def setup(self):
# Initialize cosmology
par = self.get_cosmological_parameters()
dpk = self.get_pk()
a = 1./(1+dpk['z'][::-1])
self.cosmo = ccl.CosmologyCalculator(Omega_c=par['Omega_m']-par['Omega_b'],
Omega_b=par['Omega_b'],
h=par['h'], n_s=par['n_s'],
A_s=par['A_s'], w0=par['w0'],
pk_linear={'a': a,
'k': dpk['k'],
'delta_matter:delta_matter': dpk['pk_lin'][::-1][:]},
pk_nonlin={'a': a,
'k': dpk['k'],
'delta_matter:delta_matter': dpk['pk_nl'][::-1][:]})
# Initialize tracers
if self.config.get('tracers_from_kernels', False):
tpar = self.get_tracer_parameters()
ker = self.get_tracer_kernels()
a_g = 1./(1+ker['z_cl'][::-1])
self.t_g = []
for k in ker['kernels_cl']:
t = ccl.Tracer()
barr = np.ones_like(a_g)
t.add_tracer(self.cosmo,
(ker['chi_cl'], k),
transfer_a=(a_g, barr))
self.t_g.append(t)
self.t_s = []
for k in ker['kernels_sh']:
t = ccl.Tracer()
t.add_tracer(self.cosmo,
kernel=(ker['chi_sh'], k),
der_bessel=-1, der_angles=2)
self.t_s.append(t)
else:
nzs = self.get_tracer_dndzs()
tpar = self.get_tracer_parameters()
z_g = nzs['z_cl']
z_s = nzs['z_sh']
self.t_g = [ccl.NumberCountsTracer(self.cosmo, True,
(z_g, nzs['dNdz_cl'][:, ni]),
bias=(z_g,
np.full(len(z_g), b)))
for ni, b in zip(range(0, 10),
tpar['b_g'])]
self.t_s = [ccl.WeakLensingTracer(self.cosmo,
(z_s, nzs['dNdz_sh'][:, ni]),
True)
for ni in range(0, 5)]
def get_tracer(tracer_type, cosmo=None, **tracer_kwargs):
if cosmo is None:
cosmo = COSMO
z = np.linspace(0., 1., 2000)
n = dndz(z)
b = np.sqrt(1. + z)
if tracer_type == 'nc':
ntr = 3
tr = ccl.NumberCountsTracer(cosmo,
True,
dndz=(z, n),
bias=(z, b),
mag_bias=(z, b),
**tracer_kwargs)
elif tracer_type == 'wl':
ntr = 2
tr = ccl.WeakLensingTracer(cosmo,
dndz=(z, n),
ia_bias=(z, b),
**tracer_kwargs)
elif tracer_type == 'cl':
ntr = 1
tr = ccl.CMBLensingTracer(cosmo, 1100., **tracer_kwargs)
else:
ntr = 0
tr = ccl.Tracer(**tracer_kwargs)
return tr, ntr
def get_tracer(tracer_type):
z = np.linspace(0., 1., 2000)
n = dndz(z)
b = np.sqrt(1. + z)
if tracer_type == 'nc':
ntr = 3
tr = ccl.NumberCountsTracer(COSMO,
True,
dndz=(z, n),
bias=(z, b),
mag_bias=(z, b))
elif tracer_type == 'wl':
ntr = 2
tr = ccl.WeakLensingTracer(COSMO, dndz=(z, n), ia_bias=(z, b))
elif tracer_type == 'cl':
ntr = 1
tr = ccl.CMBLensingTracer(COSMO, 1100.)
else:
ntr = 0
tr = ccl.Tracer()
return tr, ntr
def get_tracer():
chis = np.linspace(CHIMIN, CHIMAX, NCHI)
ws = np.ones(NCHI)
t = ccl.Tracer()
t.add_tracer(COSMO, kernel=(chis, ws))
return t
def test_tracers_analytic(set_up, alpha, beta, gamma, is_factorizable,
w_transfer, mismatch, der_bessel, der_angles):
cosmo = set_up
zmax = 0.8
zi = 0.4
zf = 0.6
nchi = 1024
nk = 512
# x arrays
chii = ccl.comoving_radial_distance(cosmo, 1. / (1 + zi))
chif = ccl.comoving_radial_distance(cosmo, 1. / (1 + zf))
chimax = ccl.comoving_radial_distance(cosmo, 1. / (1 + zmax))
chiarr = np.linspace(0.1, chimax, nchi)
if mismatch:
# Remove elements around the edges
mask = (chiarr < 0.9 * chif) & (chiarr > 1.1 * chii)
chiarr_transfer = chiarr[mask]
else:
chiarr_transfer = chiarr
aarr_transfer = ccl.scale_factor_of_chi(cosmo, chiarr_transfer)[::-1]
aarr = ccl.scale_factor_of_chi(cosmo, chiarr)[::-1]
lkarr = np.log(10.**np.linspace(-6, 3, nk))
# Kernel
wchi = np.ones_like(chiarr)
wchi[chiarr < chii] = 0
wchi[chiarr > chif] = 0
# Transfer
t = ccl.Tracer()
if w_transfer:
ta = (chiarr_transfer**gamma)[::-1]
tk = np.exp(beta * lkarr)
if is_factorizable:
# 1D
t.add_tracer(cosmo,
kernel=(chiarr, wchi),
transfer_k=(lkarr, tk),
transfer_a=(aarr_transfer, ta),
der_bessel=der_bessel,
der_angles=der_angles)
else:
# 2D
tka = ta[:, None] * tk[None, :]
t.add_tracer(cosmo,
kernel=(chiarr, wchi),
transfer_ka=(aarr_transfer, lkarr, tka),
der_bessel=der_bessel,
der_angles=der_angles)
else:
t.add_tracer(cosmo,
kernel=(chiarr, wchi),
der_bessel=der_bessel,
der_angles=der_angles)
# Power spectrum
pkarr = np.ones_like(aarr)[:, None] * (np.exp(alpha * lkarr))[None, :]
pk2d = ccl.Pk2D(a_arr=aarr, lk_arr=lkarr, pk_arr=pkarr, is_logp=False)
# C_ells
larr = np.linspace(2, 3000, 100)
# 1D
cl = ccl.angular_cl(cosmo, t, t, larr, p_of_k_a=pk2d)
# Prediction
clpred = get_prediction(larr, chii, chif, alpha, beta, gamma, der_bessel,
der_angles)
assert np.all(np.fabs(cl / clpred - 1) < 5E-3)