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- Astropy v5.2.1 »
- Astrostatistics Tools (`astropy.stats`) »
- Events

# Events

class astropy.stats.Events(p0=0.05, gamma=None, ncp_prior=None)

  Bases: `FitnessFunc`

  Bayesian blocks fitness for binned or unbinned events

  Parameters:
    p0 `python:float`, optional

      False alarm probability, used to compute the prior on \(N_{\rm blocks}\) (see eq. 21 of Scargle 2013). For the Events type data, `p0` does not seem to be an accurate representation of the actual false alarm probability. If you are using this fitness function for a triggering type condition, it is recommended that you run statistical trials on signal-free noise to determine an appropriate value of `gamma` or `ncp_prior` to use for a desired false alarm rate.

    gamma `python:float`, 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_prior `python:float`, optional

      If specified, use the value of `ncp_prior` to compute the prior as above, using the definition \({\tt ncp\_prior} = -\ln({\tt gamma})\). If `ncp_prior` is specified, `gamma` and `p0` is ignored.

  Methods Summary

  `fitness`(N_k, T_k) |
  `validate_input`(t, x, sigma) | Validate inputs to the model.

  Methods Documentation

  fitness(N_k, T_k)
  validate_input(t, x, sigma)

    Validate inputs to the model.

    Parameters:
      t numpy:array_like

        times of observations

      x numpy:array_like, optional

        values observed at each time

      sigma `python:float` or numpy:array_like, optional

        errors in values x

    Returns:
      t, x, sigma numpy:array_like, `python:float` or `python:None`

        validated and perhaps modified versions of inputs