Bases: object
Base class for bayesian blocks fitness functions
Derived classes should overload the following method:
Additionally, other methods may be overloaded as well:
For examples of implemented fitness functions, see Events, RegularEvents, and PointMeasures.
References
[Scargle2012] | Scargle, J et al. (2012) http://adsabs.harvard.edu/abs/2012arXiv1207.5578S |
Methods Summary
compute_ncp_prior(N) | If ncp_prior is not explicitly defined, compute it from gamma or p0. |
fit(t[, x, sigma]) | Fit the Bayesian Blocks model given the specified fitness function. |
fitness(**kwargs) | |
p0_prior(N) | Empirical prior, parametrized by the false alarm probability p0 See eq. |
validate_input(t[, x, sigma]) | Validate inputs to the model. |
Methods Documentation
If ncp_prior is not explicitly defined, compute it from gamma or p0.
Fit the Bayesian Blocks model given the specified fitness function.
Parameters: | t : array_like
x : array_like (optional)
sigma : array_like or float (optional)
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Returns: | edges : ndarray
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Empirical prior, parametrized by the false alarm probability p0 See eq. 21 in Scargle (2012)
Note that there was an error in this equation in the original Scargle paper (the “log” was missing). The following corrected form is taken from http://arxiv.org/abs/1304.2818
Validate inputs to the model.
Parameters: | t : array_like
x : array_like (optional)
sigma : float or array_like (optional)
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Returns: | t, x, sigma : array_like, float or None
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