RegularEvents¶
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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)Methods Documentation
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fitness
(T_k, N_k)[source] [edit on github]¶
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validate_input
(t, x, sigma)[source] [edit on github]¶
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