def fit(type): spatial_model = get_spatial(type) print 'Fitting %s with %s spatial model' % (name,type) roi.modify(which=name, spatial_model=spatial_model) likelihood_state.restore(just_spectra=True) roi.fit() if isinstance(roi.get_source(name), PointSource): roi.localize(which=name, update=True) else: roi.fit_extension(which=name) roi.fit() roi.print_summary(galactic=True) results[type] = dict(pointlike=sourcedict(roi,name)) gtlike = Gtlike(roi, enable_edisp=(args.edisp==True), binsz=0.05, chatter=4, minbinsz=0.05, rfactor=2, ) like = gtlike.like like.fit(covar=True) results[type]['gtlike'] = sourcedict(like,name) savedict('results_%s.yaml' % istr,results)
def gtlike_analysis(roi, name, plotdir): gtlike = Gtlike(roi) like = gtlike print_summary = lambda: print_summary(like, maxdist=10) print_summary() paranoid_gtlike_fit(like) print_summary() results = sourcedict(like, name) results['powerlaw_upper_limit'] = powerlaw_upper_limit( like, name, powerlaw_index=2.1, emin=emin, emax=emax, cl=.95, delta_log_like_limits=10) sed = GtlikeSED(like, name, always_upper_limit=True) sed.plot('%s/sed_gtlike_%s.png' % (plotdir, name)) sed.save('%s/sed_gtlike_%s.yaml' % (plotdir, name)) return results
def pointlike_analysis(roi, name, plotdir): print_summary = lambda: roi.print_summary(galactic=True, maxdist=10) print_summary() fit_prefactor(roi, name) roi.fit() print_summary() results = sourcedict(roi, name) results['powerlaw_upper_limit'] = powerlaw_upper_limit(roi, name, powerlaw_index=2.1, emin=emin, emax=emax, cl=.95) roi.plot_sed(which=name, filename='%s/sed_pointlike_%s.png' % (plotdir,name), use_ergs=True) return results
def gtlike_analysis(roi, name, plotdir): gtlike = Gtlike(roi) like = gtlike print_summary = lambda: print_summary(like, maxdist=10) print_summary() paranoid_gtlike_fit(like) print_summary() results=sourcedict(like, name) results['powerlaw_upper_limit'] = powerlaw_upper_limit(like, name, powerlaw_index=2.1, emin=emin, emax=emax, cl=.95, delta_log_like_limits=10) sed = GtlikeSED(like, name, always_upper_limit=True) sed.plot('%s/sed_gtlike_%s.png' % (plotdir,name)) sed.save('%s/sed_gtlike_%s.yaml' % (plotdir,name)) return results
def pointlike_analysis(roi, name, plotdir): print_summary = lambda: roi.print_summary(galactic=True, maxdist=10) print_summary() fit_prefactor(roi, name) roi.fit() print_summary() results = sourcedict(roi, name) results['powerlaw_upper_limit'] = powerlaw_upper_limit(roi, name, powerlaw_index=2.1, emin=emin, emax=emax, cl=.95) roi.plot_sed(which=name, filename='%s/sed_pointlike_%s.png' % (plotdir, name), use_ergs=True) return results
xmlfile=expandvars(join(simdir,"gtlike_model.xml")), roi_dir=roi_dir, fit_emin=10**1.75, # 56 MeV fit_emax=10**3.25, # 1778 MeV ) state=PointlikeState(roi) results=dict() print 'bins',roi.bin_edges roi.print_summary(galactic=True) results['pointlike'] = dict() results['pointlike']['mc'] = sourcedict(roi,which,errors=False) roi.fit(use_gradient=False, fit_bg_first = True) results['pointlike']['fit'] = sourcedict(roi,which) roi.print_summary(galactic=True) print roi roi.plot_counts_map(filename='counts_map.pdf') roi.print_summary(galactic=True) print roi s=roi.plot_sed(which=which,filename='sed_pointlike.pdf', use_ergs=True) pointlike_sed_to_yaml(s,'sed_pointlike.yaml') roi.toXML('results_pointlike.xml')
roi.print_summary() roi.fit() roi.print_summary() try: roi.localize(which=point, update=True) except Exception, ex: traceback.print_exc(file=sys.stdout) roi.fit() roi.print_summary() r['bin_edges'] = roi.bin_edges r['point'] = sourcedict(roi,point) r['extension_ul'] = roi.extension_upper_limit(which=point) roi.del_source(point) roi.add_source(es.copy()) roi.print_summary() roi.fit() roi.print_summary() roi.fit_extension(which=extended) roi.fit() roi.print_summary() fit_sm = roi.get_source(extended)
savedir=tempdir, use_weighted_livetime=True, ) point = PointSource(name=name, model=model_mc, skydir=roi_dir) roi = sa.roi( roi_dir = roi_dir, point_sources=[point], diffuse_sources=diffuse) r = dict() kwargs=dict(name=name, emin=1e2, emax=1e5) r['mc'] = sourcedict(roi, errors=False, **kwargs) r['mc']['galcenter_flux'] = galcenter_flux r['mc']['bg_ratio'] = bg_ratio roi.print_summary(galactic=True) roi.fit() roi.print_summary(galactic=True) roi.localize(which=name, update=True) roi.fit() roi.print_summary(galactic=True) r['point'] = sourcedict(roi, errors=True, **kwargs)
fit_emax = emax, ) roi.plot_counts_map(filename='counts_before.pdf') # print out the ROI roi.print_summary(galactic=True) print roi roi.fit(fit_bg_first=True) roi.print_summary(galactic=True) print roi # Get out the paramters for Cas A and save them to a file results['pointlike'] = sourcedict(roi, name) save() # Plot the SED pointlike_sed_to_yaml(roi.plot_sed(which=name, filename='sed_pointlike.pdf'),'sed_pointlike.yaml') gtlike = Gtlike(roi, binsz=1/8., bigger_roi=False, savedir='savedir', enable_edisp=True) like=gtlike.like like.fit(covar=True) results['gtlike'] = sourcedict(like, name) save() sed = SuperSED(like, name=name, freeze_background=False) sed.plot('sed_gtlike.pdf')
zenithcut=100, use_weighted_livetime=True, mc_energy=(args.edisp==False), ) roi = sa.roi( roi_dir=w44_2FGL.skydir, point_sources=[], diffuse_sources=diffuse_sources, ) likelihood_state = PointlikeState(roi) results = r = dict() results['mc'] = sourcedict(roi, name, errors=False) print roi def fit(type): spatial_model = get_spatial(type) print 'Fitting %s with %s spatial model' % (name,type) roi.modify(which=name, spatial_model=spatial_model) likelihood_state.restore(just_spectra=True) roi.fit() if isinstance(roi.get_source(name), PointSource): roi.localize(which=name, update=True) else: roi.fit_extension(which=name)
roi_dir=roi_dir, minROI=roi_size, maxROI=roi_size, use_weighted_livetime=True, zenithcut=100, ) roi = sa.roi(roi_dir=roi_dir, point_sources=point_sources, diffuse_sources=diffuse_sources, ) state = PointlikeState(roi) results = r = dict(argparse_kwargs(args)) mc=sourcedict(roi, name, save_TS=False, errors=False) roi.print_summary() roi.fit(use_gradient=False) roi.print_summary() fit=sourcedict(roi, name, save_TS=False) results['pointlike'] = dict(mc=mc, fit=fit) state.restore() gtlike = Gtlike(roi, savedir=fitdir, binsz=args.binsz, minbinsz=args.minbinsz, rfactor=args.rfactor,