def run_analysis(self, argv): """Run this analysis""" args = self._parser.parse_args(argv) if not HAVE_ST: raise RuntimeError("Trying to run fermipy analysis, but don't have ST") gta = GTAnalysis(args.config, logging={'verbosity': 3}, fileio={'workdir_regex': '\.xml$|\.npy$'}) gta.setup(overwrite=False) gta.load_roi('fit_baseline') gta.print_roi() basedir = os.path.dirname(args.config) # This should be a no-op, b/c it was done in the baseline analysis gta.free_sources(skydir=gta.roi.skydir, distance=1.0, pars='norm') for profile in args.profiles: pkey, pdict = SEDAnalysis._build_profile_dict(basedir, profile) # test_case need to be a dict with spectrum and morphology gta.add_source(pkey, pdict) # refit the ROI gta.fit() # build the SED gta.sed(pkey, outfile="sed_%s.fits" % pkey) # remove the source gta.delete_source(pkey) # put the ROI back to how it was gta.load_xml('fit_baseline') return gta
def run_analysis(self, argv): """Run this analysis""" args = self._parser.parse_args(argv) if not HAVE_ST: raise RuntimeError( "Trying to run fermipy analysis, but don't have ST") workdir = os.path.dirname(args.config) _config_file = self._clone_config_and_srcmaps(args.config, args.seed) gta = GTAnalysis(_config_file, logging={'verbosity': 3}, fileio={'workdir_regex': '\.xml$|\.npy$'}) gta.load_roi(args.roi_baseline) simfile = os.path.join(workdir, 'sim_%s_%s.yaml' % (args.sim, args.sim_profile)) mcube_file = "%s_%s_%06i" % (args.sim, args.sim_profile, args.seed) sim_config = utils.load_yaml(simfile) injected_source = sim_config.get('injected_source', None) if injected_source is not None: src_dict = injected_source['source_model'] src_dict['ra'] = gta.config['selection']['ra'] src_dict['dec'] = gta.config['selection']['dec'] injected_name = injected_source['name'] gta.add_source(injected_name, src_dict) gta.write_model_map(mcube_file) mc_spec_dict = dict( true_counts=gta.model_counts_spectrum(injected_name), energies=gta.energies, model=src_dict) mcspec_file = os.path.join( workdir, "mcspec_%s_%06i.yaml" % (mcube_file, args.seed)) utils.write_yaml(mc_spec_dict, mcspec_file) else: injected_name = None gta.write_roi('sim_baseline_%06i' % args.seed) test_sources = {} for profile in args.profiles: profile_path = os.path.join(workdir, 'profile_%s.yaml' % profile) test_source = load_yaml(profile_path) test_sources[profile] = test_source first = args.seed last = first + args.nsims for seed in range(first, last): self._run_simulation(gta, args.roi_baseline, injected_name, test_sources, first, seed, non_null_src=args.non_null_src, do_find_src=args.do_find_src)
def run_analysis(self, argv): """Run this analysis""" args = self._parser.parse_args(argv) if not HAVE_ST: raise RuntimeError( "Trying to run fermipy analysis, but don't have ST") workdir = os.path.dirname(args.config) _config_file = self._clone_config_and_srcmaps(args.config, args.seed) gta = GTAnalysis(_config_file, logging={'verbosity': 3}, fileio={'workdir_regex': '\.xml$|\.npy$'}) gta.load_roi(args.roi_baseline) simfile = os.path.join(workdir, 'sim_%s_%s.yaml' % (args.sim, args.sim_profile)) mcube_file = "%s_%s_%06i" % (args.sim, args.sim_profile, args.seed) sim_config = utils.load_yaml(simfile) injected_source = sim_config.get('injected_source', None) if injected_source is not None: src_dict = injected_source['source_model'] src_dict['ra'] = gta.config['selection']['ra'] src_dict['dec'] = gta.config['selection']['dec'] injected_name = injected_source['name'] gta.add_source(injected_name, src_dict) gta.write_model_map(mcube_file) mc_spec_dict = dict(true_counts=gta.model_counts_spectrum(injected_name), energies=gta.energies, model=src_dict) mcspec_file = os.path.join(workdir, "mcspec_%s_%06i.yaml" % (mcube_file, args.seed)) utils.write_yaml(mc_spec_dict, mcspec_file) else: injected_name = None gta.write_roi('sim_baseline_%06i' % args.seed) test_sources = [] for profile in args.profiles: profile_path = os.path.join(workdir, 'profile_%s.yaml' % profile) test_source = load_yaml(profile_path) test_sources.append(test_source) first = args.seed last = first + args.nsims for seed in range(first, last): self._run_simulation(gta, args.roi_baseline, injected_name, test_sources, first, seed, non_null_src=args.non_null_src)
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'])
soi = s.name loc=gta.localize(soi, update=True, free_radius=1) fit_res = gta.optimize() gta.write_roi('fit_srcfind_loc') #check if there is a source close to the target position, if not put a test source and fit it srcname = NAME if gta.roi.sources[0]._data['offset']>gta.roi.sources[0]._data['pos_r99']: print '# No source consistent with target position after localization, creating and fitting test source... #' gta.add_source(srcname,{ 'ra' : srcRA, 'dec' : srcDec ,'SpectrumType' : 'PowerLaw', 'Index' : 2.2,'Scale' : 1000, 'Prefactor' : 5.0E-12,'SpatialModel' : 'PointSource' }) gta.free_source(srcname) gta.free_sources(pars='norm') gta.free_source('galdiff') gta.free_source('isodiff') fit_res = gta.optimize() fit_res = gta.fit() gta.write_roi('fit_testsrc') print '# Test source fitted successfully... #' if gta.roi[srcname]._data['ts']>25: print '# ...and we have a significant detection! (TS='+str(gta.roi[srcname]._data['ts'])+') Localizing... #' loc=gta.localize(srcname, update=True) if loc['fit_success']==True: print '# Localization succeeded! #' print '#Final optimization run...#' fit_res = gta.optimize()