def _process_lc_bin(itime, name, config, basedir, workdir, diff_sources, const_spectrum, roi, lck_params, **kwargs): i, time = itime roi = copy.deepcopy(roi) config = copy.deepcopy(config) config['selection']['tmin'] = time[0] config['selection']['tmax'] = time[1] # create output directories labeled in MET vals outdir = basedir + 'lightcurve_%.0f_%.0f' % (time[0], time[1]) config['fileio']['outdir'] = os.path.join(workdir, outdir) config['logging']['prefix'] = 'lightcurve_%.0f_%.0f ' % (time[0], time[1]) config['fileio']['logfile'] = os.path.join(config['fileio']['outdir'], 'fermipy.log') utils.mkdir(config['fileio']['outdir']) yaml.dump(utils.tolist(config), open(os.path.join(config['fileio']['outdir'], 'config.yaml'), 'w')) xmlfile = os.path.join(config['fileio']['outdir'], 'base.xml') try: from fermipy.gtanalysis import GTAnalysis gta = GTAnalysis(config, roi, loglevel=logging.DEBUG) gta.logger.info('Fitting time range %i %i' % (time[0], time[1])) gta.setup() except: print('Analysis failed in time range %i %i' % (time[0], time[1])) print(sys.exc_info()[0]) raise return {} gta._lck_params = lck_params # Recompute source map for source of interest and sources within 3 deg if gta.config['gtlike']['use_scaled_srcmap']: names = [s.name for s in gta.roi.get_sources(distance=3.0, skydir=gta.roi[name].skydir) if not s.diffuse] gta.reload_sources(names) # Write the current model gta.write_xml(xmlfile) # Optimize the model gta.optimize(skip=diff_sources, shape_ts_threshold=kwargs.get('shape_ts_threshold')) fit_results = _fit_lc(gta, name, **kwargs) gta.write_xml('fit_model_final.xml') srcmodel = copy.deepcopy(gta.get_src_model(name)) numfree = gta.get_free_param_vector().count(True) max_ts_thresholds = [None, 4, 9] for max_ts in max_ts_thresholds: if max_ts is not None: gta.free_sources(minmax_ts=[None, max_ts], free=False, exclude=[name]) # rerun fit using params from full time (constant) fit using same # param vector as the successful fit to get loglike specname, spectrum = const_spectrum gta.set_source_spectrum(name, spectrum_type=specname, spectrum_pars=spectrum, update_source=False) gta.free_source(name, free=False) const_fit_results = gta.fit() if not const_fit_results['fit_success']: continue const_srcmodel = gta.get_src_model(name) # rerun using shape fixed to full time fit # for the fixed-shape lightcurve gta.free_source(name, pars='norm') fixed_fit_results = gta.fit() if not fixed_fit_results['fit_success']: continue fixed_srcmodel = gta.get_src_model(name) break # special lc output o = {'flux_const': const_srcmodel['flux'], 'loglike_const': const_fit_results['loglike'], 'fit_success': fit_results['fit_success'], 'fit_success_fixed': fixed_fit_results['fit_success'], 'fit_quality': fit_results['fit_quality'], 'fit_status': fit_results['fit_status'], 'num_free_params': numfree, 'config': config} # full flux output if fit_results['fit_success'] == 1: for k in defaults.source_flux_output.keys(): if not k in srcmodel: continue o[k] = srcmodel[k] o[k+'_fixed'] = fixed_srcmodel[k] gta.logger.info('Finished time range %i %i' % (time[0], time[1])) return o
class ExtensionFit: def __init__(self, configFile): self.gta = GTAnalysis(configFile, logging={'verbosity': 3}) self.target = None self.targetRadius = None self.distance = None self.catalog = fits.getdata('/users-data/mfalxa/code/gll_psch_v13.fit', 1) def setSourceName(self, sourceObject, newName): self.gta.delete_source(sourceObject['name']) self.gta.add_source(newName, sourceObject) ''' INITIALIZE ''' def initialize(self, sizeROI, rInner, addToROI, TSMin, debug): self.gta.setup() if self.gta.config['selection']['emin'] >= 10000: self.gta.set_parameter('galdiff', 'Scale', 30000) if debug == True: self.gta.make_plots('startAll') self.gta.residmap(prefix='startAll', make_plots=True) # Get model source names sourceList = self.gta.get_sources(exclude=['isodiff', 'galdiff']) # Delete sources unassociated with TS < 50 for i in range(len(sourceList)): if sourceList[i]['catalog']['TS_value'] < TSMin and self.catalog[ 'CLASS'][self.catalog['Source_Name'] == sourceList[i] ['name']][0] == '': self.gta.delete_source(sourceList[i]['name']) closests = self.gta.get_sources(distance=rInner, exclude=['isodiff', 'galdiff']) # Delete all unidentified sources for i in range(len(closests)): if self.catalog['CLASS'][self.catalog['Source_Name'] == closests[i] ['name']][0].isupper() == False: self.gta.delete_source(closests[i]['name']) if self.catalog['CLASS'][self.catalog['Source_Name'] == closests[i] ['name']][0] == 'SFR': self.target = closests[i] self.setSourceName(self.target, 'TESTSOURCE') # If debug, save ROI and make plots if debug == True: self.gta.write_roi('startModel') self.gta.residmap(prefix='start', make_plots=True) self.gta.make_plots('start') # Optmize spectral parameters for sources with npred > 1 self.gta.optimize(npred_threshold=1, skip=['isodiff']) # Get model source names sourceList = self.gta.get_sources(distance=sizeROI + addToROI, square=True, exclude=['isodiff', 'galdiff']) # Iterate source localizing on source list for i in range(len(sourceList)): if sourceList[i].extended == False: self.gta.localize(sourceList[i]['name'], write_fits=False, write_npy=False, update=True) # Free sources within ROI size + extra distance from center self.gta.free_sources(distance=sizeROI + addToROI, square=True) # Re-optimize ROI self.gta.optimize(skip=['isodiff']) # Save and make plots if debug if debug == True: self.gta.write_roi('modelInitialized') self.gta.residmap(prefix='initialized', make_plots=True) self.gta.make_plots('initialized') # Lock sources self.gta.free_sources(free=False) ''' OUTER REGION ''' def outerRegionAnalysis(self, sizeROI, rInner, sqrtTsThreshold, minSeparation, debug): self.gta.free_sources(distance=sizeROI, pars='norm', square=True, free=True) self.gta.free_sources(distance=rInner, free=False) self.gta.free_source('galdiff', free=True) self.gta.free_source('isodiff', free=False) # Seek new sources until none are found sourceModel = { 'SpectrumType': 'PowerLaw', 'Index': 2.0, 'Scale': 30000, 'Prefactor': 1.e-15, 'SpatialModel': 'PointSource' } newSources = self.gta.find_sources(sqrt_ts_threshold=sqrtTsThreshold, min_separation=minSeparation, model=sourceModel, **{ 'search_skydir': self.gta.roi.skydir, 'search_minmax_radius': [rInner, sizeROI] }) if len(newSources) > 0: for i in range(len(newSources)): if newSources['sources'][i]['ts'] > 100.: self.gta.set_source_spectrum( newSources['sources'][i]['name'], spectrum_type='LogParabola') self.gta.free_source(newSources['sources'][i]['name']) self.gta.fit() self.gta.free_source(newSources['sources'][i]['name'], free=False) # Optimize all ROI self.gta.optimize(skip=['isodiff']) # Save sources found if debug == True: self.gta.residmap(prefix='outer', make_plots=True) self.gta.write_roi('outerAnalysisROI') self.gta.make_plots('outer') ''' INNER REGION ''' def innerRegionAnalysis(self, sizeROI, rInner, maxIter, sqrtTsThreshold, minSeparation, dmMin, TSm1Min, TSextMin, debug): self.gta.free_sources(distance=sizeROI, square=True, free=False) self.gta.free_sources(distance=rInner, free=True, exclude=['isodiff']) # Keep closest source if identified with star forming region in catalog or look for new source closest to center within Rinner if self.target != None: print('Closest source identified with star forming region : ', self.target['name']) self.gta.set_source_morphology('TESTSOURCE', **{'spatial_model': 'PointSource'}) else: closeSources = self.gta.find_sources(sqrt_ts_threshold=2., min_separation=minSeparation, max_iter=1, **{ 'search_skydir': self.gta.roi.skydir, 'search_minmax_radius': [0., rInner] }) dCenter = np.array([]) for i in range(len(closeSources['sources'])): dCenter = np.append( dCenter, self.gta.roi.skydir.separation( closeSources['sources'][i].skydir).value) self.target = closeSources['sources'][np.argmin(dCenter)] print('Target name : ', self.target['name']) self.setSourceName(self.target, 'TESTSOURCE') for i in [ x for x in range(len(closeSources['sources'])) if x != (np.argmin(dCenter)) ]: self.gta.delete_source(closeSources['sources'][i]['name']) self.gta.optimize(skip=['isodiff']) # Initialize n sources array nSources = [] # Save ROI without extension fit self.gta.write_roi('nSourcesFit') if debug == True: self.gta.make_plots('innerInit') self.gta.residmap(prefix='innerInit', make_plots=True) # Test for extension extensionTest = self.gta.extension('TESTSOURCE', make_plots=True, write_npy=debug, write_fits=debug, spatial_model='RadialDisk', update=True, free_background=True, fit_position=True) extLike = extensionTest['loglike_ext'] TSext = extensionTest['ts_ext'] print('TSext : ', TSext) extAIC = 2 * (len(self.gta.get_free_param_vector()) - self.gta._roi_data['loglike']) self.gta.write_roi('extFit') if debug == True: self.gta.residmap(prefix='ext0', make_plots=True) self.gta.make_plots('ext0') self.gta.load_roi('nSourcesFit', reload_sources=True) for i in range(1, maxIter + 1): # Test for n point sources nSourcesTest = self.gta.find_sources( sources_per_iter=1, sqrt_ts_threshold=sqrtTsThreshold, min_separation=minSeparation, max_iter=1, **{ 'search_skydir': self.gta.roi.skydir, 'search_minmax_radius': [0., rInner] }) if len(nSourcesTest['sources']) > 0: if nSourcesTest['sources'][0]['ts'] > 100.: self.gta.set_source_spectrum( nSourcesTest['sources'][0]['name'], spectrum_type='LogParabola') self.gta.free_source(nSourcesTest['sources'][0]['name']) self.gta.fit() self.gta.free_source(nSourcesTest['sources'][0]['name'], free=False) if debug == True: self.gta.make_plots('nSources' + str(i)) nSources.append(nSourcesTest['sources']) self.gta.localize(nSourcesTest['sources'][0]['name'], write_npy=debug, write_fits=debug, update=True) nAIC = 2 * (len(self.gta.get_free_param_vector()) - self.gta._roi_data['loglike']) self.gta.free_source(nSourcesTest['sources'][0]['name'], free=True) self.gta.residmap(prefix='nSources' + str(i), make_plots=True) self.gta.write_roi('n1SourcesFit') # Estimate Akaike Information Criterion difference between both models dm = extAIC - nAIC print('AIC difference between both models = ', dm) # Estimate TS_m+1 extensionTestPlus = self.gta.extension( 'TESTSOURCE', make_plots=True, write_npy=debug, write_fits=debug, spatial_model='RadialDisk', update=True, free_background=True, fit_position=True) TSm1 = 2 * (extensionTestPlus['loglike_ext'] - extLike) print('TSm+1 = ', TSm1) if debug == True: self.gta.residmap(prefix='ext' + str(i), make_plots=True) self.gta.make_plots('ext' + str(i)) if dm < dmMin and TSm1 < TSm1Min: self.gta.load_roi('extFit', reload_sources=True) break else: # Set extension test to current state and save current extension fit ROI and load previous nSources fit ROI extensionTest = extensionTestPlus extLike = extensionTestPlus['loglike_ext'] TSext = extensionTestPlus['ts_ext'] print('TSext : ', TSext) extAIC = 2 * (len(self.gta.get_free_param_vector()) - self.gta._roi_data['loglike']) self.gta.write_roi('extFit') self.gta.load_roi('n1SourcesFit', reload_sources=True) self.gta.write_roi('nSourcesFit') else: if TSext > TSextMin: self.gta.load_roi('extFit', reload_sources=True) break else: self.gta.load_roi('nSourcesFit', reload_sources=True) break self.gta.fit() # Get source radius depending on spatial model endSources = self.gta.get_sources() for i in range(len(endSources)): if endSources[i]['name'] == 'TESTSOURCE': self.target = endSources[i] self.distance = self.gta.roi.skydir.separation( endSources[i].skydir).value if endSources[i].extended == True: self.targetRadius = endSources[i]['SpatialWidth'] else: self.targetRadius = endSources[i]['pos_r95'] ''' CHECK OVERLAP ''' def overlapDisk(self, rInner, radiusCatalog): print('Target radius : ', self.targetRadius) # Check radius sizes if radiusCatalog < self.targetRadius: r = float(radiusCatalog) R = float(self.targetRadius) else: r = float(self.targetRadius) R = float(radiusCatalog) # Estimating overlapping area d = self.distance print('Distance from center : ', d) if d < (r + R): if R < (r + d): area = r**2 * np.arccos( (d**2 + r**2 - R**2) / (2 * d * r)) + R**2 * np.arccos( (d**2 + R**2 - r**2) / (2 * d * R)) - 0.5 * np.sqrt( (-d + r + R) * (d + r - R) * (d - r + R) * (d + r + R)) overlap = round((area / (np.pi * r**2)) * 100, 2) else: area = np.pi * r**2 overlap = 100.0 else: area = 0. overlap = 0. print('Overlapping surface : ', area) print('Overlap : ', overlap) if overlap > 68. and self.distance < rInner: associated = True else: associated = False return associated ''' CHECK UPPER LIMIT ''' def upperLimit(self, name, radius): sourceModel = { 'SpectrumType': 'PowerLaw', 'Index': 2.0, 'Scale': 30000, 'Prefactor': 1.e-15, 'SpatialModel': 'RadialDisk', 'SpatialWidth': radius, 'glon': self.gta.config['selection']['glon'], 'glat': self.gta.config['selection']['glat'] } self.gta.add_source(name, sourceModel, free=True) self.gta.fit() self.gta.residmap(prefix='upperLimit', make_plots=True) print('Upper limit : ', self.gta.get_sources()[0]['flux_ul95'])
def _process_lc_bin(itime, name, config, basedir, workdir, diff_sources, const_spectrum, roi, lck_params, **kwargs): i, time = itime roi = copy.deepcopy(roi) config = copy.deepcopy(config) config['selection']['tmin'] = time[0] config['selection']['tmax'] = time[1] # create output directories labeled in MET vals outdir = basedir + 'lightcurve_%.0f_%.0f' % (time[0], time[1]) config['fileio']['outdir'] = os.path.join(workdir, outdir) config['logging']['prefix'] = 'lightcurve_%.0f_%.0f ' % (time[0], time[1]) config['fileio']['logfile'] = os.path.join(config['fileio']['outdir'], 'fermipy.log') utils.mkdir(config['fileio']['outdir']) yaml.dump(utils.tolist(config), open(os.path.join(config['fileio']['outdir'], 'config.yaml'), 'w')) xmlfile = os.path.join(config['fileio']['outdir'], 'base.xml') try: from fermipy.gtanalysis import GTAnalysis gta = GTAnalysis(config, roi, loglevel=logging.DEBUG) gta.logger.info('Fitting time range %i %i' % (time[0], time[1])) gta.setup() except: print('Analysis failed in time range %i %i' % (time[0], time[1])) print(sys.exc_info()[0]) raise return {} gta._lck_params = lck_params # Recompute source map for source of interest and sources within 3 deg if gta.config['gtlike']['use_scaled_srcmap']: names = [s.name for s in gta.roi.get_sources(distance=3.0, skydir=gta.roi[name].skydir) if not s.diffuse] gta.reload_sources(names) # Write the current model gta.write_xml(xmlfile) # Optimize the model gta.optimize(skip=diff_sources, shape_ts_threshold=kwargs.get('shape_ts_threshold')) fit_results = _fit_lc(gta, name, **kwargs) gta.write_xml('fit_model_final.xml') srcmodel = copy.deepcopy(gta.get_src_model(name)) numfree = gta.get_free_param_vector().count(True) const_srcmodel = gta.get_src_model(name).copy() fixed_fit_results = fit_results.copy() fixed_srcmodel = gta.get_src_model(name).copy() fixed_fit_results['fit_success'],fixed_srcmodel['fit_success'] = [False,False] fixed_fit_results['fit_quality'],fixed_srcmodel['fit_quality'] = [0,0] max_ts_thresholds = [None, 4, 9, 16, 25] for max_ts in max_ts_thresholds: if max_ts is not None: gta.free_sources(minmax_ts=[None, max_ts], free=False, exclude=[name]) # rerun fit using params from full time (constant) fit using same # param vector as the successful fit to get loglike specname, spectrum = const_spectrum gta.set_source_spectrum(name, spectrum_type=specname, spectrum_pars=spectrum, update_source=False) gta.free_source(name, free=False) const_fit_results = gta.fit() if not const_fit_results['fit_success']: continue const_srcmodel = gta.get_src_model(name) # rerun using shape fixed to full time fit # for the fixed-shape lightcurve gta.free_source(name, pars='norm') fixed_fit_results = gta.fit() if not fixed_fit_results['fit_success']: continue fixed_srcmodel = gta.get_src_model(name) break # special lc output o = {'flux_const': const_srcmodel['flux'], 'loglike_const': const_fit_results['loglike'], 'fit_success': fit_results['fit_success'], 'fit_success_fixed': fixed_fit_results['fit_success'], 'fit_quality': fit_results['fit_quality'], 'fit_status': fit_results['fit_status'], 'num_free_params': numfree, 'config': config} # full flux output if fit_results['fit_success'] == 1: for k in defaults.source_flux_output.keys(): if not k in srcmodel: continue o[k] = srcmodel[k] o[k+'_fixed'] = fixed_srcmodel[k] gta.logger.info('Finished time range %i %i' % (time[0], time[1])) return o