old_contents: """
  Using gap score to determine optimal cluster number
  """
  
  import unittest
  from unittest import TestCase
  from flaky import flaky
  
  import numpy as np
  import scipy
  
  from uncurl import gap_score
  
  class GapScoreTest(TestCase):
  
      def setUp(self):
          pass
  
      def test_gap_score(self):
          data_mat = scipy.io.loadmat('data/10x_pooled_400.mat')
          data = data_mat['data']
          data_tsvd = gap_score.preproc_data(data)
          max_k, gap_vals, sk_vals = gap_score.run_gap_k_selection(data_tsvd,
                  k_min=1, k_max=50, skip=5, B=5)
          # just test that the score is in a very broad range
          self.assertTrue(max_k > 3)
          self.assertTrue(max_k < 20)
  
      def test_gap_score_2(self):
          data_mat = scipy.io.loadmat('data/GSE60361_dat.mat')
          data = data_mat['Dat']
          data_tsvd = gap_score.preproc_data(data)
          max_k, gap_vals, sk_vals = gap_score.run_gap_k_selection(data_tsvd,
                  k_min=1, k_max=50, skip=5, B=5)
          self.assertTrue(max_k > 3)
          self.assertTrue(max_k < 30)
  
      @flaky(max_runs=3)
      def test_gap_score_3(self):
          data_mat = scipy.io.loadmat('data/SCDE_test.mat')
          data = data_mat['dat']
          data_tsvd = gap_score.preproc_data(data, gene_subset=True)
          max_k, gap_vals, sk_vals = gap_score.run_gap_k_selection(data_tsvd,
                  k_min=1, k_max=50, skip=5, B=5)
          self.assertTrue(max_k < 10)
  
  
  
  if __name__ == '__main__':
      unittest.main()
  
  
  
new_contents: """
  Using gap score to determine optimal cluster number
  """
  
  import unittest
  from unittest import TestCase
  from flaky import flaky
  
  import numpy as np
  import scipy
  
  from uncurl import gap_score
  
  class GapScoreTest(TestCase):
  
      def setUp(self):
          pass
  
      def test_gap_score(self):
          data_mat = scipy.io.loadmat('data/10x_pooled_400.mat')
          data = data_mat['data']
          data_tsvd = gap_score.preproc_data(data, gene_subset=True)
          max_k, gap_vals, sk_vals = gap_score.run_gap_k_selection(data_tsvd,
                  k_min=1, k_max=50, skip=5, B=5)
          # just test that the score is in a very broad range
          self.assertTrue(max_k > 3)
          self.assertTrue(max_k < 20)
  
      def test_gap_score_2(self):
          data_mat = scipy.io.loadmat('data/GSE60361_dat.mat')
          data = data_mat['Dat']
          data_tsvd = gap_score.preproc_data(data, gene_subset=True)
          max_k, gap_vals, sk_vals = gap_score.run_gap_k_selection(data_tsvd,
                  k_min=1, k_max=50, skip=5, B=5)
          self.assertTrue(max_k > 3)
          self.assertTrue(max_k < 30)
  
      @flaky(max_runs=3)
      def test_gap_score_3(self):
          data_mat = scipy.io.loadmat('data/SCDE_test.mat')
          data = data_mat['dat']
          data_tsvd = gap_score.preproc_data(data, gene_subset=True)
          max_k, gap_vals, sk_vals = gap_score.run_gap_k_selection(data_tsvd,
                  k_min=1, k_max=50, skip=5, B=5)
          self.assertTrue(max_k < 10)
  
  
  
  if __name__ == '__main__':
      unittest.main()
  
  
  
subject: test gap score