RegularEvents¶
-
class
astropy.stats.
RegularEvents
(dt, p0=0.05, gamma=None, ncp_prior=None)[source]¶ 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
- 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 \({\tt ncp\_prior} = -\ln({\tt gamma})\). Ifncp_prior
is specified,gamma
andp0
are ignored.
Methods Summary
fitness
(self, T_k, N_k)validate_input
(self, t, x, sigma)Validate inputs to the model.
Methods Documentation
-
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