def processAllObs(self, fix_shape=True, delete_below_ts=None,
ul_flux_dflux=0, ul_chi2_ts=None, ul_bayes_ts=4.0,
ul_cl=0.95, verbosity=0, emin=0, emax=0,
interim_save_filename=None):
self.logger.info("Processing all observations.")
for f in self.obsfiles:
lc = dict()
lc['config'] = dict()
lc['config']['fix_shape'] = fix_shape
lc['config']['delete_below_ts'] = delete_below_ts
lc['config']['ul_flux_dflux'] = ul_flux_dflux
lc['config']['ul_chi2_ts'] = ul_chi2_ts
lc['config']['ul_bayes_ts'] = ul_bayes_ts
lc['config']['ul_cl'] = ul_cl
lc['config']['emin'] = emin
lc['config']['emax'] = emax
lc['config']['files'] = f
#lc['config']['argv'] = sys.argv
lc['e_min'] = emin;
lc['e_max'] = emax;
if type(f) != list:
[ obs, like ] = self.loadObs(f,verbosity)
lc['t_min'] = obs.roiCuts().minTime()
lc['t_max'] = obs.roiCuts().maxTime()
if (emin == 0 or emax == 0):
lc['e_min'] = obs.roiCuts().getEnergyCuts()[0];
lc['e_max'] = obs.roiCuts().getEnergyCuts()[1];
else:
lc['t_min'] = None
lc['t_max'] = None
like = SL.SummedLikelihood(self.optimizer)
for ff in f:
[ obs, like1 ] = self.loadObs(ff,verbosity)
tmin = obs.roiCuts().minTime()
tmax = obs.roiCuts().maxTime()
if lc['t_min'] == None or tmin<lc['t_min']:
lc['t_min'] = tmin
if lc['t_max'] == None or tmax>lc['t_max']:
lc['t_max'] = tmax
if (lc['e_min'] == 0 or lc['e_max'] == 0):
ecuts = obs.roiCuts().getEnergyCuts()
lc['e_min'] = ecuts[0]
lc['e_max'] = ecuts[1]
elif (emin == 0 or emax == 0):
ecuts = obs.roiCuts().getEnergyCuts()
lc['e_min'] = max(lc['e_min'], ecuts[0])
lc['e_max'] = min(lc['e_max'], ecuts[1])
like.addComponent(like1)
emin = lc['e_min']
emax = lc['e_max']
like.tol = like.tol*0.01;
if verbosity > 1:
print '- Time:',lc['t_min'],'to',lc['t_max']
src = like[self.likelihoodConf['sourcename']]
if src == None:
raise NameError("No source \""+self.likelihoodConf['sourcename']+"\" in model "+
self.model)
srcfreepar=like.freePars(self.likelihoodConf['sourcename'])
srcnormpar=like.normPar(self.likelihoodConf['sourcename'])
if len(srcfreepar)>0:
like.setFreeFlag(self.likelihoodConf['sourcename'], srcfreepar, 0)
like.syncSrcParams(self.likelihoodConf['sourcename'])
meanvalue = srcnormpar.getValue()
meanerror = srcnormpar.error()
if meanerror == 0:
self.logger.critical("The error on the normalization for your source is 0! You need to do a global fit first (with quickLike) and provide the final XML file (<basename>_likeMinuit.xml) with errors included before you run compute.")
return
lc['original']=dict()
lc['original']['normpar_init_value'] = meanvalue
lc['original']['normpar_name'] = srcnormpar.getName()
lc['original']['nfree'] = len(like.freePars(self.likelihoodConf['sourcename']))
lc['original']['flux'] = like[self.likelihoodConf['sourcename']].flux(emin, emax)
lc['original']['logL'] = like.logLike.value()
if verbosity > 1:
print '- Original log Like:',lc['original']['logL']
if fix_shape:
if verbosity > 1:
print '- Fixing spectral shape parameters'
sync_name = ""
for p in like.params():
if sync_name != "" and sync_name != p.srcName:
like.syncSrcParams(sync_name)
sync_name = ""
if(p.isFree() and p.srcName!=self.likelihoodConf['sourcename'] and
p.getName()!=like.normPar(p.srcName).getName()):
if verbosity > 2:
print '-- '+p.srcName+'.'+p.getName()
p.setFree(False)
sync_name = p.srcName
if sync_name != "" and sync_name != p.srcName:
like.syncSrcParams(sync_name)
sync_name = ""
# ----------------------------- FIT 1 -----------------------------
if verbosity > 1:
print '- Fit 1 - All parameters of',self.likelihoodConf['sourcename'],'fixed'
like.fit(max(verbosity-3, 0))
lc['allfixed'] = dict()
lc['allfixed']['logL'] = like.logLike.value()
fitstat = like.optObject.getRetCode()
if verbosity > 1 and fitstat != 0:
print "- Fit 1 - Minimizer returned with code: ", fitstat
lc['allfixed']['fitstat'] = fitstat
if verbosity > 1:
print '- Fit 1 - log Like:',lc['allfixed']['logL']
if delete_below_ts:
frozensrc = []
if verbosity > 1:
print '- Deleting point sources with TS<'+str(delete_below_ts)
deletesrc = []
for s in like.sourceNames():
freepars = like.freePars(s)
if(s!=self.likelihoodConf['sourcename'] and like[s].src.getType() == 'Point'
and len(freepars)>0):
ts = like.Ts(s)
if ts<delete_below_ts:
deletesrc.append(s)
if verbosity > 2:
print '--',s,'(TS='+str(ts)+')'
if deletesrc:
for s in deletesrc:
like.deleteSource(s)
if verbosity > 1:
print '- Fit 1 - refitting model'
like.fit(max(verbosity-3, 0))
lc['allfixed']['fitstat_initial'] = \
lc['allfixed']['fitstat']
fitstat = like.optObject.getRetCode()
if verbosity > 1 and fitstat != 0:
print "- Fit 1 - Minimizer returned with code: ",\
fitstat
lc['allfixed']['fitstat'] = fitstat
lc['allfixed']['logL'] = like.logLike.value()
if verbosity > 1:
print '- Fit 1 - log Like:',lc['allfixed']['logL']
lc['allfixed']['flux']=like[self.likelihoodConf['sourcename']].flux(emin, emax)
pars = dict()
for pn in like[self.likelihoodConf['sourcename']].funcs['Spectrum'].paramNames:
p = like[self.likelihoodConf['sourcename']].funcs['Spectrum'].getParam(pn)
pars[p.getName()] = dict(name = p.getName(),
value = p.getTrueValue(),
error = p.error()*p.getScale(),
free = p.isFree())
lc['allfixed']['pars'] = pars
# ------------------ N SIGMA PROFILE LIKELIHOOD -------------------
prof_sigma = (-1,-0.5,0,0.5,1.0)
lc['profile'] = dict();
lc['profile']['sigma'] = []
lc['profile']['value'] = []
lc['profile']['logL'] = []
lc['profile']['flux'] = []
lc['profile']['fitstat'] = []
if verbosity > 1:
print '- Fit 1 - generating %d point likelihood profile'%\
len(prof_sigma)
for sigma in prof_sigma:
val = sigma*meanerror+meanvalue
if val < srcnormpar.getBounds()[0]:
val = srcnormpar.getBounds()[0]
if (lc['profile']['value']
and lc['profile']['value'][-1]==val):
continue
lc['profile']['value'].append(val)
lc['profile']['sigma'].append((val-meanvalue)/meanerror)
if(val == meanvalue):
lc['profile']['logL'].append(lc['allfixed']['logL'])
lc['profile']['flux'].append(lc['allfixed']['flux'])
else:
srcnormpar.setValue(val)
like.syncSrcParams(self.likelihoodConf['sourcename'])
like.fit(max(verbosity-3, 0))
fitstat = like.optObject.getRetCode()
if verbosity > 2 and fitstat != 0:
print "- Fit 1 - profile: Minimizer returned code: ",\
fitstat
lc['profile']['fitstat'].append(fitstat)
lc['profile']['logL'].append(like.logLike.value())
lc['profile']['flux'].append(like[self.likelihoodConf['sourcename']].\
flux(emin, emax))
if verbosity > 2:
print '- Fit 1 - profile: %+g, %f -> %f'%\
(sigma,lc['profile']['value'][-1],
lc['profile']['logL'][-1]-lc['allfixed']['logL'])
srcnormpar.setValue(meanvalue)
like.syncSrcParams(self.likelihoodConf['sourcename'])
# ----------------------------- FIT 2 -----------------------------
if verbosity > 1:
print '- Fit 2 - Normalization parameter of',\
self.likelihoodConf['sourcename'],'free'
srcnormpar.setFree(1)
like.syncSrcParams(self.likelihoodConf['sourcename'])
like.fit(max(verbosity-3, 0))
lc['normfree'] = dict()
fitstat = like.optObject.getRetCode()
if verbosity > 1 and fitstat != 0:
print "- Fit 2 - Minimizer returned with code: ", fitstat
lc['normfree']['fitstat'] = fitstat
lc['normfree']['logL'] = like.logLike.value()
lc['normfree']['ts'] = like.Ts(self.likelihoodConf['sourcename'])
lc['normfree']['flux_dflux'] = \
srcnormpar.getValue()/srcnormpar.error()
if verbosity > 1:
print '- Fit 2 - log Like:',lc['normfree']['logL'],\
'(TS='+str(lc['normfree']['ts'])+')'
lc['normfree']['nfree']=len(like.freePars(self.likelihoodConf['sourcename']))
lc['normfree']['flux']=like[self.likelihoodConf['sourcename']].flux(emin, emax)
pars = dict()
for pn in like[self.likelihoodConf['sourcename']].funcs['Spectrum'].paramNames:
p = like[self.likelihoodConf['sourcename']].funcs['Spectrum'].getParam(pn)
pars[p.getName()] = dict(name = p.getName(),
value = p.getTrueValue(),
error = p.error()*p.getScale(),
free = p.isFree())
lc['normfree']['pars'] = pars
ul_type = None
if ul_bayes_ts != None and lc['normfree']['ts'] < ul_bayes_ts:
ul_type = 'bayesian'
[ul_flux, ul_results] = \
IUL.calc_int(like,self.likelihoodConf['sourcename'],cl=ul_cl,
skip_global_opt=True,
verbosity = max(verbosity-2,0),
emin=emin, emax=emax,
poi_values = lc['profile']['value'])
elif ( ul_flux_dflux != None and \
lc['normfree']['flux_dflux'] < ul_flux_dflux ) or \
( ul_chi2_ts != None and lc['normfree']['ts'] < ul_chi2_ts):
ul_type = 'chi2'
[ul_flux, ul_results] = \
IUL.calc_chi2(like,self.likelihoodConf['sourcename'],cl=ul_cl,
skip_global_opt=True,
verbosity = max(verbosity-2,0),
emin=emin, emax=emax)
if ul_type != None:
lc['normfree']['ul'] = dict(flux = ul_flux,
results = ul_results,
type = ul_type)
# ----------------------------- FIT 3 -----------------------------
if verbosity > 1:
print '- Fit 3 - All parameters of',self.likelihoodConf['sourcename'],'free'
like.setFreeFlag(self.likelihoodConf['sourcename'], srcfreepar, 1)
like.syncSrcParams(self.likelihoodConf['sourcename'])
like.fit(max(verbosity-3, 0))
lc['allfree'] = dict()
fitstat = like.optObject.getRetCode()
if verbosity > 1 and fitstat != 0:
print "- Fit 3 - Minimizer returned with code: ", fitstat
lc['allfree']['fitstat'] = fitstat
lc['allfree']['logL'] = like.logLike.value()
lc['allfree']['ts'] = like.Ts(self.likelihoodConf['sourcename'])
if verbosity > 1:
print '- Fit 3 - log Like:',lc['allfree']['logL'],\
'(TS='+str(lc['allfree']['ts'])+')'
lc['allfree']['nfree']=len(like.freePars(self.likelihoodConf['sourcename']))
lc['allfree']['flux']=like[self.likelihoodConf['sourcename']].flux(emin, emax)
pars = dict()
for pn in like[self.likelihoodConf['sourcename']].funcs['Spectrum'].paramNames:
p = like[self.likelihoodConf['sourcename']].funcs['Spectrum'].getParam(pn)
pars[p.getName()] = dict(name = p.getName(),
value = p.getTrueValue(),
error = p.error()*p.getScale(),
free = p.isFree())
lc['allfree']['pars'] = pars
self.lc.append(lc)
if interim_save_filename != None:
self.saveProcessedObs(interim_save_filename)
def processAllObs(self, fix_shape=True, delete_below_ts=None,
ul_flux_dflux=0,ul_chi2_ts=None, ul_bayes_ts=4.0,
ul_cl = 0.95, verbosity=0, ul_optimizer=None):
for f in self.obsfiles:
spect = dict()
spect['config']=dict()
spect['config']['fix_shape'] = fix_shape
spect['config']['delete_below_ts'] = delete_below_ts
spect['config']['ul_flux_dflux'] = ul_flux_dflux
spect['config']['ul_chi2_ts'] = ul_chi2_ts
spect['config']['ul_bayes_ts'] = ul_bayes_ts
spect['config']['ul_cl'] = ul_cl
spect['config']['files'] = f
[ obs, like ] = self.loadObs(f,verbosity)
spect['t_min'] = obs.roiCuts().minTime()
spect['t_max'] = obs.roiCuts().maxTime()
[emin, emax] = obs.roiCuts().getEnergyCuts()
spect['e_min'] = emin
spect['e_max'] = emax
if verbosity > 1:
print '- Time:',spect['t_min'],'to',spect['t_max']
print '- Energy:',emin,'to',emax,'MeV'
src = like[self.srcName]
if src == None:
raise NameError("No source \""+self.srcName+"\" in model "+
self.model)
srcnormpar=like.normPar(self.srcName)
spect['original']=dict()
spect['original']['normpar_init_value'] = srcnormpar.getValue()
spect['original']['normpar_name'] = srcnormpar.getName()
spect['original']['flux'] = like[self.srcName].flux(emin, emax)
spect['original']['logL'] = like.logLike.value()
if verbosity > 1:
print '- Original log Like:',spect['original']['logL']
if fix_shape:
if verbosity > 1:
print '- Fixing spectral shape parameters'
sync_name = ""
for p in like.params():
if sync_name != "" and sync_name != p.srcName:
like.syncSrcParams(sync_name)
sync_name = ""
if(p.isFree() and #p.srcName!=self.srcName and
p.getName()!=like.normPar(p.srcName).getName()):
if verbosity > 2:
print '-- '+p.srcName+'.'+p.getName()
p.setFree(False)
sync_name = p.srcName
if sync_name != "" and sync_name != p.srcName:
like.syncSrcParams(sync_name)
sync_name = ""
# ------------------------------ FIT ------------------------------
if verbosity > 1:
print '- Fit - starting'
like.fit(max(verbosity-3, 0))
spect['fit'] = dict()
spect['fit']['logL'] = like.logLike.value()
if verbosity > 1:
print '- Fit - log Like:',spect['fit']['logL']
if delete_below_ts:
frozensrc = []
if verbosity > 1:
print '- Deleting point sources with TS<'+str(delete_below_ts)
deletesrc = []
for s in like.sourceNames():
freepars = like.freePars(s)
if(s!=self.srcName and like[s].type == 'PointSource'
and len(freepars)>0):
ts = like.Ts(s)
if ts<delete_below_ts:
deletesrc.append(s)
if verbosity > 2:
print '--',s,'(TS='+str(ts)+')'
if deletesrc:
for s in deletesrc:
like.deleteSource(s)
if verbosity > 1:
print '- Fit - refitting model'
like.fit(max(verbosity-3, 0))
spect['fit']['logL'] = like.logLike.value()
if verbosity > 1:
print '- Fit - log Like:',spect['fit']['logL']
spect['fit']['ts']=like.Ts(self.srcName)
if verbosity > 1:
print '- TS of %s: %f'%(self.srcName,spect['fit']['ts'])
spect['fit']['flux']=like[self.srcName].flux(emin, emax)
emid = math.sqrt(emin*emax)
spect['fit']['e_mid']=emid
# Note: be careful about the meaning here - it is the
# differential flux in the middle of the energy bin, not a
# flux error. This contradicts the meaning in 'flux_dflux'
spect['fit']['dflux'] = \
like[self.srcName].flux(emid*(1-0.001),emid*(1+0.001))/(emid*0.002)
spect['fit']['flux_dflux'] = \
srcnormpar.getValue()/srcnormpar.error()
pars = dict()
for pn in like[self.srcName].funcs['Spectrum'].paramNames:
p = like[self.srcName].funcs['Spectrum'].getParam(pn)
pars[p.getName()] = dict(name = p.getName(),
value = p.getTrueValue(),
error = p.error()*p.getScale(),
free = p.isFree())
spect['fit']['pars'] = pars
ul_type = None
if ul_bayes_ts != None and spect['fit']['ts'] < ul_bayes_ts:
ul_type = 'bayesian'
[ul_flux, ul_results] = \
IntegralUpperLimit.calc_int(like,self.srcName,cl=ul_cl,
skip_global_opt=True,
verbosity = max(verbosity-2,0),
emin=emin, emax=emax,
profile_optimizer=ul_optimizer)
elif ( ul_flux_dflux != None and \
spect['fit']['flux_dflux'] < ul_flux_dflux ) or \
( ul_chi2_ts != None and spect['fit']['ts'] < ul_chi2_ts):
ul_type = 'chi2'
[ul_flux, ul_results] = \
IntegralUpperLimit.calc_chi2(like,self.srcName,cl=ul_cl,
skip_global_opt=True,
verbosity = max(verbosity-2,0),
emin=emin, emax=emax,
profile_optimizer=ul_optimizer)
if ul_type != None:
spect['fit']['ul'] = dict(flux = ul_flux,
results = ul_results,
type = ul_type)
self.spectra.append(spect)
