def test_tkk1h_errors():
from pyccl.pyutils import assert_warns
hmc = ccl.halos.HMCalculator(COSMO, HMF, HBF, mass_def=M200)
k_arr = KK
a_arr = np.array([0.1, 0.4, 0.7, 1.0])
# Wrong first profile
with pytest.raises(TypeError):
ccl.halos.halomod_trispectrum_1h(COSMO, hmc, k_arr, a_arr, None)
# Wrong other profiles
with pytest.raises(TypeError):
ccl.halos.halomod_trispectrum_1h(COSMO,
hmc,
k_arr,
a_arr,
P1,
prof2=PKC)
with pytest.raises(TypeError):
ccl.halos.halomod_trispectrum_1h(COSMO,
hmc,
k_arr,
a_arr,
P1,
prof3=PKC)
with pytest.raises(TypeError):
ccl.halos.halomod_trispectrum_1h(COSMO,
hmc,
k_arr,
a_arr,
P1,
prof4=PKC)
# Wrong 2pts
with pytest.raises(TypeError):
ccl.halos.halomod_trispectrum_1h(COSMO,
hmc,
k_arr,
a_arr,
P1,
prof12_2pt=P2)
with pytest.raises(TypeError):
ccl.halos.halomod_trispectrum_1h(COSMO,
hmc,
k_arr,
a_arr,
P1,
prof34_2pt=P2)
# Negative profile in logspace
assert_warns(ccl.CCLWarning,
ccl.halos.halomod_Tk3D_1h,
COSMO,
hmc,
P3,
prof2=Pneg,
lk_arr=np.log(k_arr),
a_arr=a_arr,
use_log=True)
def test_halomodel_power_consistent():
from pyccl.pyutils import assert_warns
a = 0.8
k = np.logspace(-1, 1, 10)
# These are all deprecated.
tot = assert_warns(ccl.CCLWarning, ccl.halomodel_matter_power, COSMO, k, a)
one = assert_warns(ccl.CCLWarning, ccl.onehalo_matter_power, COSMO, k, a)
two = assert_warns(ccl.CCLWarning, ccl.twohalo_matter_power, COSMO, k, a)
assert np.allclose(one + two, tot)
def test_mu_sigma_matter_power_err(mp):
from pyccl.pyutils import assert_warns
with pytest.raises(ccl.CCLError):
cosmo = ccl.Cosmology(Omega_c=0.25,
Omega_b=0.05,
h=0.7,
A_s=2.1e-9,
n_s=0.96,
mu_0=0.1,
sigma_0=0.2,
transfer_function=None,
matter_power_spectrum=mp)
# Also raises a warning, so catch that.
assert_warns(ccl.CCLWarning, ccl.nonlin_matter_power, cosmo, 1, 1)
def test_halomodel_choices_raises():
from pyccl.pyutils import assert_warns
cosmo = ccl.Cosmology(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
sigma8=0.8,
n_s=0.96,
transfer_function='bbks',
matter_power_spectrum='linear',
mass_function='tinker')
a = 0.8
k = np.geomspace(1E-2, 1, 10)
with pytest.raises(ValueError):
assert_warns(ccl.CCLWarning, ccl.twohalo_matter_power, cosmo, k, a)
def test_halomod_f2d_copy():
from pyccl.pyutils import assert_warns
mdef = ccl.halos.MassDef('vir', 'matter')
hmf = ccl.halos.MassFuncSheth99(COSMO_HM,
mdef,
mass_def_strict=False,
use_delta_c_fit=True)
hbf = ccl.halos.HaloBiasSheth99(COSMO_HM,
mass_def=mdef,
mass_def_strict=False)
cc = ccl.halos.ConcentrationDuffy08(mdef)
prf = ccl.halos.HaloProfileNFW(cc)
hmc = ccl.halos.HMCalculator(COSMO_HM, hmf, hbf, mdef)
pk2d = ccl.halos.halomod_Pk2D(COSMO_HM, hmc, prf, normprof1=True)
psp_new = pk2d.psp
# This just triggers the internal calculation
pk_old = assert_warns(ccl.CCLWarning, ccl.nonlin_matter_power, COSMO_HM,
1., 0.8)
pk_new = pk2d.eval(1., 0.8, COSMO_HM)
psp_old = COSMO_HM.get_nonlin_power().psp
assert psp_new.lkmin == psp_old.lkmin
assert psp_new.lkmax == psp_old.lkmax
assert psp_new.amin == psp_old.amin
assert psp_new.amax == psp_old.amax
assert psp_new.is_factorizable == psp_old.is_factorizable
assert psp_new.is_k_constant == psp_old.is_k_constant
assert psp_new.is_a_constant == psp_old.is_a_constant
assert psp_new.is_log == psp_old.is_log
assert psp_new.growth_factor_0 == psp_old.growth_factor_0
assert psp_new.growth_exponent == psp_old.growth_exponent
assert psp_new.extrap_order_lok == psp_old.extrap_order_lok
assert psp_new.extrap_order_hik == psp_old.extrap_order_hik
assert pk_old == pk_new
def test_halo_concentration(m):
from pyccl.pyutils import assert_warns
a = 0.8
# Deprecated.
c = assert_warns(ccl.CCLWarning, ccl.halo_concentration, COSMO, m, a)
assert np.all(np.isfinite(c))
assert np.shape(c) == np.shape(m)
def test_massfunc_m2r_smoke(m):
from pyccl.pyutils import assert_warns
# Deprecated
# TODO: switch to mass2radius_lagrangian
r = assert_warns(ccl.CCLWarning, ccl.massfunc_m2r, COSMO, m)
assert np.all(np.isfinite(r))
assert np.shape(r) == np.shape(m)
def test_halo_bias_smoke(m):
from pyccl.pyutils import assert_warns
a = 0.8
# Deprecated
# TODO: switch to HaloBias
b = assert_warns(ccl.CCLWarning, ccl.halo_bias, COSMO, m, a)
assert np.all(np.isfinite(b))
assert np.shape(b) == np.shape(m)
def test_tkkssc_linear_bias_smoke_and_errors():
hmc = ccl.halos.HMCalculator(COSMO, HMF, HBF, mass_def=M200, nlog10M=2)
k_arr = KK
a_arr = np.array([0.3, 0.5, 0.7, 1.0])
# Tk's exact version
prof = ccl.halos.HaloProfileNFW(ccl.halos.ConcentrationDuffy08(M200),
fourier_analytic=True)
ccl.halos.halomod_Tk3D_SSC_linear_bias(COSMO,
hmc,
prof=prof,
p_of_k_a='linear')
ccl.halos.halomod_Tk3D_SSC_linear_bias(COSMO,
hmc,
prof=prof,
p_of_k_a='nonlinear')
pk = COSMO.get_nonlin_power()
ccl.halos.halomod_Tk3D_SSC_linear_bias(COSMO, hmc, prof=prof, p_of_k_a=pk)
# Error when prof is not NFW
with pytest.raises(TypeError):
ccl.halos.halomod_Tk3D_SSC_linear_bias(COSMO, hmc, prof=P2)
# Error when p_of_k_a is wrong
with pytest.raises(TypeError):
ccl.halos.halomod_Tk3D_SSC_linear_bias(COSMO,
hmc,
prof=prof,
p_of_k_a=P1)
# Negative profile in logspace
assert_warns(ccl.CCLWarning,
ccl.halos.halomod_Tk3D_SSC_linear_bias,
COSMO,
hmc,
prof,
bias1=-1,
lk_arr=np.log(k_arr),
a_arr=a_arr,
use_log=True)
def test_tkkssc_errors():
hmc = ccl.halos.HMCalculator(COSMO, HMF, HBF, mass_def=M200)
k_arr = KK
a_arr = np.array([0.3, 0.5, 0.7, 1.0])
# Wrong first profile
with pytest.raises(TypeError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, None)
# Wrong other profiles
for i in range(2, 4):
kw = {'prof%d' % i: PKC}
with pytest.raises(TypeError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, P1, **kw)
# Wrong 2pts
with pytest.raises(TypeError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, P1, prof12_2pt=P2)
with pytest.raises(TypeError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, P1, prof34_2pt=P2)
# No normalization for number counts profile
with pytest.raises(ValueError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, P2, normprof1=False)
with pytest.raises(ValueError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, P1, prof2=P2, normprof2=False)
with pytest.raises(ValueError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, P1, prof3=P2, normprof3=False)
with pytest.raises(ValueError):
ccl.halos.halomod_Tk3D_SSC(COSMO, hmc, P1, prof4=P2, normprof4=False)
# Negative profile in logspace
assert_warns(ccl.CCLWarning,
ccl.halos.halomod_Tk3D_1h,
COSMO,
hmc,
P3,
prof2=Pneg,
lk_arr=np.log(k_arr),
a_arr=a_arr,
use_log=True)
def test_halomodel_power(k, kind):
from pyccl.pyutils import assert_warns
a = 0.8
if kind == 'one':
func = ccl.onehalo_matter_power
elif kind == 'two':
func = ccl.twohalo_matter_power
else:
func = ccl.halomodel_matter_power
pk = assert_warns(ccl.CCLWarning, func, COSMO, k, a)
assert np.all(np.isfinite(pk))
assert np.shape(k) == np.shape(pk)
def test_haloprofile_smoke(func, r):
from pyccl.pyutils import assert_warns
cosmo = ccl.Cosmology(
Omega_c=0.27, Omega_b=0.045, h=0.67, sigma8=0.8, n_s=0.96,
transfer_function='bbks', matter_power_spectrum='linear')
a = 1
c = 5
mass = 1e14
odelta = 200
# These are all deprecated
prof = assert_warns(
ccl.CCLWarning,
getattr(ccl, func), cosmo, c, mass, odelta, a, r)
assert np.all(np.isfinite(prof))
assert np.shape(prof) == np.shape(r)
def test_halo_bias_models_smoke(mf_type):
from pyccl.pyutils import assert_warns
cosmo = ccl.Cosmology(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
sigma8=0.8,
n_s=0.96,
transfer_function='bbks',
matter_power_spectrum='linear',
mass_function=mf_type)
hbf_cls = ccl.halos.halo_bias_from_name(MF_EQUIV[mf_type])
hbf = hbf_cls(cosmo)
for m in MS:
# Deprecated
bm_old = assert_warns(ccl.CCLWarning, ccl.halo_bias, cosmo, m, 1.)
bm_new = hbf.get_halo_bias(cosmo, m, 1.)
assert np.all(np.isfinite(bm_old))
assert np.shape(bm_old) == np.shape(m)
assert np.all(np.array(bm_old) == np.array(bm_new))
def test_correlation_newtypes(typs):
from pyccl.pyutils import assert_warns
z = np.linspace(0., 1., 200)
n = np.ones(z.shape)
lens = ccl.WeakLensingTracer(COSMO, dndz=(z, n))
ell = np.logspace(1, 3, 5)
cl = ccl.angular_cl(COSMO, lens, lens, ell)
theta = np.logspace(-2., np.log10(5.), 5)
corr_old = assert_warns(ccl.CCLWarning,
ccl.correlation,
COSMO,
ell,
cl,
theta,
corr_type=typs[0])
corr_new = ccl.correlation(COSMO, ell, cl, theta, type=typs[1])
assert np.all(corr_new == corr_old)
def test_halomodel_choices_smoke(mf_c):
from pyccl.pyutils import assert_warns
mf, c = mf_c
cosmo = ccl.Cosmology(Omega_c=0.27,
Omega_b=0.045,
h=0.67,
sigma8=0.8,
n_s=0.96,
transfer_function='bbks',
matter_power_spectrum='linear',
mass_function=mf,
halo_concentration=c)
a = 0.8
k = np.geomspace(1E-2, 1, 10)
# Deprecated
# TODO: Convert this and other places to using the non-deprecated syntax
# Or, since this wasn't already done, maybe this is a useful convenience
# function?
p = assert_warns(ccl.CCLWarning, ccl.twohalo_matter_power, cosmo, k, a)
pb = get_pk_new(mf, c, cosmo, a, k, False, True)
assert np.all(np.isfinite(p))
assert np.allclose(p, pb)
