def check_cls(cosmo):
"""
Check that cls functions can be run.
"""
# Number density input
z = np.linspace(0., 1., 200)
n = np.ones(z.shape)
# Bias input
b = np.sqrt(1. + z)
# ell range input
ell_scl = 4
ell_lst = [2, 3, 4, 5, 6, 7, 8, 9]
ell_arr = np.arange(2, 10)
# ClTracer test objects
lens1 = ccl.ClTracerLensing(cosmo, False, n=n, z=z)
lens2 = ccl.ClTracerLensing(cosmo, True, n=(z,n), bias_ia=(z,n), f_red=(z,n))
nc1 = ccl.ClTracerNumberCounts(cosmo, False, False, n=(z,n), bias=(z,b))
nc2 = ccl.ClTracerNumberCounts(cosmo, True, False, n=(z,n), bias=(z,b))
nc3 = ccl.ClTracerNumberCounts(cosmo, True, True, n=(z,n), bias=(z,b),
mag_bias=(z,b))
cmbl=ccl.ClTracerCMBLensing(cosmo,1100.)
# Check valid ell input is accepted
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_scl)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_lst)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_scl)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_lst)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, cmbl, cmbl, ell_arr)) )
# Check various cross-correlation combinations
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, nc1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, nc2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, nc3, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, nc1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, nc2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, nc3, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc3, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc2, nc3, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc2, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc3, cmbl, ell_arr)) )
# Check that reversing order of ClTracer inputs works
assert_( all_finite(ccl.angular_cl(cosmo, nc1, lens1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, lens2, ell_arr)) )
clt_dlo_n12b = ccl.ClTracerNumberCounts(cosmo, False, False,
(zarr_dlo_n12b, nzarr_dlo_n12b),
(zarr_dlo_n12b, bzarr_dlo_n12b))
clt_qso_n12b = ccl.ClTracerNumberCounts(cosmo, False, False,
(zarr_qso_n12b, nzarr_qso_n12b),
(zarr_qso_n12b, bzarr_qso_n12b))
clt_dlo_g16 = ccl.ClTracerNumberCounts(cosmo, False, False,
(zarr_dlo_g16, nzarr_dlo_g16),
(zarr_dlo_g16, bzarr_dlo_g16))
clt_qso_g16 = ccl.ClTracerNumberCounts(cosmo, False, False,
(zarr_qso_g16, nzarr_qso_g16),
(zarr_qso_g16, bzarr_qso_g16))
clt_qsu = ccl.ClTracerNumberCounts(cosmo, False, False,
(zarr_qsu, nzarr_qsu),
(zarr_qsu, bzarr_qsu))
clt_cmbl = ccl.ClTracerCMBLensing(cosmo)
larr_b = np.concatenate((1. * np.arange(500), 500 + 10 * np.arange(950)))
cl_dc_n12 = ccl.angular_cl(cosmo,
clt_dlo_n12,
clt_cmbl,
larr_b,
l_limber=-1)
cl_qc_n12 = ccl.angular_cl(cosmo,
clt_qso_n12,
clt_cmbl,
larr_b,
l_limber=-1)
cl_dc_n12b = ccl.angular_cl(cosmo,
clt_dlo_n12b,
clt_cmbl,
larr_b,
def check_cls_nu(cosmo):
"""
Check that cls functions can be run.
"""
# Number density input
z = np.linspace(0., 1., 200)
n = np.exp(-((z-0.5)/0.1)**2)
# Bias input
b = np.sqrt(1. + z)
# ell range input
ell_scl = 4
ell_lst = [2, 3, 4, 5, 6, 7, 8, 9]
ell_arr = np.arange(2, 10)
# Check if power spectrum type is valid for CMB
cmb_ok = True
if cosmo.configuration.matter_power_spectrum_method \
== ccl.core.matter_power_spectrum_types['emu']: cmb_ok = False
# ClTracer test objects
lens1 = ccl.ClTracerLensing(cosmo, False, n=n, z=z)
lens2 = ccl.ClTracerLensing(cosmo, True, n=(z,n), bias_ia=(z,n), f_red=(z,n))
nc1 = ccl.ClTracerNumberCounts(cosmo, False, False, n=(z,n), bias=(z,b))
# Check that for massive neutrinos including rsd raises an error (not yet implemented)
assert_raises(RuntimeError, ccl.ClTracerNumberCounts, cosmo, True, False, n=(z,n), bias=(z,b))
cmbl=ccl.ClTracerCMBLensing(cosmo,1100.)
# Check valid ell input is accepted
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_scl)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_lst)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_scl)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_lst)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, cmbl, cmbl, ell_arr)) )
# Check various cross-correlation combinations
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, nc1, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, lens1, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, nc1, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, lens2, cmbl, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, nc1, cmbl, ell_arr)) )
# Check that reversing order of ClTracer inputs works
assert_( all_finite(ccl.angular_cl(cosmo, nc1, lens1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, lens2, ell_arr)) )
# Check get_internal_function()
a_scl = 0.5
a_lst = [0.2, 0.4, 0.6, 0.8, 1.]
a_arr = np.linspace(0.2, 1., 5)
assert_( all_finite(nc1.get_internal_function(cosmo, 'dndz', a_scl)) )
assert_( all_finite(nc1.get_internal_function(cosmo, 'dndz', a_lst)) )
assert_( all_finite(nc1.get_internal_function(cosmo, 'dndz', a_arr)) )
# Check that invalid options raise errors
assert_raises(KeyError, nc1.get_internal_function, cosmo, 'x', a_arr)
assert_raises(ValueError, ccl.ClTracerNumberCounts, cosmo, True, True,
n=(z,n), bias=(z,b))
assert_raises(KeyError, ccl.ClTracer, cosmo, 'x', True, True,
n=(z,n), bias=(z,b))
assert_raises(ValueError, ccl.ClTracerLensing, cosmo,
has_intrinsic_alignment=True, n=(z,n), bias_ia=(z,n))
assert_no_warnings(ccl.cls._cltracer_obj, nc1)
assert_no_warnings(ccl.cls._cltracer_obj, nc1.cltracer)
assert_raises(TypeError, ccl.cls._cltracer_obj, None)
def check_cls(cosmo):
"""
Check that cls functions can be run.
"""
# Number density input
z = np.linspace(0., 1., 200)
n = np.exp(-((z-0.5)/0.1)**2)
# Bias input
b = np.sqrt(1. + z)
# ell range input
ell_scl = 4
ell_lst = [2, 3, 4, 5, 6, 7, 8, 9]
ell_arr = np.arange(2, 10)
# Check if power spectrum type is valid for CMB
cmb_ok = True
if cosmo.configuration.matter_power_spectrum_method \
== ccl.core.matter_power_spectrum_types['emu']: cmb_ok = False
# ClTracer test objects
lens1 = ccl.ClTracerLensing(cosmo, False, n=n, z=z)
lens2 = ccl.ClTracerLensing(cosmo, True, n=(z,n), bias_ia=(z,n), f_red=(z,n))
nc1 = ccl.ClTracerNumberCounts(cosmo, False, False, n=(z,n), bias=(z,b))
nc2 = ccl.ClTracerNumberCounts(cosmo, True, False, n=(z,n), bias=(z,b))
nc3 = ccl.ClTracerNumberCounts(cosmo, True, True, n=(z,n), bias=(z,b),
mag_bias=(z,b))
cmbl=ccl.ClTracerCMBLensing(cosmo,1100.)
# Check valid ell input is accepted
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_scl)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_lst)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_scl)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_lst)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, cmbl, cmbl, ell_arr)) )
# Check non-limber calculations
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_arr, l_limber=20, non_limber_method="native")))
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc1, ell_arr, l_limber=20, non_limber_method="angpow")))
# Check various cross-correlation combinations
assert_( all_finite(ccl.angular_cl(cosmo, lens1, lens2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, nc1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, nc2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens1, nc3, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, lens1, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, nc1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, nc2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, lens2, nc3, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, lens2, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc2, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, nc3, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, nc1, cmbl, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc2, nc3, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, nc2, cmbl, ell_arr)) )
if cmb_ok: assert_( all_finite(ccl.angular_cl(cosmo, nc3, cmbl, ell_arr)) )
# Check that reversing order of ClTracer inputs works
assert_( all_finite(ccl.angular_cl(cosmo, nc1, lens1, ell_arr)) )
assert_( all_finite(ccl.angular_cl(cosmo, nc1, lens2, ell_arr)) )
# Wrong non limber method
assert_raises(KeyError, ccl.angular_cl, cosmo, lens1, lens1, ell_scl, non_limber_method='xx')
zarr_dlo, nzarr_dlo = get_nz_oversample(bn, nz, 256)
bzarr_dlo = bdla * np.ones_like(zarr_dlo)
nz, bins = np.histogram(data_dla['zqso'], range=[0, 7], bins=50)
zarr_qso, nzarr_qso = get_nz_oversample(bn, nz, 256)
bzarr_qso = bqso * np.ones_like(zarr_qso)
cosmo = ccl.Cosmology(Omega_c=0.27,
Omega_b=0.045,
h=0.69,
sigma8=0.83,
n_s=0.96)
clt_dlo = ccl.ClTracerNumberCounts(cosmo, False, False, (zarr_dlo, nzarr_dlo),
(zarr_dlo, bzarr_dlo))
clt_qso = ccl.ClTracerNumberCounts(cosmo, False, False, (zarr_qso, nzarr_qso),
(zarr_qso, bzarr_qso))
clt_cmbl = ccl.ClTracerCMBLensing(cosmo, z_source=1100.)
def get_fisher(xp):
ll = np.arange(xp.lmin, xp.lmax)
cl_dd = ccl.angular_cl(cosmo, clt_dlo, clt_dlo, ll) #,l_limber=-1)
cl_dq = ccl.angular_cl(cosmo, clt_dlo, clt_qso, ll) #,l_limber=-1)
cl_dc = ccl.angular_cl(cosmo, clt_dlo, clt_cmbl, ll) #,l_limber=-1)
cl_qq = ccl.angular_cl(cosmo, clt_qso, clt_qso, ll) #,l_limber=-1)
cl_qc = ccl.angular_cl(cosmo, clt_qso, clt_cmbl, ll) #,l_limber=-1)
cl_cc = ccl.angular_cl(cosmo, clt_cmbl, clt_cmbl, ll) #,l_limber=-1)
cl_aa = cl_dd + 2 * cl_dq + cl_qq
cl_aq = cl_dq + cl_qq
cl_ac = cl_dc + cl_qc
nl_dd = cl_dd + xp.cl_noise_dla(ll)
nl_dc = cl_dc
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,
z=rdshift,
n=n_dla,
bias=bias_dla)
theory_dla = ccl.angular_cl(cosmo, lens, source_dla, ell_arr, l_limber=-1)