class_docstr: Fitness for binned or unbinned events
  
  Parameters
  ----------
  p0 : float
      False alarm probability, used to compute the prior on N
      (see eq. 21 of Scargle 2012).  Default prior is for p0 = 0.
  gamma : float or None
      If specified, then use this gamma to compute the general prior form,
      p ~ gamma^N.  If gamma is specified, p0 is ignored.
class_docstr_tok: ["Fitness","for","binned","or","unbinned","events","Parameters","--","--","--","--","--","p0",":","float","False","alarm","probability",",","used","to","compute","the","prior","on","N","(","see","eq",".","21","of","Scargle","2012",")",".","Default","prior","is","for","p0","=","0.","gamma",":","float","or","None","If","specified",",","then","use","this","gamma","to","compute","the","general","prior","form",",","p","~","gamma^N",".","If","gamma","is","specified",",","p0","is","ignored","."]
code: def prior(self, N, Ntot):
      if self.gamma is not None:
          return self.gamma_prior(N, Ntot)
      else:
          # eq. 21 from Scargle 2012
          return 4 - np.log(73.53 * self.p0 * (N**-0.478))
  
imports: import numpy
  from astroML.utils import deprecated
  from astroML.utils.exceptions import AstroMLDeprecationWarning
label_desc: Use image node_id 2 for calling the Events obj's underlying member method code with example usage: obj.prior(N, Ntot) and returns: self, unknown