def variability_followup(self, hypothesis):
name = self.name
roi = self.reload_roi(hypothesis)
modify = import_module(self.modify)
good_interval = modify.get_variability_time_cuts(name)
nbins = 36
if good_interval is not None:
ft1files = roi.sa.pixeldata.ft1files
earliest_time, latest_time = CombinedVariabilityTester.get_time_range(
ft1files)
bins = b = np.round(
np.linspace(earliest_time, latest_time, nbins + 1)).astype(int)
starts = b[:-1]
stops = b[1:]
print 'Initial binning:'
print ' * starts=', starts
print ' * stops=', stops
starts,stops = zip(*[(start,stop) \
for (start,stop) in zip(starts,stops) \
if good_interval(start,stop)])
print 'Initial binning:'
print ' * starts=', starts
print ' * stops=', stops
kwargs = dict(tstarts=starts, tstops=stops)
else:
kwargs = dict(nbins=nbins)
frozen = freeze_far_away(roi,
roi.get_source(name).skydir, self.max_free)
v = CombinedVariabilityTester(roi,
name,
use_pointlike_ltcube=True,
refit_background=True,
refit_other_sources=True,
verbosity=4,
**kwargs)
unfreeze_far_away(roi, frozen)
results = v.todict()
savedict(results,
'results_%s_variability_%s.yaml' % (name, hypothesis))
try:
v.plot(filename='plots/variability_%s_hypothesis_%s.pdf' %
(name, hypothesis))
except Exception, ex:
print 'ERROR plotting variability tester:', ex
traceback.print_exc(file=sys.stdout)
def variability_followup(self, hypothesis):
name = self.name
roi = self.reload_roi(hypothesis)
modify = import_module(self.modify)
good_interval = modify.get_variability_time_cuts(name)
nbins=36
if good_interval is not None:
ft1files=roi.sa.pixeldata.ft1files
earliest_time, latest_time = CombinedVariabilityTester.get_time_range(ft1files)
bins = b = np.round(np.linspace(earliest_time, latest_time, nbins+1)).astype(int)
starts = b[:-1]
stops = b[1:]
print 'Initial binning:'
print ' * starts=',starts
print ' * stops=',stops
starts,stops = zip(*[(start,stop) \
for (start,stop) in zip(starts,stops) \
if good_interval(start,stop)])
print 'Initial binning:'
print ' * starts=',starts
print ' * stops=',stops
kwargs=dict(tstarts=starts, tstops=stops)
else:
kwargs=dict(nbins=nbins)
frozen = freeze_far_away(roi, roi.get_source(name).skydir, self.max_free)
v = CombinedVariabilityTester(roi,name,
use_pointlike_ltcube=True, refit_background=True,
refit_other_sources=True,
verbosity=4, **kwargs)
unfreeze_far_away(roi, frozen)
results = v.todict()
savedict(results,'results_%s_variability_%s.yaml' % (name,hypothesis))
try:
v.plot(filename='plots/variability_%s_hypothesis_%s.pdf' % (name,hypothesis))
except Exception, ex:
print 'ERROR plotting variability tester:', ex
traceback.print_exc(file=sys.stdout)
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)
else:
print 'Override Localization for source %s:', name
print 'override_localization=',override_localization
roi.modify(which=None, skydir=override_localization['init_position'])
assert override_localization['method'] == 'MinuitLocalizer'
m=MinuitLocalizer(roi, name, verbosity=4)
print 'Localization Ellipse:',m.todict()
if fit_extension:
roi.fit_extension(which=name)
ellipse = paranoid_localize(roi, name)
print ellipse
unfreeze_far_away(roi, frozen)
fit()
print 'Making pointlike SED for hypothesis %s' % hypothesis
sed = PointlikeSED(roi, name, verbosity=4)
sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' % (seddir,hypothesis,name))
sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' % (seddir,hypothesis,name))
print_summary()
p = source_dict(roi, name)
pul = PointlikePowerLawUpperLimit(roi, name, emin=emin, emax=emax, cl=.95, verbosity=4)
p['powerlaw_upper_limit']=pul.todict()
cul = PointlikeCutoffUpperLimit(roi, name, Index=1.7, Cutoff=3e3, b=1, cl=.95, verbosity=4)