Events#
- class astropy.stats.Events(p0: float = 0.05, gamma: float | None = None, ncp_prior: float | None = None)[source]#
Bases:
FitnessFuncBayesian blocks fitness for binned or unbinned events.
- Parameters:
- p0
python: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,
p0does 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 ofgammaorncp_priorto use for a desired false alarm rate.- gamma
python: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
python:float, optional If specified, use the value of
ncp_priorto compute the prior as above, using the definition \({\tt ncp\_prior} = -\ln({\tt gamma})\). Ifncp_prioris specified,gammaandp0is 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:
- tnumpy:array_like
times of observations
- xnumpy:array_like, optional
values observed at each time
- sigma
python:floator numpy:array_like, optional errors in values x
- Returns:
- t, x, sigmanumpy:array_like,
python:float validated and perhaps modified versions of inputs
- t, x, sigmanumpy:array_like,