plfit/plfit.py
def xminvsks(self, **kwargs):
        """
        Plot xmin versus the ks value for derived alpha.  This plot can be used
        as a diagnostic of whether you have derived the 'best' fit: if there are
        multiple local minima, your data set may be well suited to a broken
        powerlaw or a different function.
        """

        pylab.plot(self._xmins,self._xmin_kstest,'.')
        pylab.plot(self._xmin,self._ks,'s')
        #pylab.errorbar([self._ks],self._alpha,yerr=self._alphaerr,fmt='+')

        ax=pylab.gca()
        ax.set_ylabel("KS statistic")
        ax.set_xlabel("min(x)")
        pylab.draw()

        return ax
def xminvsks(self, **kwargs):
        """
        Plot xmin versus the ks value for derived alpha.  This plot can be used
        as a diagnostic of whether you have derived the 'best' fit: if there are
        multiple local minima, your data set may be well suited to a broken
        powerlaw or a different function.
        """

        pylab.plot(self._xmins,self._xmin_kstest,'.')
        pylab.plot(self._xmin,self._ks,'s')
        #pylab.errorbar([self._ks],self._alpha,yerr=self._alphaerr,fmt='+')

        ax=pylab.gca()
        ax.set_ylabel("KS statistic")
        ax.set_xlabel("min(x)")
        pylab.draw()

        return ax
Plot xmin versus the ks value for derived alpha.  This plot can be used
        as a diagnostic of whether you have derived the 'best' fit: if there are
        multiple local minima, your data set may be well suited to a broken
        powerlaw or a different function.