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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def coinc(self, s0, s1, slide, step):
 # pylint:disable=unused-argument """Calculate the final coinc ranking statistic"""

        # 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