def lightcurve(self, name, **kwargs):
        """Generate a lightcurve for the named source. The function will
        complete the basic analysis steps for each bin and perform a
        likelihood fit for each bin. Extracted values (along with
        errors) are Integral Flux, spectral model, Spectral index, TS
        value, pred. # of photons. Note: successful calculation of 
        TS:subscript:`var` requires at least one free background 
        parameter and a previously optimized ROI model.

        Parameters
        ---------
        name: str
            source name

        {options}

        Returns
        ---------
        LightCurve : dict
           Dictionary containing output of the LC analysis

        """

        name = self.roi.get_source_by_name(name).name

        # Create schema for method configuration
        schema = ConfigSchema(self.defaults['lightcurve'],
                              optimizer=self.defaults['optimizer'])
        schema.add_option('prefix', '')
        config = utils.create_dict(self.config['lightcurve'],
                                   optimizer=self.config['optimizer'])
        config = schema.create_config(config, **kwargs)

        self.logger.info('Computing Lightcurve for %s' % name)

        o = self._make_lc(name, **config)
        filename = utils.format_filename(self.workdir, 'lightcurve',
                                         prefix=[config['prefix'],
                                                 name.lower().replace(' ', '_')])

        o['file'] = None
        if config['write_fits']:
            o['file'] = os.path.basename(filename) + '.fits'
            self._make_lc_fits(o, filename + '.fits', **config)

        if config['write_npy']:
            np.save(filename + '.npy', o)

        self.logger.info('Finished Lightcurve')

        return o
Generate a lightcurve for the named source. The function will
        complete the basic analysis steps for each bin and perform a
        likelihood fit for each bin. Extracted values (along with
        errors) are Integral Flux, spectral model, Spectral index, TS
        value, pred. # of photons. Note: successful calculation of 
        TS:subscript:`var` requires at least one free background 
        parameter and a previously optimized ROI model.

        Parameters
        ---------
        name: str
            source name

        {options}

        Returns
        ---------
        LightCurve : dict
           Dictionary containing output of the LC analysis