class_docstr: Univariate density estimation by orthonormal series expansion
  
  
  Uses an orthonormal polynomial basis to approximate a univariate density.
  
  
  currently all arguments can be given to fit, I might change it to requiring
  arguments in __init__ instead.
class_docstr_tok: ["Univariate","density","estimation","by","orthonormal","series","expansion","Uses","an","orthonormal","polynomial","basis","to","approximate","a","univariate","density",".","currently","all","arguments","can","be","given","to","fit",",","I","might","change","it","to","requiring","arguments","in","__init__","instead","."]
docstr: check for bona fide density correction
  
          currently only checks that density integrates to 1
  
  `       non-negativity - NotImplementedYet
          
docstr_tok: ["check","for","bona","fide","density","correction","currently","only","checks","that","density","integrates","to","1","`","non-negativity","-","NotImplementedYet"]
code: def _verify(self):
      """check for bona fide density correction
  
              currently only checks that density integrates to 1
  
      `       non-negativity - NotImplementedYet
      """
      # watch out for circular/recursive usage
  
      # evaluate uses domain of data, we stay offset away from bounds
      intdomain = self.limits  # self.polys[0].intdomain
      self._corfactor = (
          1.0 / integrate.quad(self.evaluate, *intdomain)[0]
      )
      # self._corshift = 0
      # self._corfactor
      return self._corfactor
  
imports: from scipy import stats, integrate, special
  import numpy
label_desc: Use image node_id 5 for calling the DensityOrthoPoly obj's underlying member method code with example usage: obj._verify() and returns: self