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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 lognoiserate(self, trigs):
 """ Calculate the log noise rate density over single-ifo newsnr Read in single trigger information, make the newsnr statistic and rescale by the fitted coefficients alpha and rate """

        alphai, ratei, thresh = self.find_fits(trigs)
        newsnr = self.get_newsnr(trigs)
        # alphai is constant of proportionality between single-ifo newsnr and
        #  negative log noise likelihood in given template
        # ratei is rate of trigs in given template compared to average
        # thresh is stat threshold used in given ifo
        lognoisel = - alphai * (newsnr - thresh) + numpy.log(alphai) + \
                      numpy.log(ratei)
        return numpy.array(lognoisel, ndmin=1, dtype=numpy.float32)