def gtlike_analysis(pipeline, roi, name, hypothesis, upper_limit):
print 'Performing Gtlike crosscheck for %s' % hypothesis
gtlike = Gtlike(roi, savedir='savedir' if pipeline.cachedata else None)
like = gtlike.like
print 'About to fit gtlike ROI'
print summary(like, maxdist=10)
paranoid_gtlike_fit(like, verbosity=4)
print 'Done fiting gtlike ROI'
print summary(like, maxdist=10)
like.writeXml("%s/srcmodel_gtlike_%s_%s.xml" %
(pipeline.dirdict['data'], hypothesis, name))
r = source_dict(like, name)
upper_limit_kwargs = dict()
if upper_limit:
pul = GtlikePowerLawUpperLimit(like, name, cl=.95, verbosity=4)
r['powerlaw_upper_limit'] = pul.todict()
def sed(kind, **kwargs):
print 'Making %s SED' % kind
s = GtlikeSED(like,
name,
always_upper_limit=True,
verbosity=4,
upper_limit_kwargs=upper_limit_kwargs,
**kwargs)
s.plot('%s/sed_gtlike_%s_%s.png' %
(pipeline.dirdict['seds'], kind, name))
s.save('%s/sed_gtlike_%s_%s.yaml' %
(pipeline.dirdict['seds'], kind, name))
sed('1bpd_%s' % hypothesis, bin_edges=[10**2, 10**3, 10**4, 10**5.5])
sed('2bpd_%s' % hypothesis, bin_edges=np.logspace(2, 5.5, 8))
if not pipeline.fast:
sed('4bpd_%s' % hypothesis, bin_edges=np.logspace(2, 5.5, 15))
return r
def gtlike_analysis(pipeline, roi, name, hypothesis, upper_limit):
print "Performing Gtlike crosscheck for %s" % hypothesis
gtlike = Gtlike(roi, savedir="savedir" if pipeline.cachedata else None)
like = gtlike.like
print "About to fit gtlike ROI"
print summary(like, maxdist=10)
paranoid_gtlike_fit(like, verbosity=4)
print "Done fiting gtlike ROI"
print summary(like, maxdist=10)
like.writeXml("%s/srcmodel_gtlike_%s_%s.xml" % (pipeline.dirdict["data"], hypothesis, name))
r = source_dict(like, name)
upper_limit_kwargs = dict()
if upper_limit:
pul = GtlikePowerLawUpperLimit(like, name, cl=0.95, verbosity=4)
r["powerlaw_upper_limit"] = pul.todict()
def sed(kind, **kwargs):
print "Making %s SED" % kind
s = GtlikeSED(like, name, always_upper_limit=True, verbosity=4, upper_limit_kwargs=upper_limit_kwargs, **kwargs)
s.plot("%s/sed_gtlike_%s_%s.png" % (pipeline.dirdict["seds"], kind, name))
s.save("%s/sed_gtlike_%s_%s.yaml" % (pipeline.dirdict["seds"], kind, name))
sed("1bpd_%s" % hypothesis, bin_edges=[10 ** 2, 10 ** 3, 10 ** 4, 10 ** 5.5])
sed("2bpd_%s" % hypothesis, bin_edges=np.logspace(2, 5.5, 8))
if not pipeline.fast:
sed("4bpd_%s" % hypothesis, bin_edges=np.logspace(2, 5.5, 15))
return r
def gtlike_analysis(roi, name, hypothesis, max_free,
seddir, datadir, plotdir,
upper_limit=False, cutoff=False,
cutoff_model=None,
do_bandfitter=False, do_sed=False,
):
print 'Performing Gtlike crosscheck for %s' % hypothesis
frozen = freeze_far_away(roi, roi.get_source(name).skydir, max_free)
gtlike=Gtlike(roi, extended_dir_name=datadir)
unfreeze_far_away(roi, frozen)
global like
like=gtlike.like
like.tol = 1e-1 # I found that the default tol '1e-3' would get the fitter stuck in infinite loops
import pyLikelihood as pyLike
like.setFitTolType(pyLike.ABSOLUTE)
emin, emax = get_full_energy_range(like)
print 'About to fit gtlike ROI'
print summary(like, maxdist=10)
paranoid_gtlike_fit(like, verbosity=4)
print 'Done fiting gtlike ROI'
print summary(like, maxdist=10)
spectrum_name = like.logLike.getSource(name).spectrum().genericName()
like.writeXml("%s/srcmodel_gtlike_%s_%s_%s.xml"%(datadir, hypothesis, spectrum_name, name))
r=source_dict(like, name)
#upper_limit_kwargs=dict(delta_log_like_limits=10)
upper_limit_kwargs=dict()
if upper_limit:
pul = GtlikePowerLawUpperLimit(like, name, emin=emin, emax=emax, cl=.95,
upper_limit_kwargs=upper_limit_kwargs,
verbosity=4,
xml_name=join("%s/srcmodel_gtlike_%s_%s_%s.xml" % (datadir, hypothesis, 'PowerLaw_Upper_Limit', name)))
r['powerlaw_upper_limit'] = pul.todict()
cul = GtlikeCutoffUpperLimit(like, name, Index=1.7, Cutoff=3e3, b=1, cl=.95,
upper_limit_kwargs=upper_limit_kwargs,
verbosity=4,
xml_name=join("%s/srcmodel_gtlike_%s_%s_%s.xml" % (datadir, hypothesis, 'PLSuperExpCutoff_Upper_Limit', name)))
r['cutoff_upper_limit'] = cul.todict()
if do_bandfitter:
if all_energy(emin,emax):
try:
bf = GtlikeBandFitter(like, name, bin_edges=one_bin_per_dec(emin,emax),
upper_limit_kwargs=upper_limit_kwargs,
verbosity=4)
bf.plot('%s/bandfits_gtlike_%s_%s.png' % (plotdir,hypothesis,name))
r['bandfits'] = bf.todict()
except Exception, ex:
print 'ERROR computing bandfit:', ex
traceback.print_exc(file=sys.stdout)
fit(fit_bg_first=True)
fit()
if localize:
paranoid_localize(roi, name, verbosity=4)
if fit_extension:
roi.fit_extension(which=name)
paranoid_localize(roi, name)
fit()
print 'Making pointlike SED for hypothesis %s' % hypothesis
sed = PointlikeSED(roi, name, verbosity=4)
sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' % (pipeline.dirdict['seds'],hypothesis,name))
sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' % (pipeline.dirdict['seds'],hypothesis,name))
print_summary()
p = source_dict(roi, name)
if upper_limit:
pul = PointlikePowerLawUpperLimit(roi, name, cl=.95, verbosity=4)
p['powerlaw_upper_limit']=pul.todict()
roi.toXML(filename="%s/srcmodel_pointlike_%s_%s.xml"%(pipeline.dirdict['data'], hypothesis, name))
roi.save('roi_%s_%s.dat' % (hypothesis,name))
return p
if localize:
paranoid_localize(roi, name, verbosity=4)
if fit_extension:
roi.fit_extension(which=name)
paranoid_localize(roi, name)
fit()
print 'Making pointlike SED for hypothesis %s' % hypothesis
sed = PointlikeSED(roi, name, verbosity=4)
sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' %
(pipeline.dirdict['seds'], hypothesis, name))
sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' %
(pipeline.dirdict['seds'], hypothesis, name))
print_summary()
p = source_dict(roi, name)
if upper_limit:
pul = PointlikePowerLawUpperLimit(roi, name, cl=.95, verbosity=4)
p['powerlaw_upper_limit'] = pul.todict()
roi.toXML(filename="%s/srcmodel_pointlike_%s_%s.xml" %
(pipeline.dirdict['data'], hypothesis, name))
roi.save('roi_%s_%s.dat' % (hypothesis, name))
return p
