code: def adjustHspsForPlotting(self, titleAlignments):
          
          # If we're using bit scores, there's nothing to do.
          if self.scoreClass is HigherIsBetterScore:
              return
  
          # Convert all e-values to high positive values, and keep track of the
          # maximum converted value.
          maxConvertedEValue = None
          zeroHsps = []
  
          # Note: don't call self.hsps() here because that will read them
          # from disk again, which is not what's wanted.
          for hsp in titleAlignments.hsps():
              if hsp.score.score == 0.0:
                  zeroHsps.append(hsp)
              else:
                  convertedEValue = -1.0 * log10(hsp.score.score)
                  hsp.score.score = convertedEValue
                  if (maxConvertedEValue is None or
                          convertedEValue > maxConvertedEValue):
                      maxConvertedEValue = convertedEValue
  
          if zeroHsps:
              # Save values so that we can use them in self.adjustPlot
              self._maxConvertedEValue = maxConvertedEValue
              self._zeroEValueFound = True
  
              # Adjust all zero e-value HSPs to have numerically high values.
              if self.randomizeZeroEValues:
                  for hsp in zeroHsps:
                      hsp.score.score = (maxConvertedEValue + 2 + uniform(
                          0, ZERO_EVALUE_UPPER_RANDOM_INCREMENT))
              else:
                  for count, hsp in enumerate(zeroHsps, start=1):
                      hsp.score.score = maxConvertedEValue + count
          else:
              self._zeroEValueFound = False
docs: Our HSPs are about to be plotted. If we are using e-values, these need
          to be adjusted.
  
          @param titleAlignments: An instance of L{TitleAlignment}.