code: def stratify_s(self):
  
  		
  
  		pscore_order = self.raw_data['pscore'].argsort()
  		pscore = self.raw_data['pscore'][pscore_order]
  		D = self.raw_data['D'][pscore_order]
  		logodds = np.log(pscore / (1-pscore))
  		K = self.raw_data['K']
  
  		blocks_uniq = set(select_blocks(pscore, logodds, D, K, 0, 1))
  		self.blocks = sorted(blocks_uniq)
  		self.stratify()
docs: Stratifies the sample based on propensity score using the
  		bin selection procedure suggested by [1]_.
  
  		The bin selection algorithm is based on a sequence of
  		two-sample t tests performed on the log-odds ratio.
  
  		This method should only be executed after the propensity score
  		has been estimated.
  
  		References
  		----------
  		.. [1] Imbens, G. & Rubin, D. (2015). Causal Inference in
  			Statistics, Social, and Biomedical Sciences: An
  			Introduction.