old_contents: import unittest
  import numpy as np
  import pandas as pd
  import neurokit as nk
  import matplotlib
  
  class TestStatistics(unittest.TestCase):
  
  #==============================================================================
  # STATISTICS
  #==============================================================================
      def test_mad(self):
          self.assertEqual(nk.mad([1, 2, 3, 4, 5, 6, 7]), 2.0)
  
      def test_z_score(self):
          raw_scores = [1, 1, 5, 2, 1]
          z = nk.z_score(raw_scores)
          self.assertEqual(int(z[3]), 0)
  
      def test_find_outliers(self):
          outliers = nk.find_outliers([1, 2, 1, 5, 666, 4, 1 ,3, 5])
          self.assertEqual(outliers[4], True)
  
      def test_normal_range(self):
          bottom, top = nk.normal_range(mean=100, sd=15, treshold=2)
          self.assertEqual(bottom, 70)
  
  #==============================================================================
  # ROUTINES
  #==============================================================================
      def test_compute_dprime(self):
          parameters = nk.compute_dprime(n_Hit=7, n_Miss=4, n_FA=6, n_CR=6)
          self.assertEqual(np.round(parameters["bppd"], 2), -0.5)
  
      def test_compute_BMI(self):
          self.assertEqual(round(nk.BMI(182, 70, 27, "m")['BMI_old'], 2), 21.13)
  
      def test_compute_interoceptive_accuracy(self):
          self.assertEqual(nk.compute_interoceptive_accuracy(5, 3), 0.5)
  
  #==============================================================================
  # Plots
  #==============================================================================
      def test_plot_polarbar(self):
          fig = nk.plot_polarbar([1, 2, 3], show=False)
          self.assertIsInstance(fig, matplotlib.figure.Figure)
          
  if __name__ == '__main__':
      unittest.main()
new_contents: import unittest
  import numpy as np
  import pandas as pd
  import neurokit as nk
  import matplotlib
  
  class TestStatistics(unittest.TestCase):
  
  #==============================================================================
  # STATISTICS
  #==============================================================================
      def test_mad(self):
          self.assertEqual(nk.mad([1, 2, 3, 4, 5, 6, 7]), 2.0)
  
      def test_z_score(self):
          raw_scores = [1, 1, 5, 2, 1]
          z = nk.z_score(raw_scores)
          self.assertEqual(int(z[3]), 0)
  
      def test_find_outliers(self):
          outliers = nk.find_outliers([1, 2, 1, 5, 666, 4, 1 ,3, 5])
          self.assertEqual(outliers[4], True)
  
      def test_normal_range(self):
          bottom, top = nk.normal_range(mean=100, sd=15, treshold=2)
          self.assertEqual(bottom, 70)
  
  #==============================================================================
  # ROUTINES
  #==============================================================================
      def test_compute_dprime(self):
          parameters = nk.compute_dprime(n_Hit=7, n_Miss=4, n_FA=6, n_CR=6)
          self.assertEqual(np.round(parameters["bppd"], 2), -0.5)
  
      def test_compute_BMI(self):
          self.assertEqual(round(nk.BMI(182, 70, 27, "m")['BMI_old'], 2), 21.13)
  
      def test_compute_interoceptive_accuracy(self):
          self.assertEqual(nk.compute_interoceptive_accuracy(5, 3), 0.5)
  
  #==============================================================================
  # Plots
  #==============================================================================
      def test_plot_polarbar(self):
          fig = nk.plot_polarbar([1, 2, 3])
          self.assertIsInstance(fig, matplotlib.figure.Figure)
          
  if __name__ == '__main__':
      unittest.main()
subject: test hotfix