def angular_cl(cosmo, cltracer1, cltracer2, ell):
"""
Calculate the angular (cross-)power spectrum for a pair of tracers.
Args:
cosmo (:obj:`Cosmology`): A Cosmology object.
cltracer1, cltracer2 (:obj:): ClTracer objects, of any kind.
ell (float or array_like): Angular wavenumber(s) to evaluate the
angular power spectrum at.
Returns:
cl (float or array_like): Angular (cross-)power spectrum values, C_ell,
for the pair of tracers, as a function of `ell`.
"""
# Access ccl_cosmology object
cosmo = _cosmology_obj(cosmo)
# Access CCL_ClTracer objects
clt1 = _cltracer_obj(cltracer1)
clt2 = _cltracer_obj(cltracer2)
status = 0
# Return Cl values, according to whether ell is an array or not
if isinstance(ell, float) or isinstance(ell, int) :
# Use single-value function
cl, status = lib.angular_cl(cosmo, ell, clt1, clt2, status)
elif isinstance(ell, np.ndarray):
# Use vectorised function
cl, status = lib.angular_cl_vec(cosmo, clt1, clt2, ell, ell.size, status)
else:
# Use vectorised function
cl, status = lib.angular_cl_vec(cosmo, clt1, clt2, ell, len(ell), status)
check(status)
return cl
def dNdz_tomog(z, dNdz_type, zmin, zmax, pz_func):
"""Calculates dNdz in a particular tomographic bin, convolved
with a photo-z model (defined by the user), and normalized.
Args:
z (float or array_like): Spectroscopic redshifts to evaluate dNdz at.
dNdz_type (:obj:`str`): Type of redshift distribution.
zmin (float): Minimum photo-z of the bin.
zmax (float): Maximum photo-z of the bin.
pz_func (callable): User-defined photo-z function.
Return:
dNdz (float or array_like): dNdz values evalued at each z.
"""
# Ensure that an array will be passed to specs_dNdz_tomog_vec
z = np.atleast_1d(z)
# Do type-check for pz_func argument
if not isinstance(pz_func, PhotoZFunction):
raise TypeError("pz_func must be a PhotoZFunction instance.")
# Get dNdz type
if dNdz_type not in dNdz_types.keys():
raise ValueError("'%s' not a valid dNdz_type." % dNdz_type)
# Call dNdz tomography function
status = 0
dNdz, status = lib.specs_dNdz_tomog_vec(dNdz_types[dNdz_type], zmin, zmax,
pz_func.pz_func, z, z.size, status)
check(status)
return dNdz
def compute_power(self):
"""Interfaces with src/ccl_power.c: ccl_cosmology_compute_power().
Sets up the splines for the power spectrum.
"""
status = 0
status = lib.cosmology_compute_power(self.cosmo, status)
check(status)
def compute_growth(self):
"""Interfaces with src/ccl_background.c: ccl_cosmology_compute_growth().
Sets up the splines for the growth function.
"""
status = 0
status = lib.cosmology_compute_growth(self.cosmo, status)
check(status)
def compute_distances(self):
"""Interfaces with src/compute_background.c: ccl_cosmology_compute_distances().
Sets up the splines for the distances.
"""
status = 0
status = lib.cosmology_compute_distances(self.cosmo, status)
check(status)
def correlation(cosmo, ell, C_ell, theta, corr_type='gg', method='fftlog'):
"""
Compute the angular correlation function.
Args:
cosmo (:obj:`Cosmology`): A Cosmology object.
ell (array_like): Multipoles corresponding to the input angular power spectrum
C_ell (array_like): Input angular power spectrum.
theta (float or array_like): Angular separation(s) at which to calculate the angular correlation function (in degrees).
corr_type (string): Type of correlation function. Choices: 'GG' (galaxy-galaxy), 'GL' (galaxy-shear), 'L+' (shear-shear, xi+), 'L-' (shear-shear, xi-).
method (string, optional): Method to compute the correlation function. Choices: 'Bessel' (direct integration over Bessel function), 'FFTLog' (fast integration with FFTLog), 'Legendre' (brute-force sum over Legendre polynomials).
Returns:
Value(s) of the correlation function at the input angular separation(s).
"""
cosmo = _cosmology_obj(cosmo)
status = 0
# Convert to lower case
corr_type = corr_type.lower()
method = method.lower()
if corr_type not in correlation_types.keys():
raise KeyError("'%s' is not a valid correlation type." % corr_type)
if method.lower() not in correlation_methods.keys():
raise KeyError("'%s' is not a valid correlation method." % method)
# Convert scalar input into an array
scalar = False
if isinstance(theta, float):
scalar = True
theta = np.array([
theta,
])
# Call correlation function
wth, status = lib.correlation_vec(cosmo, ell, C_ell, theta,
correlation_types[corr_type],
correlation_methods[method], len(theta),
status)
check(status)
if scalar: return wth[0]
return wth
def angular_cl(cosmo,
cltracer1,
cltracer2,
ell,
l_limber=-1.,
l_logstep=1.05,
l_linstep=20.,
non_limber_method="native"):
"""
Calculate the angular (cross-)power spectrum for a pair of tracers.
Args:
cosmo (:obj:`Cosmology`): A Cosmology object.
cltracer1, cltracer2 (:obj:): ClTracer objects, of any kind.
ell (float or array_like): Angular wavenumber(s) to evaluate the
angular power spectrum at.
l_limber (float) : Angular wavenumber beyond which Limber's
approximation will be used
l_logstep (float) : logarithmic step in ell at low multipoles
l_linstep (float) : linear step in ell at high multipoles
non_limber_method (str) : non-Limber integration method. Supported:
"native" and "angpow"
Returns:
cl (float or array_like): Angular (cross-)power spectrum values, C_ell,
for the pair of tracers, as a function of `ell`.
"""
# Access ccl_cosmology object
cosmo = _cosmology_obj(cosmo)
if non_limber_method not in nonlimber_methods.keys():
raise KeyError("'%s' is not a valide non-Limber integration method." %
non_limber_method)
# Access CCL_ClTracer objects
clt1 = _cltracer_obj(cltracer1)
clt2 = _cltracer_obj(cltracer2)
status = 0
# Return Cl values, according to whether ell is an array or not
if isinstance(ell, float) or isinstance(ell, int):
# Use single-value function
cl_one, status = lib.angular_cl_vec(
cosmo, clt1, clt2, l_limber, l_logstep, l_linstep,
nonlimber_methods[non_limber_method], [ell], 1, status)
cl = cl_one[0]
elif isinstance(ell, np.ndarray):
# Use vectorised function
cl, status = lib.angular_cl_vec(cosmo, clt1, clt2, l_limber, l_logstep,
l_linstep,
nonlimber_methods[non_limber_method],
ell, ell.size, status)
else:
# Use vectorised function
cl, status = lib.angular_cl_vec(cosmo, clt1, clt2, l_limber, l_logstep,
l_linstep,
nonlimber_methods[non_limber_method],
ell, len(ell), status)
check(status)
return cl