = ccl.Cosmology(**cosmo_pars)
if config['dtype'] == 'generic':
return np.ones(3 * config['nside'])
if config['dtype'] == 'galaxy_density':
z, nz = np.loadtxt('xcell/tests/data/DESY1gc_dndz_bin0.txt',
usecols=(1, 3),
unpack=True)
b = np.ones_like(z)
tracer = ccl.NumberCountsTracer(cosmo,
dndz=(z, nz),
bias=(z, b),
has_rsd=None)
elif config['dtype'] == 'galaxy_shear':
z, nz = np.loadtxt('xcell/tests/data/Nz_DIR_z0.1t0.3.asc',
usecols=(0, 1),
unpack=True)
tracer = ccl.WeakLensingTracer(cosmo, dndz=(z, nz))
elif config['dtype'] == 'cmb_convergence':
tracer = ccl.CMBLensingTracer(cosmo, z_source=1100)
elif config['dtype'] == 'cmb_tSZ':
tracer = ccl.tSZTracer(cosmo, z_max=3.)
cl = ccl.angular_cl(cosmo, tracer, tracer, np.arange(3 * config['nside']))
return cl
def test_clfid_halomod(tr1, tr2):
data = get_config(dtype0=tr1, dtype1=tr2, inc_hm=True)
cosmo = ccl.Cosmology(**data['cov']['fiducial']['cosmo'])
md = ccl.halos.MassDef200m()
mf = ccl.halos.MassFuncTinker10(cosmo, mass_def=md)
hb = ccl.halos.HaloBiasTinker10(cosmo, mass_def=md)
cm = ccl.halos.ConcentrationDuffy08(mdef=md)
hmc = ccl.halos.HMCalculator(cosmo, mf, hb, md)
pNFW = ccl.halos.HaloProfileNFW(cm)
profs = {}
ccltr = {}
normed = {}
for tr, lab in [(tr1, 'Dummy__0'), (tr2, 'Dummy__1')]:
if tr == 'galaxy_density':
data['tracers'][lab]['hod_params'] = {'lMmin_0': 12.1,
'lM1_p': 0.1,
'bg_0': 1.2}
profs[tr] = ccl.halos.HaloProfileHOD(cm, lMmin_0=12.1,
lM1_p=0.1, bg_0=1.2)
z, nz = np.loadtxt('xcell/tests/data/DESY1gc_dndz_bin0.txt',
usecols=(1, 3), unpack=True)
ccltr[tr] = ccl.NumberCountsTracer(cosmo, False, dndz=(z, nz),
bias=(z, np.ones_like(z)))
normed[tr] = True
elif tr == 'cmb_tSZ':
data['tracers'][lab]['gnfw_params'] = {'mass_bias': 0.9}
profs[tr] = ccl.halos.HaloProfilePressureGNFW(mass_bias=0.9)
ccltr[tr] = ccl.tSZTracer(cosmo, z_max=3.)
normed[tr] = False
elif tr == 'galaxy_shear':
profs[tr] = pNFW
z, nz = np.loadtxt('xcell/tests/data/Nz_DIR_z0.1t0.3.asc',
usecols=(0, 1), unpack=True)
ccltr[tr] = ccl.WeakLensingTracer(cosmo, dndz=(z, nz))
normed[tr] = True
elif tr == 'cmb_convergence':
profs[tr] = pNFW
ccltr[tr] = ccl.CMBLensingTracer(cosmo, z_source=1100.)
normed[tr] = True
clf = ClFid(data, 'Dummy__0', 'Dummy__1')
d = clf.get_cl_file()
shutil.rmtree(tmpdir1)
k_arr = np.geomspace(1E-4, 1E2, 512)
a_arr = 1./(1+np.linspace(0, 3, 15)[::-1])
pk = ccl.halos.halomod_Pk2D(cosmo, hmc, profs[tr1],
prof2=profs[tr2],
normprof1=normed[tr1],
normprof2=normed[tr2],
lk_arr=np.log(k_arr),
a_arr=a_arr)
# Commented out until these features are pushed to the pip release of CCL
# smooth_transition=(lambda a: 0.7),
# supress_1h=(lambda a: 0.01))
clb = ccl.angular_cl(cosmo, ccltr[tr1], ccltr[tr2], d['ell'], p_of_k_a=pk)
assert np.all(np.fabs(clb[2:]/d['cl'][0][2:]-1) < 1E-4)
def update_tracer(self, cosmo, **kwargs):
if self.type == 'g':
nz_new = self.nzf(self.z_avg +
(self.z - self.z_avg) / kwargs['width'])
nz_new /= simps(nz_new, x=self.z)
self.tracer = ccl.NumberCountsTracer(cosmo,
has_rsd=False,
dndz=(self.z, nz_new),
bias=(self.z, self.bz))
elif self.type == 'y':
self.tracer = ccl.tSZTracer(cosmo)
elif self.type == 'k':
self.tracer = ccl.CMBLensingTracer(cosmo, z_source=1100.)
def _get_cl_ccl(self, dtype):
ls = np.arange(3 * self.nside)
if dtype == 'galaxy_density':
z, nz = self.get_nz()
b = np.ones_like(z)
tracer = ccl.NumberCountsTracer(self.cosmo, has_rsd=False,
dndz=(z, nz), bias=(z, b))
elif dtype == 'galaxy_shear':
z, nz = self.get_nz()
tracer = ccl.WeakLensingTracer(self.cosmo, dndz=(z, nz))
elif dtype == 'cmb_convergence':
tracer = ccl.CMBLensingTracer(self.cosmo, z_source=1100)
elif dtype == 'cmb_tSZ':
# Note that the tSZ power spectrum implemented here is wrong
# But it's not worth for now adding all the halo model stuff.
tracer = ccl.tSZTracer(self.cosmo, z_max=3.)
return ccl.angular_cl(self.cosmo, tracer, tracer, ls)
def test_szcl():
fsky = 1.
COSMO = ccl.Cosmology(Omega_b=0.05,
Omega_c=0.25,
h=0.7,
n_s=0.9645,
A_s=2.02E-9,
Neff=3.046,
transfer_function='boltzmann_class')
bm = np.loadtxt("benchmarks/data/sz_cl_P13_szpowerspectrum.txt",
unpack=True)
l_bm = bm[0]
cl_bm = bm[1]
tll_bm = np.loadtxt("benchmarks/data/tSZ_trispectrum_ref_for_cobaya.txt")
fac = 2 * np.pi / (l_bm * (l_bm + 1) * 1E12)
cl_bm *= fac
tll_bm *= fac[:, None] * fac[None, :] / (4 * np.pi * fsky)
mass_def = ccl.halos.MassDef(500, 'critical')
hmf = ccl.halos.MassFuncTinker08(COSMO, mass_def=mass_def)
hbf = ccl.halos.HaloBiasTinker10(COSMO, mass_def=mass_def)
hmc = ccl.halos.HMCalculator(COSMO, hmf, hbf, mass_def)
prf = ccl.halos.HaloProfilePressureGNFW()
prf.update_parameters(mass_bias=1. / 1.41, x_out=6.)
tr = ccl.tSZTracer(COSMO, z_max=3.)
# Power spectrum
pk = ccl.halos.halomod_Pk2D(COSMO, hmc, prf, get_2h=False)
cl = ccl.angular_cl(COSMO, tr, tr, l_bm, p_of_k_a=pk)
# Covariance
lk_arr = np.log(np.geomspace(1E-4, 1E2, 256))
a_arr = 1. / (1 + np.linspace(0, 3., 20))[::-1]
tkk = ccl.halos.halomod_Tk3D_1h(COSMO,
hmc,
prf,
lk_arr=lk_arr,
a_arr=a_arr,
use_log=True)
tll = ccl.angular_cl_cov_cNG(COSMO, tr, tr, l_bm, tkk, fsky=fsky)
assert np.all(np.fabs(cl / cl_bm - 1) < 2E-2)
assert np.all(np.fabs(tll / tll_bm - 1) < 5E-2)
def test_szcl():
COSMO = ccl.Cosmology(Omega_b=0.05,
Omega_c=0.25,
h=0.67,
n_s=0.9645,
A_s=2.02E-9,
Neff=3.046)
bm = np.loadtxt("benchmarks/data/sz_cl_P13_szpowerspectrum.txt",
unpack=True)
l_bm = bm[0]
cl_bm = bm[1]
cl_bm *= (2 * np.pi) / (1E12 * l_bm * (l_bm + 1))
mass_def = ccl.halos.MassDef(500, 'critical')
hmf = ccl.halos.MassFuncTinker08(COSMO, mass_def=mass_def)
hbf = ccl.halos.HaloBiasTinker10(COSMO, mass_def=mass_def)
hmc = ccl.halos.HMCalculator(COSMO, hmf, hbf, mass_def)
prf = ccl.halos.HaloProfilePressureGNFW(mass_bias=1. / 1.41)
pk = ccl.halos.halomod_Pk2D(COSMO, hmc, prf, get_2h=False)
tr = ccl.tSZTracer(COSMO, z_max=4.)
cl = ccl.angular_cl(COSMO, tr, tr, l_bm, p_of_k_a=pk)
assert np.all(np.fabs(cl / cl_bm - 1) < 2E-2)
def test_clfid_halomod_M500c():
tr1 = 'cmb_tSZ'
tr2 = 'cmb_convergence'
data = get_config(dtype0=tr1, dtype1=tr2, inc_hm=True)
data['cov']['fiducial']['halo_model'] = {'mass_def': '500c',
'concentration': 'Duffy08M500c'}
data['tracers']['Dummy__0']['gnfw_params'] = {'mass_bias': 0.9}
cosmo = ccl.Cosmology(**data['cov']['fiducial']['cosmo'])
md = ccl.halos.MassDef(500, 'critical')
mf = ccl.halos.MassFuncTinker10(cosmo, mass_def=md)
hb = ccl.halos.HaloBiasTinker10(cosmo, mass_def=md)
cm = ConcentrationDuffy08M500c(mdef=md)
hmc = ccl.halos.HMCalculator(cosmo, mf, hb, md)
prof1 = ccl.halos.HaloProfilePressureGNFW(mass_bias=0.9)
ccltr1 = ccl.tSZTracer(cosmo, z_max=3.)
prof2 = ccl.halos.HaloProfileNFW(cm)
ccltr2 = ccl.CMBLensingTracer(cosmo, z_source=1100.)
clf = ClFid(data, 'Dummy__0', 'Dummy__1')
d = clf.get_cl_file()
shutil.rmtree(tmpdir1)
k_arr = np.geomspace(1E-4, 1E2, 512)
a_arr = 1./(1+np.linspace(0, 6., 30)[::-1])
pk = ccl.halos.halomod_Pk2D(cosmo, hmc, prof1,
prof2=prof2,
normprof1=False,
normprof2=True,
lk_arr=np.log(k_arr),
a_arr=a_arr)
# Commented out until these features are pushed to the pip release of CCL
# smooth_transition=(lambda a: 0.7),
# supress_1h=(lambda a: 0.01))
clb = ccl.angular_cl(cosmo, ccltr1, ccltr2, d['ell'], p_of_k_a=pk)
assert np.all(np.fabs(clb[2:]/d['cl'][0][2:]-1) < 1E-4)
def compute_tracer_ccl(self, name, tracer, mapper):
cosmo = self.get_cosmo_ccl()
hm_par = self.get_halomodel_params()
dtype = mapper.get_dtype()
ccl_pr = hm_par['prof_NFW']
ccl_pr_2pt = hm_par['prof_2pt']
with_hm = tracer.get('use_halo_model', False)
normed_profile = True
# Get Tracers
if dtype == 'galaxy_density':
# Import z, pz
z, pz = mapper.get_nz(dz=0)
bias = (z, np.ones_like(z))
mag_bias = None
mag_s = tracer.get('magnif_s', None)
if mag_s:
mag_bias = (z, np.ones_like(z) * mag_s)
# Get tracer
ccl_tr = ccl.NumberCountsTracer(cosmo,
has_rsd=False,
dndz=(z, pz),
bias=bias,
mag_bias=mag_bias)
if with_hm:
hod_pars = tracer.get('hod_params', {})
ccl_pr = ccl.halos.HaloProfileHOD(hm_par['cM'], **hod_pars)
ccl_pr_2pt = ccl.halos.Profile2ptHOD()
elif dtype == 'galaxy_shear':
# Import z, pz
z, pz = mapper.get_nz(dz=0)
# # Calculate bias IA
ia_bias = None
if self.config['wl_ia']:
# TODO: Improve this in yml file
A, eta, z0 = self.config['wl_ia']
# pyccl2 -> has already the factor inside. Only needed bz
bz = A * ((1. + z) / (1. + z0))**eta * 0.0139 / 0.013872474
ia_bias = (z, bz)
# Get tracer
ccl_tr = ccl.WeakLensingTracer(cosmo,
dndz=(z, pz),
ia_bias=ia_bias)
elif dtype == 'cmb_convergence':
# TODO: correct z_source
ccl_tr = ccl.CMBLensingTracer(cosmo, z_source=1100)
elif dtype == 'cmb_tSZ':
normed_profile = False
ccl_tr = ccl.tSZTracer(cosmo, z_max=3.)
if with_hm:
pars = tracer.get('gnfw_params', {})
ccl_pr = ccl.halos.HaloProfilePressureGNFW(**pars)
else:
raise ValueError('Type of tracer not recognized. It can be \
galaxy_density, galaxy_shear, cmb_tSZ, or \
cmb_convergence!')
return {
'name': name,
'ccl_tr': ccl_tr,
'ccl_pr': ccl_pr,
'ccl_pr_2pt': ccl_pr_2pt,
'with_hm': with_hm,
'normed': normed_profile
}