def alphavsks(self,autozoom=True,**kwargs):
pylab.plot(self._alpha_values, self._xmin_kstest, '<STR_LIT:.>')<EOL>pylab.errorbar(self._alpha, self._ks, xerr=self._alphaerr, fmt='<STR_LIT:+>')<EOL>ax=pylab.gca()<EOL>if autozoom:<EOL><INDENT>ax.set_ylim(<NUM_LIT>*(self._ks),<NUM_LIT:3>*(self._ks))<EOL>ax.set_xlim((self._alpha)-<NUM_LIT:5>*self._alphaerr,(self._alpha)+<NUM_LIT:5>*self._alphaerr)<EOL><DEDENT>ax.set_ylabel("<STR_LIT>")<EOL>ax.set_xlabel(r'<STR_LIT>')<EOL>pylab.draw()<EOL>return ax<EOL>
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.