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 false-alarm probability
p0
(See [1] for details).
For examples of implemented fitness functions, see
Events
,RegularEvents
, andPointMeasures
.References
Methods Summary
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
.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:
- tarray_like
data times (one dimensional, length N)
- xarray_like, optional
data values
- sigmaarray_like or
float
, optional data errors
- Returns:
- edges
ndarray
array containing the (M+1) edges defining the M optimal bins
- edges
- p0_prior(N)[source]#
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)[source]#
Validate inputs to the model.
- Parameters:
- tarray_like
times of observations
- xarray_like, optional
values observed at each time
- sigma
float
or array_like, optional errors in values x
- Returns:
- t, x, sigmaarray_like,
float
orNone
validated and perhaps modified versions of inputs
- t, x, sigmaarray_like,