MassFunctionWDM

class hmf.alternatives.wdm.MassFunctionWDM(*args, **kwargs)

A subclass of hmf.MassFunction that mixes in WDM capabilities.

This replaces the standard CDM quantities with WDM-derived ones, where relevant.

In addition to the parameters directly passed to this class, others are available which are passed on to its superclass. To read a standard documented list of (all) parameters, use parameter_info(). If you want to just see the plain list of available parameters, use :meth`get_all_parameters`. To see the actual defaults for each parameter, use get_all_parameter_defaults().

Attributes

ERROR_ON_BAD_MDEF	Switch to turn off exceptions for mdef's not matching hmf_model
Mmax	Maximum mass at which to perform analysis [units \(\log_{10}M_\odot h^{-1}\)].
Mmin	Minimum mass at which to perform analysis [units \(\log_{10}M_\odot h^{-1}\)].
alter_model	A model for empirical recalibration of the HMF.
alter_params	Model parameters for alter_model.
cosmo	The cosmography object with custom parameters applied.
cosmo_model	The cosmological model instance.
cosmo_params	Parameters deviating from the base cosmology.
delta_c	The critical overdensity for collapse, \(\delta_c\).
delta_k	Dimensionless power spectrum, \(\Delta_k = \frac{k^3 P(k)}{2\pi^2}\).
disable_mass_conversion	Disable converting mass function from builtin definition to that provided.
dlnk	Step-size of log wave-numbers.
dlog10m	log10 interval between mass bins.
dndlnm	The differential mass function in terms of natural log of m, len=len(m).
dndlog10m	The differential mass function in terms of log of m, len=len(m).
dndm	The number density of haloes in WDM, len=len(m).
filter	Instantiated model for filter/window functions.
filter_model	A model for the window/filter function.
filter_params	Model parameters for filter_model.
fsigma	The multiplicity function, \(f(\sigma)\), for hmf_model.
growth	The instantiated growth model.
growth_factor	The growth factor.
growth_model	The model to use to calculate the growth function/growth rate.
growth_params	Relevant parameters of the growth_model.
halo_overdensity_crit	The halo overdensity with respect to the critical density.
halo_overdensity_mean	The halo overdensity with respect to the mean background.
hmf	Instantiated model for the hmf fitting function.
hmf_model	A model to use as the fitting function \(f(\sigma)\).
hmf_params	Model parameters for hmf_model.
how_big	Expected simulation volume per halo.
k	Wavenumbers, [h/Mpc].
lnk_max	Maximum (natural) log wave-number, k [h/Mpc].
lnk_min	Minimum (natural) log wave-number, k [h/Mpc].
lnsigma	Natural log of inverse mass variance, len=len(m).
m	Halo masses (defined via mdef).
mass_nonlinear	The nonlinear mass, \(\nu(M_\star) = 1\).
mdef	The halo mass-definition model instance.
mdef_model	A model to use as the mass definition.
mdef_params	Model parameters for mdef_model.
mean_density	Mean density of universe at redshift z.
mean_density0	Mean density of universe at z=0, [Msun h^2 / Mpc**3].
n	Spectral index of fluctuations.
n_eff	Effective spectral index at scale of halo radius, len=len(m).
n_eff_at_collapse	Effective spectral index at the scale of the nonlinear mass, \(\nu = 1\).
ngtm	The cumulative mass function above m, len=len(m) [units \(h^3 Mpc^{-3}\)].
nonlinear_delta_k	Dimensionless nonlinear power spectrum.
nonlinear_power	Non-linear log power [units \(Mpc^3/h^3\)].
normalised_filter	A normalised filter, such that filter.sigma(8) == sigma8.
nu	the squared peak height, \((\delta_c/\sigma)^2\).
nu2	The squared peak height, \(\nu^2 = \left(\frac{\delta_c}{\sigma}\right)^2\), len=len(m).
nu_fn	The squared peak height as a callable function of mass.
parameter_values	Dictionary of all parameters and their current values.
peak_height	The peak height, \(\nu = \delta_c/\sigma\), len=len(m).
power	Normalised log power spectrum [units \(Mpc^3/h^3\)].
radii	The radii corresponding to the masses m.
references	References for the framework itself (not its component models), each a formatted citation string.
rho_gtm	Mass density in haloes >m, len=len(m) [units \(M_\odot h^2 Mpc^{-3}\)].
rho_ltm	Mass density in haloes <m, len=len(m) [units \(M_\odot h^2 Mpc^{-3}\)].
sigma	The sqrt of the mass variance at z, len=len(m).
sigma8_z	sigma(8) at redshif z.
sigma_8	RMS linear density fluctuations in spheres of radius 8 Mpc/h.
sigma_8_species	"tot", "cb" or None.
takahashi	Whether to use updated HALOFIT coefficients from Takahashi+12.
transfer	The instantiated transfer model.
transfer_function	Normalised CDM log transfer function.
transfer_model	Defines which transfer function model to use.
transfer_params	Relevant parameters of the transfer_model.
wdm	The instantiated WDM model.
wdm_mass	Mass of the WDM particle.
wdm_model	A model for the WDM effect on the transfer function.
wdm_params	Parameters of the WDM model.
z	Redshift.

Methods

clone(**kwargs)

Create and return an updated clone of the current object.

get_acknowledgments(flat: bool = False) → dict[str, tuple[str, ...]] | list[str]

Get the references to cite for the current setup of the framework.

The references are grouped by where they come from, in this order:

• "hmf": the paper describing hmf itself.

• The framework’s class name (e.g. "MassFunction"): the framework’s own references, including those of its parent framework classes. Only present if there are any.

• Each *_model parameter (e.g. "hmf_model", "transfer_model"): the references of the model currently set on it. A model with nothing to cite gives an empty tuple; a parameter set to None is left out.

• Each sub-framework, with its keys prefixed by the sub-framework’s name (e.g. "tracer.transfer_model").

The keys say which model each citation belongs to, not whether that model was used in a quantity you computed. All references are read from the model classes, so this does not compute anything.

Parameters:

flat – If True, return a single list of all the references with duplicates removed (keeping the first occurrence), e.g. for a paper’s bibliography.

Returns:

A dict mapping each source to a tuple of formatted citations, or a flat list of citations if flat is True.

Return type:

dict or list

Examples

>>> from hmf import MassFunction
>>> mf = MassFunction(hmf_model="Tinker08")
>>> by_model = mf.get_acknowledgments()
>>> bibliography = mf.get_acknowledgments(flat=True)

classmethod get_all_parameter_defaults(recursive=True)

Dictionary of all parameters and defaults.

Defaults may be set with some logic in __init__, so they are read from an instance. The instance is not validated, so no quantities are computed.

classmethod get_all_parameter_names() → list[str]

Return all parameter names in the class.

These are read from the class’s @parameter definitions, so the class is not instantiated.

get_dependencies(*q)

Determine all parameter dependencies of the quantities in q.

Parameters:

q (str) – String(s) labelling a quantity

Returns:

deps – A set containing all parameters on which quantities in q are dependent.

Return type:

set

classmethod parameter_info(names=None)

Prints information about each parameter in the class.

Optionally, restrict printed parameters to those found in the list of names provided.

classmethod quantities_available() → list[str]

Obtain a list of all available (public) output quantities.

These are the class’s @cached_quantity and @subframework definitions, read without instantiating the class.

update(**kwargs)

Update parameters of the framework with kwargs.

The update is atomic: if setting any parameter, or the validation of the new set of parameters, fails, all parameters are restored to their values from before the call, and the error is re-raised.

Parameters:

**kwargs – New values of parameters of the framework. A key <name>_params, where <name> is a sub-framework, takes a dict of parameters with which to update that sub-framework.

Raises:

ValueError – If a key is not a parameter of the framework (or a sub-framework’s _params), or if the new parameters fail validation.

validate()

Perform validation of the input parameters as they relate to each other.