FitnessFunc

class astropy.stats.FitnessFunc(p0=0.05, gamma=None, ncp_prior=None)

Bases: object

Base class for bayesian blocks fitness functions

Derived classes should overload the following method:

fitness(self, **kwargs):

Compute the fitness given a set of named arguments. Arguments accepted by fitness must be among [T_k, N_k, a_k, b_k, c_k] (See [1] for details on the meaning of these parameters).

Additionally, other methods may be overloaded as well:

__init__(self, **kwargs):

Initialize the fitness function with any parameters beyond the normal p0 and gamma.

validate_input(self, t, x, sigma):

Enable specific checks of the input data (t, x, sigma) to be performed prior to the fit.

compute_ncp_prior(self, N): If ncp_prior is not defined explicitly,

this function is called in order to define it before fitting. This may be calculated from gamma, p0, or whatever method you choose.

p0_prior(self, N):

Specify the form of the prior given the false-alarm probability p0 (See [1] for details).

For examples of implemented fitness functions, see Events, RegularEvents, and PointMeasures.

References

[1] (1,2)

Scargle, J et al. (2013) https://ui.adsabs.harvard.edu/abs/2013ApJ…764..167S

Methods Summary

compute_ncp_prior(N)	If ncp_prior is not explicitly defined, compute it from gamma or p0.
fit(t[, x, sigma])	Fit the Bayesian Blocks model given the specified fitness function.
fitness(**kwargs)
p0_prior(N)	Empirical prior, parametrized by the false alarm probability p0 See eq.
validate_input(t[, x, sigma])	Validate inputs to the model.

Methods Documentation

compute_ncp_prior(N)

If ncp_prior is not explicitly defined, compute it from gamma or p0.

fit(t, x=None, sigma=None)

Fit the Bayesian Blocks model given the specified fitness function.

Parameters:

tnumpy:array_like

data times (one dimensional, length N)

xnumpy:array_like, optional

data values

sigmanumpy:array_like or python:float, optional

data errors

Returns:

edgesndarray

array containing the (M+1) edges defining the M optimal bins

fitness(**kwargs)

p0_prior(N)

Empirical prior, parametrized by the false alarm probability p0 See eq. 21 in Scargle (2013)

Note that there was an error in this equation in the original Scargle paper (the “log” was missing). The following corrected form is taken from https://arxiv.org/abs/1304.2818

validate_input(t, x=None, sigma=None)

Validate inputs to the model.

Parameters:

tnumpy:array_like

times of observations

xnumpy:array_like, optional

values observed at each time

sigmapython:float or numpy:array_like, optional

errors in values x

Returns:

t, x, sigmanumpy:array_like, python:float or python:None

validated and perhaps modified versions of inputs