halomod.bias.UnityBias¶

class halomod.bias.UnityBias(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: Bias

A toy bias model which is exactly unity for all mass.

See documentation for Bias for information on input parameters. This model has no free parameters.

bias()[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 = (<class 'hmf.mass_function.fitting_functions.PS'>, <class 'hmf.mass_function.fitting_functions.SMT'>, <class 'hmf.mass_function.fitting_functions.ST'>, <class 'hmf.mass_function.fitting_functions.Peacock'>, <class 'hmf.mass_function.fitting_functions.Tinker10'>, <class 'hmf.mass_function.fitting_functions.Manera'>)¶: The HMF model that pairs with this bias in the peak-background split