RegularEvents¶
-
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.
Parameters: - 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 \({\tt ncp\_prior} = -\ln({\tt gamma})\). Ifncp_prior
is specified,gamma
andp0
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] [edit on github]¶
-
validate_input
(t, x, sigma)[source] [edit on github]¶ Validate inputs to the model.
Parameters: - t : array_like
times of observations
- x : array_like (optional)
values observed at each time
- sigma : float or array_like (optional)
errors in values x
Returns: - t, x, sigma : array_like, float or None
validated and perhaps modified versions of inputs