old_contents: #!/bin/env python
  ''' Wrapper for R models to allow comparison to scipy models '''
  
  import numpy as np
  
  from rpy import r
  
  from exampledata import x, y
  
  class RModel(object):
      ''' Class gives R models scipy.models -like interface '''
      def __init__(self, y, design, model_type=r.lm):
          ''' Set up and estimate R model with data and design '''
          self.y = y
          self.design = design
          self.model_type = model_type
          self._design_cols = ['x.%d' % (i+1) for i in range(
              self.design.shape[1])]
          # Note the '-1' for no intercept - this is included in the design
          self.formula = r('y ~ %s-1' % '+'.join(self._design_cols))
          self.frame = r.data_frame(y=y, x=self.design)
          self.results = self.model_type(self.formula,
                                      data = self.frame)
          # Provide compatible interface with scipy models
          coeffs = self.results['coefficients']
          self.beta = np.array([coeffs[c] for c in self._design_cols])
          self.resid = self.results['residuals']
          self.predict = self.results['fitted.values']
          self.df_resid = self.results['df.residual']
  
          
  
new_contents: ''' Wrapper for R models to allow comparison to scipy models '''
  
  import numpy as np
  
  from rpy import r
  
  from exampledata import x, y
  
  class RModel(object):
      ''' Class gives R models scipy.models -like interface '''
      def __init__(self, y, design, model_type=r.lm):
          ''' Set up and estimate R model with data and design '''
          self.y = y
          self.design = design
          self.model_type = model_type
          self._design_cols = ['x.%d' % (i+1) for i in range(
              self.design.shape[1])]
          # Note the '-1' for no intercept - this is included in the design
          self.formula = r('y ~ %s-1' % '+'.join(self._design_cols))
          self.frame = r.data_frame(y=y, x=self.design)
          self.results = self.model_type(self.formula,
                                      data = self.frame)
          # Provide compatible interface with scipy models
          coeffs = self.results['coefficients']
          self.beta = np.array([coeffs[c] for c in self._design_cols])
          self.resid = self.results['residuals']
          self.predict = self.results['fitted.values']
          self.df_resid = self.results['df.residual']
  
          
  
subject: Remove unnecessary preamble.