BaseFittingFunction¶

class hmf.mass_function.fitting_functions.BaseFittingFunction(nu2: ndarray, m: ndarray | None = None, z: float = 0.0, n_eff: ndarray | None = None, mass_definition: BaseMassDefinition | None = None, cosmo: FLRW = FlatLambdaCDM(name='Planck18', H0=<Quantity 67.66 km / (Mpc s)>, Om0=0.30966, Tcmb0=<Quantity 2.7255 K>, Neff=3.046, m_nu=<Quantity [0., 0., 0.06] eV>, Ob0=0.04897), delta_c: float = 1.68647, **model_parameters)[source]¶

Base-class for a halo mass function fit.

This class should not be called directly, rather use a subclass which is specific to a certain fitting formula. The only property necessary to define for any subclass is fsigma, as well as a dictionary of default parameters as a class variable _defaults. Model parameters defined here are accessed through the params instance attribute (and may be overridden at instantiation by the user). A subclass may optionally define a cutmask property, to override the default behaviour of returning True for the whole range.

In addition, the class attributes req_mass and req_neff say whether a subclass needs m and n_eff to be passed. These must be set accordingly.

Every subclass is registered as a plugin (unless defined with abstract=True), so it can be used by name, e.g. MassFunction(hmf_model="MySMT"). When a subclass is defined, a table of its _defaults is added to the Notes section of its docstring, and undocumented fsigma and cutmask overrides get the docstrings defined here.

Examples

The following defines the Sheth-Mo-Tormen mass function (which is already included as SMT), showing the basic idea of subclassing this class:

>>> from typing import ClassVar
>>> import numpy as np
>>> from hmf.mass_function.fitting_functions import BaseFittingFunction
>>> class MySMT(BaseFittingFunction):
...     # Default parameters
...     _defaults: ClassVar[dict[str, float]] = {"a": 0.707, "p": 0.3, "A": 0.3222}
...
...     @property
...     def fsigma(self):
...         A = self.params["A"]
...         a = self.params["a"]
...         p = self.params["p"]
...         return (
...             A
...             * np.sqrt(2.0 * a / np.pi)
...             * self.nu
...             * np.exp(-(a * self.nu2) / 2.0)
...             * (1 + (1.0 / (a * self.nu2)) ** p)
...         )
>>> fit = MySMT(nu2=np.array([1.0, 4.0, 9.0]), a=0.75)
>>> fit.params["a"]
0.75
>>> fit.fsigma.shape
(3,)

In that example, we did not specify cutmask, so all elements are considered to be within the fitted range.

Parameters:

nu2array_like: A vector of peak-heights, \(\delta_c^2/\sigma^2\) corresponding to m
marray_like, optional: A vector of halo masses [units M_sun/h]. Only necessary if req_mass is True. Typically provides limits of applicability. Must correspond to nu2.
zfloat, optional: The redshift. Only used by models that depend on redshift. Default is 0.
n_effarray_like, optional: The effective spectral index at m. Only required if req_neff is True.
mass_definitionhmf.halos.mass_definitions.MassDefinition instance: A halo mass definition. Only required for fits which explicitly include a parameterization for halo definition.
cosmoastropy.cosmology.FLRW instance, optional: A cosmology. Default is hmf.cosmology.cosmo.DEFAULT_COSMOLOGY (Planck18), as for the frameworks. Either omegam_z or cosmo is required if req_omz is True. If both are passed, omegam_z takes precedence.
**model_parametersunpacked-dictionary: These parameters are model-specific. The available parameters and their defaults are listed in the Notes section of each model (and are also available as <model>._defaults).

Attributes

`cutmask`	Boolean mask of the elements within the fit's calibrated range.
`fsigma`	The function \(f(\sigma)\equiv\nu f(\nu)\).
`lnsigma`	Negative log of sigma.
`normalized`	Whether this model is normalized so that all mass is in halos
`nu`	The peak height, \(\nu = \delta_c/\sigma\).
`omegam_z`	Normalised matter density at current redshift.
`references`	References to cite when this model is used, each a formatted citation string.
`req_mass`	Whether m is required for this subclass
`req_neff`	Whether n_eff is required for this subclass
`sigma`	The sqrt of mass variance as a function of mass.
`sim_definition`	Details of the defining simulation, instance of `SimDetails`

Methods

classmethod get_measured_mdef()[source]¶: Get the mass definition used in the defining simulation.

classmethod get_models() → Mapping[str, type]¶

Get a read-only mapping of all implemented models for this component.

The mapping is a live view of the registry, so it shows models registered later, but it can not be used to add or remove models.