old_contents: import unittest
  import numpy as np
  from scipy.stats import norm
  
  from tools.match_evaluation import get_player_final_utilities_from_log_file, get_player_utilities_from_log_file, get_logs_data, calculate_confidence_interval
  
  
  class MatchEvaluationTests(unittest.TestCase):
      def test_log_reading(self):
          log_file_path = 'test/sample_log-large.log'
          final_scores, final_scores_player_names = get_player_final_utilities_from_log_file(log_file_path)
          utilities, utilities_player_names = get_player_utilities_from_log_file(log_file_path)
  
          self.assertEqual(utilities.shape, (50000, 2))
          self.assertEqual(final_scores_player_names, utilities_player_names)
          self.assertTrue(np.all(final_scores == np.sum(utilities, axis=0)))
  
      def test_confidence_interval_calculation(self):
          log_files_paths = [
              'test/match_evaluation/sample_log_1.log',
              'test/match_evaluation/sample_log_2.log'
          ]
          log_readings = [get_player_utilities_from_log_file(log_file_path) for log_file_path in log_files_paths]
          data, _ = get_logs_data(*log_readings)
          means, interval_half_size, lower_bounds, upper_bounds = calculate_confidence_interval(data, 0.95)
          self.assertEqual(means.tolist(), [1, -1])
  
          sigma = np.sqrt(8)
          sme = sigma / np.sqrt(10)
          z = norm.ppf(0.975)
  
          interval_size = z * sme
  
          self.assertEqual(interval_half_size.tolist(), [interval_size] * 2)
          self.assertEqual(lower_bounds.tolist(), [mean - interval_size for mean in means])
          self.assertEqual(upper_bounds.tolist(), [mean + interval_size for mean in means])
  
new_contents: import unittest
  import numpy as np
  from scipy.stats import norm
  
  from tools.match_evaluation import get_player_final_utilities_from_log_file, get_player_utilities_from_log_file, get_logs_data, calculate_confidence_interval
  
  
  class MatchEvaluationTests(unittest.TestCase):
      def test_log_reading(self):
          log_file_path = 'test/sample_log-large.log'
          final_scores, final_scores_player_names = get_player_final_utilities_from_log_file(log_file_path)
          utilities, utilities_player_names = get_player_utilities_from_log_file(log_file_path)
  
          self.assertEqual(utilities.shape, (50000, 2, 1))
          self.assertEqual(final_scores_player_names, utilities_player_names)
          self.assertTrue(np.all([[score] for score in final_scores] == np.sum(utilities, axis=0)))
  
      def test_confidence_interval_calculation(self):
          log_files_paths = [
              'test/match_evaluation/sample_log_1.log',
              'test/match_evaluation/sample_log_2.log'
          ]
          log_readings = [get_player_utilities_from_log_file(log_file_path) for log_file_path in log_files_paths]
          data, _ = get_logs_data(*log_readings)
          means, interval_half_size, lower_bounds, upper_bounds = calculate_confidence_interval(data, 0.95)
          self.assertEqual(means.tolist(), [[1], [-1]])
  
          sigma = np.sqrt(8)
          sme = sigma / np.sqrt(10)
          z = norm.ppf(0.975)
  
          interval_size = z * sme
  
          self.assertEqual(interval_half_size.tolist(), [interval_size] * 2)
          self.assertEqual(lower_bounds.tolist(), [mean - interval_size for mean in means])
          self.assertEqual(upper_bounds.tolist(), [mean + interval_size for mean in means])
  
subject: Fix tests