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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 profile_bins(self):
 """ The binning to use to do the profile fitting """

        log_mean = np.log10(self.mean())
        log_half_width = max(5. * self.sigma(), 3.)
        # Default is to profile over +-5 sigma,
        # centered on mean, using 100 bins
        return np.logspace(log_mean - log_half_width,
                           log_mean + log_half_width, 101)/self._j_ref