class_docstr: 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
  gamma : float
      specifies the prior on the number of bins: p ~ gamma^N
class_docstr_tok: ["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","gamma",":","float","specifies","the","prior","on","the","number","of","bins",":","p","~","gamma^N"]
code: def validate_input(self, t, x, sigma):
      unique_x = np.unique(x)
      if list(unique_x) not in ([0], [1], [0, 1]):
          raise ValueError("Regular events must have only 0 and 1 in x")
  
imports: import numpy
  from astroML.utils import deprecated
  from astroML.utils.exceptions import AstroMLDeprecationWarning
label_desc: Use image node_id 2 for calling the RegularEvents obj's underlying member method code with example usage: obj.validate_input(t, x, sigma) without return types