pyccl.halos.halo_model_base module

class pyccl.halos.halo_model_base.HMIngredients(self, *, mass_def, mass_def_strict=True)

Bases: CCLAutoRepr, CCLNamedClass

Base class for halo model ingredients.

class pyccl.halos.halo_model_base.Concentration(self, *, mass_def)

Bases: HMIngredients

This class enables the calculation of halo concentrations.

Parameters:

mass_def (MassDef) – a mass definition object or a name string.

__call__(cosmo, M, a)

Returns the concentration for input parameters.

Parameters:

• cosmo (Cosmology) – A Cosmology object.

• M (float or array) – halo mass.

• a (float) – scale factor.

Returns:

concentration.

Return type:

(float or array)

class pyccl.halos.halo_model_base.MassFunc(self, *, mass_def, mass_def_strict=True)

Bases: HMIngredients

This class enables the calculation of halo mass functions. We currently assume that all mass functions can be written as

\[\frac{dn}{d\log_{10}M} = f(\sigma_M)\,\frac{\rho_M}{M}\, \frac{d\log \sigma_M}{d\log_{10} M}\]

where \(\sigma_M^2\) is the overdensity variance on spheres with a radius given by the Lagrangian radius for mass M.

• Subclasses implementing analytical mass function parametrizations can be created by overriding the _get_fsigma method.

• Subclasses may have particular implementations of _check_mass_def_strict to ensure consistency of the halo mass definition.

• Subclasses for parametrizations that cannot be written in terms of \(\sigma_M\) can simply overload the __call__() method.

Parameters:

• mass_def (MassDef) – a mass definition object or a name string.

• mass_def_strict (bool) – if False, consistency of the mass definition will be ignored.

__call__(cosmo, M, a)

Returns the mass function for input parameters.

Parameters:

• cosmo (Cosmology) – A Cosmology object.

• M (float or array) – halo mass.

• a (float) – scale factor.

Returns:

mass function \(dn/d\log_{10}M\) in units of Mpc^-3 (comoving).

Return type:

(float or array)

class pyccl.halos.halo_model_base.HaloBias(self, *, mass_def, mass_def_strict=True)

Bases: HMIngredients

This class enables the calculation of halo bias functions. We currently assume that all halo bias functions can be written as functions that depend on \(M\) only through \(\sigma_M\) (where \(\sigma_M^2\) is the overdensity variance on spheres with a radius given by the Lagrangian radius for mass \(M\)). All sub-classes implementing specific parametrizations can therefore be simply created by replacing this class’ _get_bsigma method. New classes departing from this paradigm can simply overload the __call__() method.

Parameters:

• mass_def (MassDef) – a mass definition object or a name string.

• mass_def_strict (bool) – if False, consistency of the mass definition will be ignored.

__call__(cosmo, M, a)

Returns the halo bias for input parameters.

Parameters:

• cosmo (Cosmology) – A Cosmology object.

• M (float or array) – halo mass.

• a (float) – scale factor.

Returns:

halo bias.

Return type:

(float or array)

pyccl.halos.halo_model_base.get_delta_c(cosmo, a, kind='EdS')

Returns the linear collapse threshold.

Parameters:

• cosmo (Cosmology) – A Cosmology object.

• a (float or array) – scale factor.

• kind (str) –

  prescription to use. Should be one of

  • ’EdS’: the SC prediction in Einstein de-Sitter, \(\delta_c=(3/20)(12\pi)^{2/3}\).

  • ’EdS_approx’: a common approximation to the EdS result \(\delta_c=1.686\).

  • ’NakamuraSuto97’: the prescription from Nakamura & Suto 1997.

  • ’Mead16’: the prescription from Mead et al. 2016.

Returns:

linear collapse threshold.

Return type:

(float or array)