old_contents: # -*- coding: utf8 -*-
  
  from __future__ import absolute_import, division, print_function
  
  import logging
  
  from .scipy_stat_swfilter import SciPyStatSWFilter
  
  
  
  class TTestIndSWFilter(SciPyStatSWFilter):
      """
      Calculates the T-test for the means of TWO INDEPENDENT samples of scores.
  
      This is a two-sided test for the null hypothesis
      that 2 independent samples have identical
      average (expected) values.
      This test assumes that the populations
      have identical variances by default.
  
      """
  
      __logger = logging.getLogger(__name__)
  
      def aggregate_windows(self,
                            window_seq,
                            depth=0,
                            **kwargs):
  
          prev_win = None
          for window in window_seq:
              if prev_win is None:
                  prev_win = window
  
              result = self.ttest_ind(prev_win, window, equal_var=False)
              print (result)
  
              yield 1 - result.pvalue
              prev_win = window
  
new_contents: # -*- coding: utf8 -*-
  
  from __future__ import absolute_import, division, print_function
  
  import logging
  
  from .scipy_stat_swfilter import SciPyStatSWFilter
  
  
  
  class TTestIndSWFilter(SciPyStatSWFilter):
      """
      Calculates the T-test for the means
      of TWO INDEPENDENT samples of scores.
  
      This is a two-sided test for the null hypothesis
      that 2 independent samples have identical
      average (expected) values.
      This test assumes that the populations
      have identical variances by default.
  
      """
  
      __logger = logging.getLogger(__name__)
  
      def aggregate_windows(self,
                            window_seq,
                            depth=0,
                            **kwargs):
  
          prev_win = None
          for window in window_seq:
              if prev_win is None:
                  prev_win = window
  
              result = self.ttest_ind(prev_win, window, equal_var=False)
              print (result)
  
              yield 1 - result.pvalue
              prev_win = window
  
subject: add T-test for the means of TWO INDEPENDENT samples of scores.