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

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.


tick rate for data

p0float, 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.

ncp_priorfloat, 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, N_k)[source]
validate_input(t, x, sigma)[source]

Validate inputs to the model.


times of observations

xarray_like, optional

values observed at each time

sigmafloat or array_like, optional

errors in values x

t, x, sigmaarray_like, float or None

validated and perhaps modified versions of inputs