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,
