def fit(just_prefactor=False, fit_bg_first=False):
""" Convenience function incase fit fails. """
try:
if just_prefactor:
fit_prefactor(roi, name)
else:
roi.fit(fit_bg_first=fit_bg_first)
except Exception, ex:
print 'ERROR spectral fitting pointlike for hypothesis %s:' % hypothesis, ex
traceback.print_exc(file=sys.stdout)
def fit(just_prefactor=False, fit_bg_first=False):
""" Convenience function incase fit fails. """
try:
if just_prefactor:
fit_prefactor(roi, name)
else:
roi.fit(fit_bg_first=fit_bg_first)
except Exception, ex:
print 'ERROR spectral fitting pointlike for hypothesis %s:' % hypothesis, ex
traceback.print_exc(file=sys.stdout)
def fit(self):
roi = self.roi
which = self.which
fit_prefactor(roi, which, **self.fit_kwargs)
roi.fit(**self.fit_kwargs)
if self.localize:
roi.localize(which=which, update=True)
roi.fit(**self.fit_kwargs)
ll = -roi.logLikelihood(roi.parameters())
return ll
def fit(self):
roi=self.roi
which=self.which
fit_prefactor(roi, which, **self.fit_kwargs)
roi.fit(**self.fit_kwargs)
if self.localize:
roi.localize(which=which, update=True)
roi.fit(**self.fit_kwargs)
ll=-roi.logLikelihood(roi.parameters())
return ll
def fit(just_prefactor=False, just_source=False, fit_bg_first=False):
""" Convenience function incase fit fails. """
try:
if just_prefactor:
fit_prefactor(roi, name)
elif just_source:
fit_only_source(roi, name)
elif fit_bg_first:
roi.fit(fit_bg_first=True)
else:
roi.fit()
# For some reason, one final fit seems to help with convergence and not getting negative TS values *shurgs*
roi.fit()
except Exception, ex:
print 'ERROR spectral fitting pointlike for hypothesis %s:' % hypothesis, ex
traceback.print_exc(file=sys.stdout)
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 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