class_docstr: Class to compute the empirical null normal fit to the data.
  
  The data which is used to estimate the FDR, assuming a Gaussian null
  from Schwartzmann et al., NeuroImage 44 (2009) 71--82
class_docstr_tok: ["Class","to","compute","the","empirical","null","normal","fit","to","the","data",".","The","data","which","is","used","to","estimate","the","FDR",",","assuming","a","Gaussian","null","from","Schwartzmann","et","al.",",","NeuroImage","44","(","2009",")","71","--","82"]
docstr: Returns the FDR associated with any point of self.x
docstr_tok: ["Returns","the","FDR","associated","with","any","point","of","self.x"]
code: def fdrcurve(self):
      """
      Returns the FDR associated with any point of self.x
      """
      import scipy.stats as st
  
      if self.learned == 0:
          self.learn()
      efp = (
          self.p0
          * st.norm.sf(self.x, self.mu, self.sigma)
          * self.n
          / np.arange(self.n, 0, -1)
      )
      efp = np.minimum(efp, 1)
      ix = np.argsort(self.x)
      for i in range(np.size(efp) - 1, 0, -1):
          efp[ix[i - 1]] = np.maximum(efp[ix[i]], efp[ix[i - 1]])
      self.sorted_x = self.x[ix]
      self.sorted_fdr = efp[ix]
      return efp
  
file_path: nipy/nipy/algorithms/statistics/empirical_pvalue.py
imports: import numpy
  import scipy.stats
  from numpy.linalg import pinv
label_desc: Use image node_id 3 for calling the NormalEmpiricalNull obj's underlying member method code with example usage: obj.fdrcurve() and returns: efp