code: def coinc(self, s0, s1, slide, step): # pylint:disable=unused-argument
          
          # Approximate log likelihood ratio by summing single-ifo negative
          # log noise likelihoods
          loglr = - s0 - s1
          # add squares of threshold stat values via idealized Gaussian formula
          threshes = [self.fits_by_tid[i]['thresh'] for i in self.ifos]
          loglr += sum([t**2. / 2. for t in threshes])
          # convert back to a coinc-SNR-like statistic
          # via log likelihood ratio \propto rho_c^2 / 2
          return (2. * loglr) ** 0.5
docs: Calculate the final coinc ranking statistic