Revert of Iteratively increase sample size for good/bad classification.

scripts/slave/recipe_modules/auto_bisect/resources/significantly_different_inner.py

-"""This file is meant to be run in an environment where scipy is available."""
-import json
-import logging
-import sys
-
-try:
-  from scipy import stats
-except ImportError:
-  def main():
-    # scipy required, see module docstring.
-    logging.warning(sys.modules[__name__].__doc__)
-    return 1
-else:
-
-  def main():
-    if len(sys.argv) < 4:
-      return 1
-    _, list_a, list_b, significance = sys.argv[:4]
-    list_a = json.loads(list_a)
-    list_b = json.loads(list_b)
-    significance = float(significance)
-
-    shapiro_p_value = stats.shapiro(list_a)[1], stats.shapiro(list_b)[1]
-    mann_whitney_p_value = stats.mannwhitneyu(list_a, list_b).pvalue
-    anderson_p_value = stats.anderson_ksamp([list_a, list_b]).significance_level
-    welch_p_value = stats.ttest_ind(list_a, list_b, equal_var=False)[1]
-
-    results = {
-        'first_sample': list_a,
-        'second_sample': list_b,
-        'shapiro_p_value': shapiro_p_value,
-        'mann_p_value': mann_whitney_p_value,
-        'anderson_p_value': anderson_p_value,
-        'welch_p_value': welch_p_value,
-    }
-
-    if (results['shapiro_p_value'][0] < significance and
-        results['shapiro_p_value'][1] < significance):
-      results['normal-y'] = True
-    else:
-      results['normal-y'] = False
-    results['significantly_different'] = bool(
-        float(results['mann_p_value']) < float(significance))
-
-    print json.dumps(results)
-    return 0
-
-if __name__ == '__main__':
-  sys.exit(main())