RegularEvents¶

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

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 2012). 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(self, T_k, N_k) validate_input(self, t, x, sigma) Validate inputs to the model.

Methods Documentation

fitness(self, T_k, N_k)[source]
validate_input(self, 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, sigmaarray_like, float or None

validated and perhaps modified versions of inputs