def nextToken(self):
        
        while 1:
            self._state.token = None
            self._state.channel = DEFAULT_CHANNEL
            self._state.tokenStartCharIndex = self.input.index()
            self._state.tokenStartCharPositionInLine = self.input.charPositionInLine
            self._state.tokenStartLine = self.input.line
            self._state.text = None
            if self.input.LA(1) == EOF:
                return EOF_TOKEN

            try:
                self.mTokens()
                
                if self._state.token is None:
                    self.emit()
                    
                elif self._state.token == SKIP_TOKEN:
                    continue

                return self._state.token

            except NoViableAltException, re:
                self.reportError(re)
                self.recover(re) # throw out current char and try again

            except RecognitionException, re:
                self.reportError(re)
                # match() routine has already called recover()
Add Covariance taking into account number of degrees of freedom.
dataset/ETHPy150Open AppScale/appscale/AppServer/lib/antlr3/antlr3/recognizers.py/Lexer.nextToken--crossover with--dataset/ETHPy150Open mne-tools/mne-python/mne/cov.py/Covariance.__add__