# RegularEvents¶

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.

Parameters:
dtfloat

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.

Parameters:
tarray_like

times of observations

xarray_like, optional

values observed at each time

sigmafloat or array_like, optional

errors in values x

Returns:
t, x, sigma

validated and perhaps modified versions of inputs