class astropy.stats.RegularEvents(dt, p0=0.05, gamma=None, ncp_prior=None)[source] [edit on github]

Bases: astropy.stats.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.


dt : float

tick rate for data

p0 : float (optional)

False alarm probability, used to compute the prior on \(N_{\rm blocks}\) (see eq. 21 of Scargle 2012). 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 \({\tt ncp\_prior} = -\ln({\tt gamma})\). If ncp_prior is specified, gamma and p0 are ignored.

Methods Summary

fitness(T_k, N_k)
validate_input(t, x, sigma)

Methods Documentation

fitness(T_k, N_k)[source] [edit on github]
validate_input(t, x, sigma)[source] [edit on github]