halomod.bias.Bias

class halomod.bias.Bias(nu: ~numpy.ndarray, delta_c: float = 1.686, m: ~numpy.ndarray | None = None, mstar: float | None = None, delta_halo: float | None = 200, n: float | None = 1, sigma_8: float | None = 0.8, cosmo: ~astropy.cosmology.flrw.base.FLRW = FlatLambdaCDM(name='Planck15', H0=<Quantity 67.74 km / (Mpc s)>, Om0=0.3075, Tcmb0=<Quantity 2.7255 K>, Neff=3.046, m_nu=<Quantity [0., 0., 0.06] eV>, Ob0=0.0486), n_eff: None | ~numpy.ndarray = None, z: float = 0.0, **model_parameters)[source]

Bases: Component

The base Bias component.

This class should not be instantiated directly! Use a subclass that implements a specific bias model. The parameters listed below are the input parameters for all bias models. Extra model-specific parameters can be given – these are documented in their respective class docstring.

Parameters:

  • nu (array-like) – Peak-height, delta^2_c/sigma^2.

  • delta_c (float, optional) – Critical over-density for collapse. Not all bias components require this parameter.

  • m (array, optional) – Vector of halo masses corresponding to nu. Not all bias components require this parameter.

  • mstar (float, optional) – Nonlinear mass, defined by the relation sigma(mstar) = delta_c, with sigma the mass variance in spheres corresponding to virial radii of halos of mass mstar.

  • delta_halo (float, optional) – The over-density of halos with respect to the mean background matter density.

  • n (float, optional) – The spectral index of the linear matter power spectrum..

  • Om0 (float, optional) – The matter density, as a fraction of critical density, in the current universe.

  • sigma_8 (float, optional) – The square root of the mass in spheres of radius 8 Mpc/h in the present day (normalizes the power spectrum).

  • h (float, optional) – Hubble parameter in units of 100 km/s/Mpc.

bias() → ndarray[source]

Calculate the first-order, linear, deterministic halo bias.

Returns:

b – The bias as a function of mass, as an array of values corresponding to the instance attributes m and/or nu.

Return type:

array-like

Examples

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from halomod.bias import Mo96
>>> peak_height = np.linspace(0.1, 2, 100)
>>> bias = Mo96(nu=peak_height)
>>> plt.plot(peak_height, bias.bias())
classmethod get_models() → Dict[str, Type]

Get a dictionary of all implemented models for this component.

pair_hmf = ()

The HMF model that pairs with this bias in the peak-background split