FitnessFunc¶

class
astropy.stats.
FitnessFunc
(p0=0.05, gamma=None, ncp_prior=None)[source]¶ 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
andgamma
. 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)
: Ifncp_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 falsealarm probability
p0
(See [1] for details).
For examples of implemented fitness functions, see
Events
,RegularEvents
, andPointMeasures
.References
[1] (1, 2, 3) Scargle, J et al. (2012) http://adsabs.harvard.edu/abs/2012arXiv1207.5578S Methods Summary
compute_ncp_prior
(N)If ncp_prior
is not explicitly defined, compute it fromgamma
orp0
.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

fit
(t, x=None, sigma=None)[source]¶ Fit the Bayesian Blocks model given the specified fitness function.
Parameters:  t : array_like
data times (one dimensional, length N)
 x : array_like (optional)
data values
 sigma : array_like or float (optional)
data errors
Returns:  edges : ndarray
array containing the (M+1) edges defining the M optimal bins

p0_prior
(N)[source]¶ Empirical prior, parametrized by the false alarm probability
p0
See eq. 21 in Scargle (2012)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)[source]¶ Validate inputs to the model.
Parameters:  t : array_like
times of observations
 x : array_like (optional)
values observed at each time
 sigma : float or array_like (optional)
errors in values x
Returns:  t, x, sigma : array_like, float or None
validated and perhaps modified versions of inputs