PointMeasures#

class astropy.stats.PointMeasures(p0: float | None = 0.05, gamma: float | None = None, ncp_prior: float | None = None)[source]#

Bases: FitnessFunc

Bayesian blocks fitness for point measures.

Parameters:
p0float, optional

False alarm probability, used to compute the prior on \(N_{\rm blocks}\) (see eq. 21 of Scargle 2013). If gamma is specified, p0 is ignored.

gammafloat, optional

If specified, then use this gamma to compute the general prior form, \(p \sim {\tt gamma}^{N_{\rm blocks}}\). 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(a_k, b_k)

validate_input(t, x, sigma)

Validate inputs to the model.

Methods Documentation

fitness(a_k: NDArray[float], b_k: ArrayLike) → NDArray[float][source]#
validate_input(t: ArrayLike, x: ArrayLike | None, sigma: float | ArrayLike | None) → tuple[NDArray[float], NDArray[float], NDArray[float]][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

validated and perhaps modified versions of inputs

Edit