RegularEvents

class astropy.stats.RegularEvents(dt: float, p0: float = 0.05, gamma: float | None = None, ncp_prior: float | None = None)

Bases: FitnessFunc

Bayesian blocks fitness for regular events.

This is for data which has a fundamental “tick” length, so that all measured values are multiples of this tick length. In each tick, there are either zero or one counts.

Parameters:

dtpython:float

tick rate for data

p0python:float, optional

False alarm probability, used to compute the prior on \(N_{\rm blocks}\) (see eq. 21 of Scargle 2013). If gamma is specified, p0 is ignored.

gammapython: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_priorpython:float, optional

If specified, use the value of ncp_prior to compute the prior as above, using the definition \({\tt ncp\_prior} = -\ln({\tt gamma})\). If ncp_prior is specified, gamma and p0 are ignored.

Methods Summary

fitness(T_k, N_k)
validate_input(t[, x, sigma])	Validate inputs to the model.

Methods Documentation

fitness(T_k: NDArray[float], N_k: NDArray[float]) → NDArray[float]

validate_input(t: ArrayLike, x: ArrayLike | None = None, sigma: float | ArrayLike | None = None) → tuple[NDArray[float], NDArray[float], NDArray[float]]

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

validated and perhaps modified versions of inputs