Events#
- class astropy.stats.Events(p0: float = 0.05, gamma: float | None = None, ncp_prior: float | None = None)[source]#
Bases:
FitnessFunc
Bayesian blocks fitness for binned or unbinned events.
- Parameters:
- p0
float
, optional False alarm probability, used to compute the prior on \(N_{\rm blocks}\) (see eq. 21 of Scargle 2013). For the Events type data,
p0
does not seem to be an accurate representation of the actual false alarm probability. If you are using this fitness function for a triggering type condition, it is recommended that you run statistical trials on signal-free noise to determine an appropriate value ofgamma
orncp_prior
to use for a desired false alarm rate.- gamma
float
, optional If specified, then use this gamma to compute the general prior form, \(p \sim {\tt gamma}^{N_{\rm blocks}}\). If gamma is specified, p0 is ignored.
- ncp_prior
float
, optional If specified, use the value of
ncp_prior
to compute the prior as above, using the definition \({\tt ncp\_prior} = -\ln({\tt gamma})\). Ifncp_prior
is specified,gamma
andp0
is ignored.
- p0
Methods Summary
fitness
(N_k, T_k)validate_input
(t, x, sigma)Validate inputs to the model.
Methods Documentation
- validate_input(t: ArrayLike, x: ArrayLike | None, sigma: float | ArrayLike | None) tuple[NDArray[float], NDArray[float], NDArray[float]] [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
validated and perhaps modified versions of inputs
- t, x, sigmaarray_like,