new_contents: import pytest
  
  import numpy as np
  import spectrum_utils.spectrum as sus
  
  from ann_solo import spectrum
  from ann_solo import utils
  
  
  @pytest.fixture
  def ssms_list_without_rescore():
      # MS2PIP (HCD v20210416) simulated spectrum of HPYLEDR/2.
      mz = np.asarray(
          [
              138.066,  # b1
              235.119,  # b2
              398.182,  # b3
              511.266,  # b4
              640.309,  # b5
              755.336,  # b6
              175.119,  # y1
              290.146,  # y2
              419.188,  # y3
              532.273,  # y4
              695.336,  # y5
              792.389,  # y6
          ]
      )
      intensity = np.asarray(
          [
              0.03675187,  # b1
              0.41731364,  # b2
              0.00473946,  # b3
              0.00332476,  # b4
              0.00320261,  # b5
              0.00670335,  # b6
              0.40390085,  # y1
              0.09983288,  # y2
              0.01661951,  # y3
              0.05734070,  # y4
              0.22102276,  # y5
              0.77388125,  # y6
          ]
      )
      peak_matches = np.asarray([(i, i) for i in range(len(mz))])
      spec1 = sus.MsmsSpectrum("HPYLEDR", 465.227, 2, mz, intensity)
      intensity2 = np.copy(intensity)
      mz2 = np.copy(mz)
      ssms = []
      for index,value in enumerate(intensity2):
          intensity2[index] = value + index * 0.02
          spec2 = sus.MsmsSpectrum(
              "HPYLEDR", 465.227, 2, mz2, intensity2
          )
          ssm = spectrum.SpectrumSpectrumMatch(spec1, spec2, peak_matches)
          ssm.library_spectrum.is_decoy = False
          if index in [3, 4, 8, 9, 11]:
              ssm.library_spectrum.is_decoy = True
          ssms.append(ssm)
  
      return utils.score_ssms(
              ssms,
              0.01,
              None
          )
  
  
  def test_score_ssms(
          ssms_list_without_rescore,
  ):
      #This function tests mainly the confidence assignment module
      #Integrated within the rescoring module
      #Which invokes the SpectrumSimilarityCalculator module
      q_value_expected = [
          0.33,
          0.33,
          0.33,
          0.6,
          0.6,
          0.6,
          0.6,
          0.66,
          0.66,
          0.71,
          0.71,
          0.86
      ]
      q_value_calculated = [ssm.q for ssm in ssms_list_without_rescore]
      assert q_value_calculated == q_value_expected
  
  
subject: Add an integration test for the confidence assignment module