def _fit_position_tsmap(self, name, **kwargs): """Localize a source from its TS map.""" prefix = kwargs.get('prefix', '') dtheta_max = kwargs.get('dtheta_max', 0.5) zmin = kwargs.get('zmin', -3.0) kw = { 'map_size': 2.0 * dtheta_max, 'write_fits': kwargs.get('write_fits', False), 'write_npy': kwargs.get('write_npy', False), 'use_pylike': kwargs.get('use_pylike', True), 'max_kernel_radius': self.config['tsmap']['max_kernel_radius'], 'loglevel': logging.DEBUG } src = self.roi.copy_source(name) if src['SpatialModel'] in ['RadialDisk', 'RadialGaussian']: kw['max_kernel_radius'] = max(kw['max_kernel_radius'], 2.0 * src['SpatialWidth']) skydir = kwargs.get('skydir', src.skydir) tsmap = self.tsmap(utils.join_strings([prefix, name.lower(). replace(' ', '_')]), model=src.data, map_skydir=skydir, exclude=[name], make_plots=False, **kw) # Find peaks with TS > 4 peaks = find_peaks(tsmap['ts'], 4.0, 0.2) peak_best = None o = {} for p in sorted(peaks, key=lambda t: t['amp'], reverse=True): xy = p['ix'], p['iy'] ts_value = tsmap['ts'].data[xy[1], xy[0]] posfit = fit_error_ellipse(tsmap['ts'], xy=xy, dpix=2, zmin=max(zmin, -ts_value * 0.5)) offset = posfit['skydir'].separation(self.roi[name].skydir).deg if posfit['fit_success'] and posfit['fit_inbounds']: peak_best = p break if peak_best is None: ts_value = np.max(tsmap['ts'].data) posfit = fit_error_ellipse(tsmap['ts'], dpix=2, zmin=max(zmin, -ts_value * 0.5)) o.update(posfit) pix = posfit['skydir'].to_pixel(self.geom.wcs) o['xpix'] = float(pix[0]) o['ypix'] = float(pix[1]) o['skydir'] = posfit['skydir'].transform_to('icrs') o['pos_offset'] = posfit['skydir'].separation( self.roi[name].skydir).deg o['loglike'] = 0.5 * posfit['zoffset'] o['tsmap'] = tsmap['ts'] return o
def _fit_position_tsmap(self, name, **kwargs): """Localize a source from its TS map.""" prefix = kwargs.get('prefix', '') dtheta_max = kwargs.get('dtheta_max', 0.5) zmin = kwargs.get('zmin', -3.0) kw = { 'map_size': 2.0 * dtheta_max, 'write_fits': kwargs.get('write_fits', False), 'write_npy': kwargs.get('write_npy', False), 'use_pylike': kwargs.get('use_pylike', True), 'max_kernel_radius': self.config['tsmap']['max_kernel_radius'], 'loglevel': logging.DEBUG } src = self.roi.copy_source(name) if src['SpatialModel'] in ['RadialDisk', 'RadialGaussian']: kw['max_kernel_radius'] = max(kw['max_kernel_radius'], 2.0 * src['SpatialWidth']) skydir = kwargs.get('skydir', src.skydir) tsmap = self.tsmap(utils.join_strings([prefix, name.lower(). replace(' ', '_')]), model=src.data, map_skydir=skydir, exclude=[name], make_plots=False, **kw) # Find peaks with TS > 4 peaks = find_peaks(tsmap['ts'], 4.0, 0.2) peak_best = None o = {} for p in sorted(peaks, key=lambda t: t['amp'], reverse=True): xy = p['ix'], p['iy'] ts_value = tsmap['ts'].counts[xy[1], xy[0]] posfit = fit_error_ellipse(tsmap['ts'], xy=xy, dpix=2, zmin=max(zmin, -ts_value * 0.5)) offset = posfit['skydir'].separation(self.roi[name].skydir).deg if posfit['fit_success'] and posfit['fit_inbounds']: peak_best = p break if peak_best is None: ts_value = np.max(tsmap['ts'].counts) posfit = fit_error_ellipse(tsmap['ts'], dpix=2, zmin=max(zmin, -ts_value * 0.5)) o.update(posfit) pix = posfit['skydir'].to_pixel(self._skywcs) o['xpix'] = float(pix[0]) o['ypix'] = float(pix[1]) o['skydir'] = posfit['skydir'].transform_to('icrs') o['pos_offset'] = posfit['skydir'].separation( self.roi[name].skydir).deg o['loglike'] = 0.5 * posfit['zoffset'] o['tsmap'] = tsmap['ts'] return o
def _localize_tsmap(self, name, **kwargs): """Localize a source from its TS map.""" prefix = kwargs.get('prefix', '') dtheta_max = kwargs.get('dtheta_max', 0.5) write_fits = kwargs.get('write_fits', False) write_npy = kwargs.get('write_npy', False) src = self.roi.copy_source(name) skydir = src.skydir skywcs = self._skywcs tsmap = self.tsmap(utils.join_strings( [prefix, name.lower().replace(' ', '_')]), model=src.data, map_skydir=skydir, map_size=2.0 * dtheta_max, exclude=[name], write_fits=write_fits, write_npy=write_npy, make_plots=False) posfit, skydir = fit_error_ellipse(tsmap['ts'], dpix=2) pix = skydir.to_pixel(skywcs) o = {} o.update(posfit) o['xpix'] = float(pix[0]) o['ypix'] = float(pix[1]) return o, tsmap
def _build_src_dicts_from_peaks(self, peaks, maps, src_dict_template): tsmap = maps['ts'] amp = maps['amplitude'] src_dicts = [] names = [] for p in peaks: o = fit_error_ellipse(tsmap, (p['ix'], p['iy']), dpix=2) skydir = o['skydir'] p['fit_loc'] = o p['fit_skydir'] = o['skydir'] p.update(o) name = utils.create_source_name(skydir) src_dict = copy.deepcopy(src_dict_template) norm_par = get_function_norm_par_name( src_dict_template['SpectrumType']) src_dict.update({ norm_par: amp.data[p['iy'], p['ix']], 'ra': skydir.icrs.ra.deg, 'dec': skydir.icrs.dec.deg }) src_dict['glon_err'] = o['glon_err'] src_dict['glat_err'] = o['glat_err'] src_dict['ra_err'] = o['ra_err'] src_dict['dec_err'] = o['dec_err'] src_dict['pos_err'] = o['pos_err'] src_dict['pos_err_semimajor'] = o['pos_err_semimajor'] src_dict['pos_err_semiminor'] = o['pos_err_semiminor'] src_dict['pos_r68'] = o['pos_r68'] src_dict['pos_r95'] = o['pos_r95'] src_dict['pos_r99'] = o['pos_r99'] src_dict['pos_angle'] = o['pos_angle'] src_dict['pos_gal_cov'] = o['pos_gal_cov'] src_dict['pos_gal_corr'] = o['pos_gal_corr'] src_dict['pos_cel_cov'] = o['pos_cel_cov'] src_dict['pos_cel_corr'] = o['pos_cel_corr'] self.logger.info('Found source\n' + 'name: %s\n' % name + 'ts: %f' % p['amp']**2) names.append(name) src_dicts.append(src_dict) return names, src_dicts
def _build_src_dicts_from_peaks(self, peaks, maps, src_dict_template): tsmap = maps['ts'] amp = maps['amplitude'] src_dicts = [] names = [] for p in peaks: o = fit_error_ellipse(tsmap, (p['ix'], p['iy']), dpix=2) skydir = o['skydir'] p['fit_loc'] = o p['fit_skydir'] = o['skydir'] p.update(o) name = utils.create_source_name(skydir) src_dict = copy.deepcopy(src_dict_template) norm_par = get_function_norm_par_name( src_dict_template['SpectrumType']) src_dict.update({norm_par: amp.counts[p['iy'], p['ix']], 'ra': skydir.icrs.ra.deg, 'dec': skydir.icrs.dec.deg}) src_dict['glon_err'] = o['glon_err'] src_dict['glat_err'] = o['glat_err'] src_dict['ra_err'] = o['ra_err'] src_dict['dec_err'] = o['dec_err'] src_dict['pos_err'] = o['pos_err'] src_dict['pos_err_semimajor'] = o['pos_err_semimajor'] src_dict['pos_err_semiminor'] = o['pos_err_semiminor'] src_dict['pos_r68'] = o['pos_r68'] src_dict['pos_r95'] = o['pos_r95'] src_dict['pos_r99'] = o['pos_r99'] src_dict['pos_angle'] = o['pos_angle'] src_dict['pos_gal_cov'] = o['pos_gal_cov'] src_dict['pos_gal_corr'] = o['pos_gal_corr'] src_dict['pos_cel_cov'] = o['pos_cel_cov'] src_dict['pos_cel_corr'] = o['pos_cel_corr'] self.logger.info('Found source\n' + 'name: %s\n' % name + 'ts: %f' % p['amp'] ** 2) names.append(name) src_dicts.append(src_dict) return names, src_dicts
def _build_src_dicts_from_peaks(self, peaks, maps, src_dict_template): tsmap = maps['ts'] amp = maps['amplitude'] src_dicts = [] names = [] for p in peaks: o, skydir = fit_error_ellipse(tsmap, (p['ix'], p['iy']), dpix=2) p['fit_loc'] = o p['fit_skydir'] = skydir p.update(o) if o['fit_success']: skydir = p['fit_skydir'] else: skydir = p['skydir'] name = utils.create_source_name(skydir) src_dict = copy.deepcopy(src_dict_template) src_dict.update({ 'Prefactor': amp.counts[p['iy'], p['ix']], 'ra': skydir.icrs.ra.deg, 'dec': skydir.icrs.dec.deg }) src_dict['pos_sigma'] = o['sigma'] src_dict['pos_sigma_semimajor'] = o['sigma_semimajor'] src_dict['pos_sigma_semiminor'] = o['sigma_semiminor'] src_dict['pos_r68'] = o['r68'] src_dict['pos_r95'] = o['r95'] src_dict['pos_r99'] = o['r99'] src_dict['pos_angle'] = np.degrees(o['theta']) self.logger.info('Found source\n' + 'name: %s\n' % name + 'ts: %f' % p['amp']**2) names.append(name) src_dicts.append(src_dict) return names, src_dicts
def _fit_position_scan(self, name, **kwargs): zmin = kwargs.get('zmin', -9.0) tsmap, loglike = self._scan_position(name, **kwargs) ts_value = np.max(tsmap.data) posfit = fit_error_ellipse(tsmap, dpix=2, zmin=max(zmin, -ts_value * 0.5)) pix = posfit['skydir'].to_pixel(self.geom.wcs) o = {} o.update(posfit) o['xpix'] = float(pix[0]) o['ypix'] = float(pix[1]) o['skydir'] = posfit['skydir'].transform_to('icrs') o['pos_offset'] = posfit['skydir'].separation( self.roi[name].skydir).deg o['loglike'] = 0.5 * posfit['zoffset'] + loglike o['tsmap'] = tsmap return o
def _fit_position_scan(self, name, **kwargs): zmin = kwargs.get('zmin', -9.0) tsmap, loglike = self._scan_position(name, **kwargs) ts_value = np.max(tsmap.counts) posfit = fit_error_ellipse(tsmap, dpix=2, zmin=max(zmin, -ts_value * 0.5)) pix = posfit['skydir'].to_pixel(self._skywcs) o = {} o.update(posfit) o['xpix'] = float(pix[0]) o['ypix'] = float(pix[1]) o['skydir'] = posfit['skydir'].transform_to('icrs') o['pos_offset'] = posfit['skydir'].separation( self.roi[name].skydir).deg o['loglike'] = 0.5 * posfit['zoffset'] + loglike o['tsmap'] = tsmap return o
def localize(self, name, **kwargs): """Find the best-fit position of a source. Localization is performed in two steps. First a TS map is computed centered on the source with half-width set by ``dtheta_max``. A fit is then performed to the maximum TS peak in this map. The source position is then further refined by scanning the likelihood in the vicinity of the peak found in the first step. The size of the scan region is set to encompass the 99% positional uncertainty contour as determined from the peak fit. Parameters ---------- name : str Source name. dtheta_max : float Maximum offset in RA/DEC in deg from the nominal source position that will be used to define the boundaries of the TS map search region. nstep : int Number of steps in longitude/latitude that will be taken when refining the source position. The bounds of the scan range are set to the 99% positional uncertainty as determined from the TS map peak fit. The total number of sampling points will be nstep**2. fix_background : bool Fix background parameters when fitting the source position. update : bool Update the model for this source with the best-fit position. If newname=None this will overwrite the existing source map of this source with one corresponding to its new location. newname : str Name that will be assigned to the relocalized source when update=True. If newname is None then the existing source name will be used. make_plots : bool Generate plots. write_fits : bool Write the output to a FITS file. write_npy : bool Write the output dictionary to a numpy file. optimizer : dict Dictionary that overrides the default optimizer settings. Returns ------- localize : dict Dictionary containing results of the localization analysis. This dictionary is also saved to the dictionary of this source in 'localize'. """ name = self.roi.get_source_by_name(name).name schema = ConfigSchema(self.defaults['localize'], optimizer=self.defaults['optimizer']) schema.add_option('make_plots', False) schema.add_option('write_fits', True) schema.add_option('write_npy', True) schema.add_option('newname', name) schema.add_option('prefix', '') config = utils.create_dict(self.config['localize'], optimizer=self.config['optimizer']) config = schema.create_config(config, **kwargs) nstep = config['nstep'] dtheta_max = config['dtheta_max'] update = config['update'] newname = config['newname'] prefix = config['prefix'] self.logger.info('Running localization for %s' % name) saved_state = LikelihoodState(self.like) if config['fix_background']: self.free_sources(free=False, loglevel=logging.DEBUG) src = self.roi.copy_source(name) skydir = src.skydir skywcs = self._skywcs src_pix = skydir.to_pixel(skywcs) tsmap_fit, tsmap = self._localize_tsmap(name, prefix=prefix, dtheta_max=dtheta_max) self.logger.debug( 'Completed localization with TS Map.\n' '(ra,dec) = (%10.4f,%10.4f)\n' '(glon,glat) = (%10.4f,%10.4f)', tsmap_fit['ra'], tsmap_fit['dec'], tsmap_fit['glon'], tsmap_fit['glat']) # Fit baseline (point-source) model self.free_norm(name) fit_output = self._fit(loglevel=logging.DEBUG, **config['optimizer']) # Save likelihood value for baseline fit loglike0 = fit_output['loglike'] self.logger.debug('Baseline Model Likelihood: %f', loglike0) self.zero_source(name) o = { 'name': name, 'config': config, 'fit_success': True, 'loglike_base': loglike0, 'loglike_loc': np.nan, 'dloglike_loc': np.nan } cdelt0 = np.abs(skywcs.wcs.cdelt[0]) cdelt1 = np.abs(skywcs.wcs.cdelt[1]) scan_step = 2.0 * tsmap_fit['r95'] / (nstep - 1.0) self.logger.debug( 'Refining localization search to ' 'region of width: %.4f deg', tsmap_fit['r95']) scan_map = Map.create(SkyCoord(tsmap_fit['ra'], tsmap_fit['dec'], unit='deg'), scan_step, (nstep, nstep), coordsys=wcs_utils.get_coordsys(skywcs)) scan_skydir = scan_map.get_pixel_skydirs() lnlscan = dict(wcs=scan_map.wcs.to_header().items(), loglike=np.zeros((nstep, nstep)), dloglike=np.zeros((nstep, nstep)), dloglike_fit=np.zeros((nstep, nstep))) for i, t in enumerate(scan_skydir): model_name = '%s_localize' % (name.replace(' ', '').lower()) src.set_name(model_name) src.set_position(t) self.add_source(model_name, src, free=True, init_source=False, save_source_maps=False, loglevel=logging.DEBUG) fit_output = self._fit(loglevel=logging.DEBUG, **config['optimizer']) loglike1 = fit_output['loglike'] lnlscan['loglike'].flat[i] = loglike1 self.delete_source(model_name, loglevel=logging.DEBUG) lnlscan['dloglike'] = lnlscan['loglike'] - np.max(lnlscan['loglike']) scan_tsmap = Map(2.0 * lnlscan['dloglike'].T, scan_map.wcs) self.unzero_source(name) saved_state.restore() self._sync_params(name) self._update_roi() scan_fit, new_skydir = fit_error_ellipse(scan_tsmap, dpix=3) o.update(scan_fit) o['loglike_loc'] = np.max( lnlscan['loglike']) + 0.5 * scan_fit['offset'] o['dloglike_loc'] = o['loglike_loc'] - o['loglike_base'] # lnlscan['dloglike_fit'] = \ # utils.parabola(np.linspace(0,nstep-1.0,nstep)[:,np.newaxis], # np.linspace(0,nstep-1.0,nstep)[np.newaxis,:], # *scan_fit['popt']).reshape((nstep,nstep)) o['lnlscan'] = lnlscan # Best fit position and uncertainty from fit to TS map o['tsmap_fit'] = tsmap_fit # Best fit position and uncertainty from pylike scan o['scan_fit'] = scan_fit pix = new_skydir.to_pixel(skywcs) o['xpix'] = float(pix[0]) o['ypix'] = float(pix[1]) o['deltax'] = (o['xpix'] - src_pix[0]) * cdelt0 o['deltay'] = (o['ypix'] - src_pix[1]) * cdelt1 o['offset'] = skydir.separation(new_skydir).deg if o['offset'] > dtheta_max: o['fit_success'] = False if not o['fit_success']: self.logger.error( 'Localization failed.\n' '(ra,dec) = (%10.4f,%10.4f)\n' '(glon,glat) = (%10.4f,%10.4f)\n' 'offset = %8.4f deltax = %8.4f ' 'deltay = %8.4f', o['ra'], o['dec'], o['glon'], o['glat'], o['offset'], o['deltax'], o['deltay']) else: self.logger.info( 'Localization succeeded with ' 'coordinates:\n' '(ra,dec) = (%10.4f,%10.4f)\n' '(glon,glat) = (%10.4f,%10.4f)\n' 'offset = %8.4f r68 = %8.4f', o['ra'], o['dec'], o['glon'], o['glat'], o['offset'], o['r68']) self.roi[name]['localize'] = copy.deepcopy(o) if config['make_plots']: self._plotter.make_localization_plots(o, tsmap, self.roi, prefix=prefix, skydir=scan_skydir) if update and o['fit_success']: self.logger.info('Updating source %s ' 'to localized position.', name) src = self.delete_source(name) src.set_position(new_skydir) src.set_name(newname, names=src.names) self.add_source(newname, src, free=True) fit_output = self.fit(loglevel=logging.DEBUG) o['loglike_loc'] = fit_output['loglike'] o['dloglike_loc'] = o['loglike_loc'] - o['loglike_base'] src = self.roi.get_source_by_name(newname) self.roi[newname]['localize'] = copy.deepcopy(o) self.logger.info('LogLike: %12.3f DeltaLogLike: %12.3f', o['loglike_loc'], o['dloglike_loc']) if o['fit_success']: src = self.roi.get_source_by_name(newname) src['pos_sigma'] = o['sigma'] src['pos_sigma_semimajor'] = o['sigma_semimajor'] src['pos_sigma_semiminor'] = o['sigma_semiminor'] src['pos_r68'] = o['r68'] src['pos_r95'] = o['r95'] src['pos_r99'] = o['r99'] src['pos_angle'] = np.degrees(o['theta']) self.logger.info('Finished localization.') return o