plfit/plfit.py
def alphavsks(self,autozoom=True,**kwargs):
        """
        Plot alpha 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._alpha_values, self._xmin_kstest, '.')
        pylab.errorbar(self._alpha, self._ks, xerr=self._alphaerr, fmt='+')

        ax=pylab.gca()
        if autozoom:
            ax.set_ylim(0.8*(self._ks),3*(self._ks))
            ax.set_xlim((self._alpha)-5*self._alphaerr,(self._alpha)+5*self._alphaerr)
        ax.set_ylabel("KS statistic")
        ax.set_xlabel(r'$\alpha$')
        pylab.draw()

        return ax
def alphavsks(self,autozoom=True,**kwargs):
        """
        Plot alpha 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._alpha_values, self._xmin_kstest, '.')
        pylab.errorbar(self._alpha, self._ks, xerr=self._alphaerr, fmt='+')

        ax=pylab.gca()
        if autozoom:
            ax.set_ylim(0.8*(self._ks),3*(self._ks))
            ax.set_xlim((self._alpha)-5*self._alphaerr,(self._alpha)+5*self._alphaerr)
        ax.set_ylabel("KS statistic")
        ax.set_xlabel(r'$\alpha$')
        pylab.draw()

        return ax
def alphavsks ( self , autozoom = True , * * kwargs ) : pylab . plot ( self . _alpha_values , self . _xmin_kstest , '.' ) pylab . errorbar ( self . _alpha , self . _ks , xerr = self . _alphaerr , fmt = '+' ) ax = pylab . gca ( ) if autozoom : ax . set_ylim ( 0.8 * ( self . _ks ) , 3 * ( self . _ks ) ) ax . set_xlim ( ( self . _alpha ) - 5 * self . _alphaerr , ( self . _alpha ) + 5 * self . _alphaerr ) ax . set_ylabel ( "KS statistic" ) ax . set_xlabel ( r'$\alpha$' ) pylab . draw ( ) return ax
Plot alpha 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.
Plot alpha 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 .