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 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(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 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)
def _calculate(self,*args,**kwargs):
""" Convert all units into sympy arrays after the initial calculation. """
like = self.like
name = self.name
init_energes = like.energies[[0,-1]]
# Freeze all sources except one to make sed of.
all_sources = like.sourceNames()
if name not in all_sources:
raise Exception("Cannot find source %s in list of sources" % name)
# make copy of parameter values + free parameters
saved_state = SuperState(like)
if self.verbosity: print 'Freezing background sources'
for other_name in get_background(like):
if self.freeze_bg_diffuse:
if self.verbosity: print ' * Freezing diffuse source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity: print ' * Freezing spectral shape for diffuse source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
for other_name in get_sources(like):
if self.freeze_bg_sources:
if self.verbosity: print ' * Freezing bg source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity: print ' * Freezing spectral shape for bg source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
self.raw_results = []
for i,(lower,upper) in enumerate(zip(self.lower,self.upper)):
like.setEnergyRange(float(lower)+1, float(upper)-1)
e = np.sqrt(lower*upper)
if self.verbosity: print 'Calculating SED from %.0dMeV to %.0dMeV' % (lower,upper)
""" Note, the most robust method I have found for computing SEDs in gtlike is:
(a) Create a generic spectral model with a fixed spectral index.
(b) Set the 'Scale' to sqrt(emin*emax) so the prefactor is dNdE in the middle
of the sed bin.
(b) Set the limits to go from norm/fit_range to norm*fit_range and set the scale to 'norm'
"""
old_flux = self.init_model.i_flux(emin=lower,emax=upper)
model = PowerLaw(index=self.powerlaw_index, e0=e)
model.set_flux(old_flux, emin=lower, emax=upper)
norm = model['norm']
model.set_limits('norm',norm/float(self.fit_range),norm*self.fit_range, scale=norm)
model.set_limits('index',-5,5)
model.freeze('index')
spectrum = build_gtlike_spectrum(model)
like.setSpectrum(name,spectrum)
like.syncSrcParams(name)
if self.verbosity:
print 'Before fitting SED from %.0dMeV to %.0dMeV' % (lower,upper)
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'After fitting SED from %.0dMeV to %.0dMeV' % (lower,upper)
print summary(like)
d = dict()
self.raw_results.append(d)
d['energy'] = energy_dict(emin=lower, emax=upper, energy_units=self.energy_units)
d['flux'] = flux_dict(like, name, emin=lower,emax=upper, flux_units=self.flux_units,
errors=True, include_prefactor=True, prefactor_energy=e)
d['prefactor'] = powerlaw_prefactor_dict(like, name, errors=self.save_hesse_errors, minos_errors=True,
flux_units=self.flux_units)
d['TS'] = ts_dict(like, name, verbosity=self.verbosity)
if self.verbosity: print 'Calculating SED upper limit from %.0dMeV to %.0dMeV' % (lower,upper)
if self.always_upper_limit or d['TS']['reoptimize'] < self.min_ts:
ul = GtlikePowerLawUpperLimit(like, name,
cl=self.ul_confidence,
emin=lower,emax=upper,
flux_units=self.flux_units,
energy_units=self.energy_units,
upper_limit_kwargs=self.upper_limit_kwargs,
include_prefactor=True,
prefactor_energy=e,
verbosity=self.verbosity,
)
d['upper_limit'] = ul.todict()
# revert to old model
like.setEnergyRange(*init_energes)
saved_state.restore()
self._condense_results()
def _calculate(self, *args, **kwargs):
""" Convert all units into sympy arrays after the initial calculation. """
like = self.like
name = self.name
init_energes = like.energies[[0, -1]]
# Freeze all sources except one to make sed of.
all_sources = like.sourceNames()
if name not in all_sources:
raise Exception("Cannot find source %s in list of sources" % name)
# make copy of parameter values + free parameters
saved_state = SuperState(like)
if self.verbosity: print 'Freezing background sources'
for other_name in get_background(like):
if self.freeze_bg_diffuse:
if self.verbosity:
print ' * Freezing diffuse source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity:
print ' * Freezing spectral shape for diffuse source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
for other_name in get_sources(like):
if self.freeze_bg_sources:
if self.verbosity:
print ' * Freezing bg source %s' % other_name
modify(like, other_name, free=False)
else:
if self.verbosity:
print ' * Freezing spectral shape for bg source %s' % other_name
modify(like, other_name, freeze_spectral_shape=True)
self.raw_results = []
for i, (lower, upper) in enumerate(zip(self.lower, self.upper)):
like.setEnergyRange(float(lower) + 1, float(upper) - 1)
e = np.sqrt(lower * upper)
if self.verbosity:
print 'Calculating SED from %.0dMeV to %.0dMeV' % (lower,
upper)
""" Note, the most robust method I have found for computing SEDs in gtlike is:
(a) Create a generic spectral model with a fixed spectral index.
(b) Set the 'Scale' to sqrt(emin*emax) so the prefactor is dNdE in the middle
of the sed bin.
(b) Set the limits to go from norm/fit_range to norm*fit_range and set the scale to 'norm'
"""
old_flux = self.init_model.i_flux(emin=lower, emax=upper)
model = PowerLaw(index=self.powerlaw_index, e0=e)
model.set_flux(old_flux, emin=lower, emax=upper)
norm = model['norm']
model.set_limits('norm',
norm / float(self.fit_range),
norm * self.fit_range,
scale=norm)
model.set_limits('index', -5, 5)
model.freeze('index')
spectrum = build_gtlike_spectrum(model)
like.setSpectrum(name, spectrum)
like.syncSrcParams(name)
if self.verbosity:
print 'Before fitting SED from %.0dMeV to %.0dMeV' % (lower,
upper)
print summary(like)
paranoid_gtlike_fit(like, verbosity=self.verbosity)
if self.verbosity:
print 'After fitting SED from %.0dMeV to %.0dMeV' % (lower,
upper)
print summary(like)
d = dict()
self.raw_results.append(d)
d['energy'] = energy_dict(emin=lower,
emax=upper,
energy_units=self.energy_units)
d['flux'] = flux_dict(like,
name,
emin=lower,
emax=upper,
flux_units=self.flux_units,
errors=True,
include_prefactor=True,
prefactor_energy=e)
d['prefactor'] = powerlaw_prefactor_dict(
like,
name,
errors=self.save_hesse_errors,
minos_errors=True,
flux_units=self.flux_units)
d['TS'] = ts_dict(like, name, verbosity=self.verbosity)
if self.verbosity:
print 'Calculating SED upper limit from %.0dMeV to %.0dMeV' % (
lower, upper)
if self.always_upper_limit or d['TS']['reoptimize'] < self.min_ts:
ul = GtlikePowerLawUpperLimit(
like,
name,
cl=self.ul_confidence,
emin=lower,
emax=upper,
flux_units=self.flux_units,
energy_units=self.energy_units,
upper_limit_kwargs=self.upper_limit_kwargs,
include_prefactor=True,
prefactor_energy=e,
verbosity=self.verbosity,
)
d['upper_limit'] = ul.todict()
# revert to old model
like.setEnergyRange(*init_energes)
saved_state.restore()
self._condense_results()