def likelihood_upper_limit3(z):
#Array to hold results of flux upper limit calculation
num_ebins = 51 #1 more than the number of bins due to the fencepost problem
energies = 10**np.linspace(np.log10(6000), np.log10(800000), num_ebins)
ebin_widths = np.diff(energies)
sourcemap = '6gev_srcmap_03_pedestal.fits' #'box_srcmap_artificial_box.fits'#'6gev_srcmap_complete.fits'
box_flux = np.zeros((num_ebins - 1))
box_flux_bayesian = np.zeros((num_ebins - 1))
box_flux_frequentist = np.zeros((num_ebins - 1))
corr = np.zeros((num_ebins - 1))
corr2 = np.zeros((num_ebins - 1))
gll_index = np.zeros((num_ebins - 1))
disk_index = np.zeros((num_ebins - 1))
#reconstructed_spectra = np.zeros((num_ebins-1, num_ebins-1))
#Loop through upper edge of box
for index in range(6, 48):
box_width = energies[index] * 2.0 * np.sqrt(1.0 -
z) / (1 + np.sqrt(1.0 - z))
print "Calculating upper limit in bin " + str(
index) + " at energy " + str(energies[index])
#print "bin " + str(np.argmin(np.abs(energies-energy)))
#window_low, window_high = window(energy, energies)
window_low = index - 6
window_high = index + 2
#Generate two observations (one above the window and one below)
#Make two exposure maps
if index > 6:
exposure_complete = pyfits.open('6gev_exposure.fits')
exposure_complete[0].data = exposure_complete[0].data[:window_low +
1]
a = exposure_complete[0]
exposure_complete[1].data = exposure_complete[1].data[:window_low +
1]
b = exposure_complete[1]
hdulist = pyfits.HDUList([a, b, exposure_complete[2]])
os.system('rm exposure_low.fits')
hdulist.writeto('exposure_low.fits')
exposure_complete.close()
if index < 48:
exposure_complete = pyfits.open('6gev_exposure.fits')
exposure_complete[0].data = exposure_complete[0].data[window_high:]
a = exposure_complete[0]
exposure_complete[1].data = exposure_complete[1].data[window_high:]
b = exposure_complete[1]
hdulist = pyfits.HDUList([a, b, exposure_complete[2]])
os.system('rm exposure_high.fits')
hdulist.writeto('exposure_high.fits')
exposure_complete.close()
#Make two sourcemaps
if index > 6:
srcmap_complete = pyfits.open(sourcemap)
srcmap_complete[0].data = srcmap_complete[0].data[:window_low]
a = srcmap_complete[0]
srcmap_complete[2].data = srcmap_complete[2].data[:window_low]
b = srcmap_complete[2]
srcmap_complete[3].data = srcmap_complete[3].data[:window_low + 1]
c = srcmap_complete[3]
srcmap_complete[4].data = srcmap_complete[4].data[:window_low + 1]
d = srcmap_complete[4]
srcmap_complete[5].data = srcmap_complete[5].data[:window_low + 1]
e = srcmap_complete[5]
srcmap_complete[6].data = srcmap_complete[6].data[:window_low + 1]
f = srcmap_complete[6]
srcmap_complete[7].data = srcmap_complete[7].data[:window_low + 1]
g = srcmap_complete[7]
srcmap_complete[8].data = srcmap_complete[8].data[:window_low + 1]
h = srcmap_complete[8]
srcmap_complete[9].data = srcmap_complete[9].data[:window_low + 1]
m = srcmap_complete[9]
os.system('rm srcmap_low.fits')
b.header['DSVAL4'] = str() + ':' + str()
hdulist = pyfits.HDUList(
[a, srcmap_complete[1], b, c, d, e, f, g, h, m])
hdulist.writeto('srcmap_low.fits')
srcmap_complete.close()
if index < 48:
srcmap_complete = pyfits.open(sourcemap)
srcmap_complete[0].data = srcmap_complete[0].data[window_high:]
a = srcmap_complete[0]
srcmap_complete[2].data = srcmap_complete[2].data[window_high:]
r = 0
for entry in srcmap_complete[2].data:
entry[0] = int(r)
r += 1
#srcmap_complete[2].data[:,0] = np.arange(0, len(srcmap_complete[2].data[:,0]))
b = srcmap_complete[2]
srcmap_complete[3].data = srcmap_complete[3].data[window_high:]
c = srcmap_complete[3]
srcmap_complete[4].data = srcmap_complete[4].data[window_high:]
d = srcmap_complete[4]
srcmap_complete[5].data = srcmap_complete[5].data[window_high:]
e = srcmap_complete[5]
srcmap_complete[6].data = srcmap_complete[6].data[window_high:]
f = srcmap_complete[6]
srcmap_complete[7].data = srcmap_complete[7].data[window_high:]
g = srcmap_complete[7]
srcmap_complete[8].data = srcmap_complete[8].data[window_high:]
h = srcmap_complete[8]
srcmap_complete[9].data = srcmap_complete[9].data[window_high:]
m = srcmap_complete[9]
os.system('rm srcmap_high.fits')
hdulist = pyfits.HDUList(
[a, srcmap_complete[1], b, c, d, e, f, g, h, m])
hdulist.writeto('srcmap_high.fits')
srcmap_complete.close()
summedLike = SummedLikelihood()
if index > 6:
obs_low = BinnedObs(srcMaps='srcmap_low.fits',
expCube='6gev_ltcube.fits',
binnedExpMap='exposure_low.fits',
irfs='CALDB')
like_low = BinnedAnalysis(obs_low,
'xmlmodel_free.xml',
optimizer='NEWMINUIT')
summedLike.addComponent(like_low)
if index < 48:
obs_high = BinnedObs(srcMaps='srcmap_high.fits',
expCube='6gev_ltcube.fits',
binnedExpMap='exposure_high.fits',
irfs='CALDB')
like_high = BinnedAnalysis(obs_high,
'xmlmodel_free.xml',
optimizer='NEWMINUIT')
summedLike.addComponent(like_high)
print "Fitting SummedLikelihood"
summedLike.ftol = 1e-8
summedLike.fit(verbosity=3)
summedLike.writeXml('xmlmodel_free.xml')
for k in range(len(summedLike.params())):
summedLike.freeze(k)
summedLike.writeXml('xmlmodel_fixed.xml')
print "Fitting all data"
calculation = 'poisson'
obs_complete = BinnedObs(srcMaps=sourcemap,
expCube='6gev_ltcube.fits',
binnedExpMap='6gev_exposure.fits',
irfs='CALDB')
edit_box_xml(100000.0, 1e-15, 0.0)
like = BinnedAnalysis(obs_complete,
'xmlmodel_fixed_box.xml',
optimizer='MINUIT')
like.tol = 1e-8
like_obj = pyLike.Minuit(like.logLike)
like.fit(verbosity=3, optObject=like_obj)
like.writeXml('xmlmodel_fixed_box.xml')
#Flucuate the window data
f = pyfits.open(sourcemap)
poisson_data = np.zeros(
(len(range(max(window_low, 0), min(window_high, 49))), 50, 50))
q = 0
for bin in range(max(window_low, 0), min(window_high, 49)):
for source in like.sourceNames():
for j in range(3, 10):
if source == f[j].header['EXTNAME']:
the_index = j
model_counts = np.zeros(
(1, len(f[the_index].data[bin].ravel())))[0]
num_photons = int(
np.round(np.random.poisson(
like._srcCnts(source)[bin]))) #
for photon in range(int(num_photons)):
phot_loc = int(
make_random(np.arange(0, len(model_counts), 1),
f[the_index].data[bin].ravel()))
model_counts[phot_loc] += 1
model_counts = model_counts.reshape(50, 50)
poisson_data[q] += model_counts
q += 1
f[0].data[max(window_low, 0):min(window_high, 49)] = poisson_data
os.system('rm box_srcmap_poisson.fits')
f.writeto('box_srcmap_poisson.fits')
f.close()
obs_poisson = BinnedObs(srcMaps='box_srcmap_poisson.fits',
expCube='6gev_ltcube.fits',
binnedExpMap='6gev_exposure.fits',
irfs='CALDB')
like = BinnedAnalysis(obs_poisson,
'xmlmodel_fixed_box.xml',
optimizer='NEWMINUIT')
like.tol = 1e-8
like_obj = pyLike.Minuit(like.logLike)
like.fit(verbosity=0, optObject=like_obj)
if calculation == 'complete':
obs_calculation = obs_complete
else:
obs_calculation = obs_poisson
print "Finding Upper Limit..."
null_likelihood = loglikelihood3(energies[index], 1.0e-15,
obs_calculation, z)
delta_loglike = 0.0
#increase box flux until likelihood > 2sigma over null likelihood
box_flux[index] = 3.e-15
crit_chi2 = 2.706
while delta_loglike < crit_chi2:
print "delta loglike = " + str(delta_loglike)
print "flux = " + str(box_flux[index]) + " likelihood = " + str(
loglikelihood3(energies[index], box_flux[index],
obs_calculation, z))
box_flux[index] *= 3.0
delta_loglike = 2.0 * (loglikelihood3(
energies[index], box_flux[index], obs_calculation, z) -
null_likelihood)
box_flux[index] *= 1.0 / 3.0
delta_loglike = 2.0 * (loglikelihood3(energies[index], box_flux[index],
obs_calculation, z) -
null_likelihood)
print "Delta loglike = " + str(delta_loglike)
while delta_loglike < crit_chi2:
print "delta loglike = " + str(delta_loglike)
print "flux = " + str(box_flux[index]) + " likelihood = " + str(
loglikelihood3(energies[index], box_flux[index],
obs_calculation, z))
box_flux[index] *= 1.5
delta_loglike = 2.0 * (loglikelihood3(
energies[index], box_flux[index], obs_calculation, z) -
null_likelihood)
box_flux[index] *= 1.0 / 1.5
delta_loglike = 2.0 * (loglikelihood3(energies[index], box_flux[index],
obs_calculation, z) -
null_likelihood)
while delta_loglike < crit_chi2:
print "delta loglike = " + str(delta_loglike)
print "flux = " + str(box_flux[index]) + " likelihood = " + str(
loglikelihood3(energies[index], box_flux[index],
obs_calculation, z))
box_flux[index] *= 1.03
delta_loglike = 2.0 * (loglikelihood3(
energies[index], box_flux[index], obs_calculation, z) -
null_likelihood)
box_flux[index] *= 1.0 / 1.03
delta_loglike = 2.0 * (loglikelihood3(energies[index], box_flux[index],
obs_calculation, z) -
null_likelihood)
print "delta log like = " + str(delta_loglike)
calc_cov = False
if calc_cov:
like1 = BinnedAnalysis(obs_calculation,
'xmlmodel_fixed_box.xml',
optimizer='DRMNFB')
like1.thaw(like1.par_index('Disk Component', 'Index'))
like1.thaw(like1.par_index('Disk Component', 'Prefactor'))
like1.thaw(like1.par_index('Box Component', 'Normalization'))
like1.tol = 1e-5
like1obj = pyLike.Minuit(like1.logLike)
like1.fit(verbosity=0, optObject=like1obj, covar=False)
like1.writeXml('xmlmodel_fixed_box.xml')
like2 = BinnedAnalysis(obs_calculation,
'xmlmodel_fixed_box.xml',
optimizer='NewMinuit')
like2.tol = 1e-8
like2obj = pyLike.Minuit(like1.logLike)
like2.fit(verbosity=3, optObject=like1obj, covar=True)
#ul = UpperLimit(like1,'Box Component')
#ul.compute(emin=100.0,emax=500000, delta=3.91)
#box_flux_bayesian[index] = float(ul.bayesianUL()[0])
#box_flux_frequentist[index] = float(ul.results[0].value)
print like2.covariance
print 'Return code: ' + str(like2obj.getRetCode())
cov = like2.covariance
corr[index] = cov[0][1] / np.sqrt(cov[0][0] * cov[1][1])
corr2[index] = cov[0][2] / np.sqrt(cov[0][0] * cov[2][2])
print "Correlations:"
print corr[index]
print corr2[index]
#if like2obj.getRetCode()!=0:
plot_spectrum(like2, energies, index, window_low, window_high)
if calc_cov:
file = open('correlation_results.pk1', 'wb')
pickle.dump([corr, corr2, box_flux_bayesian, box_flux_frequentist],
file)
file.close()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(energies[np.nonzero(corr)],
corr[np.nonzero(corr)],
color='blue',
label='Box vs GC Prefactor')
ax.plot(energies[np.nonzero(corr2)],
corr2[np.nonzero(corr2)],
color='red',
label='Box vs GC index')
plt.ylim([-1.0, 1.0])
plt.legend()
plt.xscale('log')
plt.show()
return box_flux
class AnalysisManager(Configurable):
default_config = { 'convtype' : -1,
'binsperdec' : 4,
'savedir' : None,
'scratchdir' : None,
'target' : None,
'evfile' : None,
'scfile' : None,
'ltcube' : None,
'galdiff' : None,
'isodiff' : None,
'event_types': None,
'gtbin' : None,
'catalog' : '2FGL',
'optimizer' : 'MINUIT',
'joint' : None,
'irfs' : None }
def __init__(self,config=None,**kwargs):
super(AnalysisManager,self).__init__()
self.update_default_config(SelectorTask,group='select')
self.configure(config,**kwargs)
import pprint
pprint.pprint(self.config)
self._like = SummedLikelihood()
@property
def like(self):
return self._like
@property
def logLike(self):
return self._like.logLike
def setup_roi(self,**kwargs):
target_name = self.config['target']
cat = Catalog.get('2fgl')
self.src = CatalogSource(cat.get_source_by_name(target_name))
if self.config['savedir'] is None:
self.set_config('savedir',target_name)
if not os.path.exists(self.config['savedir']):
os.makedirs(self.config['savedir'])
config = self.config
self.savestate = os.path.join(config['savedir'],
"%s_savestate.P"%target_name)
self.ft1file = os.path.join(config['savedir'],
"%s_ft1.fits"%target_name)
self.binfile = os.path.join(config['savedir'],
"%s_binfile.fits"%target_name)
self.srcmdl = os.path.join(config['savedir'],
"%s_srcmdl.xml"%target_name)
self.srcmdl_fit = os.path.join(config['savedir'],
"%s_srcmdl_fit.xml"%target_name)
if os.path.isfile(config['ltcube']) and \
re.search('\.fits?',config['ltcube']):
self.ltcube = config['ltcube']
else:
ltcube = sorted(glob.glob(config['ltcube']))
self.ltcube = os.path.join(config['savedir'],
"%s_ltcube.fits"%target_name)
lt_task = LTSumTask(self.ltcube,infile1=ltcube,
config=config)
lt_task.run()
self.evfile = config['evfile']#sorted(glob.glob(config['evfile']))
# if len(self.evfile) > 1:
# evfile_list = os.path.join(self.config('savedir'),'evfile.txt')
# np.savetxt(evfile_list,self.evfile,fmt='%s')
# self.evfile = os.path.abspath(evfile_list)
# else:
# self.evfile = self.evfile[0]
# if len(self.ltfile) > 1:
# ltfile_list = os.path.join(self.config('savedir'),'ltfile.txt')
# np.savetxt(ltfile_list,self.ltfile,fmt='%s')
# self.ltfile = os.path.abspath(ltfile_list)
# else:
# self.ltfile = self.ltfile[0]
# print self.evfile
# print self.ltfile
self.skydir = SkyDir(self.src.ra,self.src.dec)
sel_task = SelectorTask(self.evfile,self.ft1file,
ra=self.src.ra,dec=self.src.dec,
config=config['select'],overwrite=False)
sel_task.run()
cat.create_roi(self.src.ra,self.src.dec,
config['isodiff'],
config['galdiff'],
self.srcmdl,radius=5.0)
# self.setup_pointlike()
self.components = []
for i, t in enumerate(self.config['joint']):
print 'Setting up binned analysis ', i
# kw = dict(irfs=None,isodiff=None)
# kw.update(t)
analysis = BinnedGtlike(self.src,
target_name + '_%02i'%(i),
config,
evfile=self.ft1file,
srcmdl=self.srcmdl,
gtselect=dict(evclass=t['evclass'],
evtype=t['evtype']),
# convtype=t['convtype'],
irfs=t['irfs'],
isodiff=t['isodiff'])
analysis.setup_inputs()
analysis.setup_gtlike()
self.components.append(analysis)
self._like.addComponent(analysis.like)
# for i, p in self.tied_pars.iteritems():
# print 'Tying parameters ', i, p
# self.comp_like.tieParameters(p)
self._like.energies = self.components[0].like.energies
return
for i, p in enumerate(self.components[0].like.params()):
print i, p.srcName, p.getName()
tied_params = []
for c in self.components:
tied_params.append([c.like,p.srcName,p.getName()])
self.comp_like.tieParameters(tied_params)
# self.tied_pars = {}
# for x in self.components:
# for s in x.like.sourceNames():
# p = x.like.normPar(s)
# pidx = x.like.par_index(s,p.getName())
# if not pidx in self.tied_pars:
# self.tied_pars[pidx] = []
# self.tied_pars[pidx].append([x.like,s,p.getName()])
# print s, p.getName()
# self.norm_pars.append([x.like,s,p.getName()])
# self.norm_pars.append([self.analysis1.like,src,p.getName()])
def fit(self):
saved_state = LikelihoodState(self.like)
print 'Fitting model'
self.like.fit(verbosity=2, covar=True)
source_dict = gtlike_source_dict(self.like,self.src.name)
import pprint
pprint.pprint(source_dict)
def write_xml_model(self):
for c in self.components:
c.write_model()
# c.make_srcmodel()
def make_source_model(self):
for c in self.components:
c.make_srcmodel()
# def gtlike_results(self, **kwargs):
# from lande.fermi.likelihood.save import source_dict
# return source_dict(self.like, self.name, **kwargs)
# def gtlike_summary(self):
# from lande.fermi.likelihood.printing import gtlike_summary
# return gtlike_summary(self.like,maxdist=self.config['radius'])
def free_source(self,name,free=True):
""" Free a source in the ROI
source : string or pointlike source object
free : boolean to free or fix parameter
"""
freePars = self.like.freePars(name)
normPar = self.like.normPar(name).getName()
idx = self.like.par_index(name, normPar)
if not free:
self.like.setFreeFlag(name, freePars, False)
else:
self.like[idx].setFree(True)
self.like.syncSrcParams(name)
def save(self):
from util import save_object
save_object(self,self.savestate)
def setup_pointlike(self):
if os.path.isfile(self.srcmdl): return
config = self.config
self._ds = DataSpecification(ft1files = self.ft1file,
ft2files = config['scfile'],
ltcube = self.ltcube,
binfile = self.binfile)
self._sa = SpectralAnalysis(self._ds,
binsperdec = config['binsperdec'],
emin = config['emin'],
emax = config['emax'],
irf = config['irfs'],
roi_dir = self.skydir,
maxROI = config['radius'],
minROI = config['radius'],
zenithcut = config['zmax'],
event_class= 0,
conv_type = config['convtype'])
sources = []
point_sources, diffuse_sources = [],[]
galdiff = config['galdiff']
isodiff = config['isodiff']
bkg_sources = self.get_background(galdiff,isodiff)
sources += filter(None, bkg_sources)
catalog = self.get_catalog(config['catalog'])
catalogs = filter(None, [catalog])
for source in sources:
if isinstance(source,PointSource): point_sources.append(source)
else: diffuse_sources.append(source)
self._roi=self._sa.roi(roi_dir=self.skydir,
point_sources=point_sources,
diffuse_sources=diffuse_sources,
catalogs=catalogs,
fit_emin=config['emin'],
fit_emax=config['emax'])
# Create model file
self._roi.toXML(self.srcmdl,
convert_extended=True,
expand_env_vars=True)
@staticmethod
def get_catalog(catalog=None, **kwargs):
if catalog is None or isinstance(catalog,SourceCatalog):
pass
elif catalog == 'PSC3Y':
catalog = Catalog3Y('/u/ki/kadrlica/fermi/catalogs/PSC3Y/gll_psc3yearclean_v1_assoc_v6r1p0.fit',
latextdir='/u/ki/kadrlica/fermi/catalogs/PSC3Y/',
prune_radius=0,
**kwargs)
elif catalog == '2FGL':
catalog = Catalog2FGL('/u/ki/kadrlica/fermi/catalogs/2FGL/gll_psc_v08.fit',
latextdir='/u/ki/kadrlica/fermi/catalogs/2FGL/Templates/',
prune_radius=0,
**kwargs)
elif catalog == "1FGL":
catalog = FermiCatalog('/u/ki/kadrlica/fermi/catalogs/gll_psc_v02.fit',
prune_radius=0,
**kwargs)
else:
raise Exception("Unknown catalog: %s"%catalog)
return catalog
@staticmethod
def get_background(galdiff=None, isodiff=None, limbdiff=None):
""" Diffuse backgrounds
galdiff: Galactic diffuse counts cube fits file
isodiff: Isotropic diffuse spectral text file
limbdiff: Limb diffuse counts map fits file
"""
backgrounds = []
if galdiff is None: gal=None
else:
gfile = os.path.basename(galdiff)
gal = get_diffuse_source('MapCubeFunction',galdiff,
'PowerLaw',None,
os.path.splitext(gfile)[0])
gal.smodel.set_default_limits()
gal.smodel.freeze('index')
backgrounds.append(gal)
if isodiff is None: iso=None
else:
ifile = os.path.basename(isodiff)
iso = get_diffuse_source('ConstantValue',None,'FileFunction'
,isodiff,
os.path.splitext(ifile)[0])
iso.smodel.set_default_limits()
backgrounds.append(iso)
if limbdiff is None: limb=None
else:
lfile = basename(limbdiff)
dmodel = SpatialMap(limbdiff)
smodel = PLSuperExpCutoff(norm=3.16e-6,index=0,
cutoff=20.34,b=1,e0=200)
limb = ExtendedSource(name=name,model=smodel,spatial_model=dmodel)
for i in range(limb.smodel.npar): limb.smodel.freeze(i)
backgrounds.append(limb)
backgrounds.append(limb)
return backgrounds
class AnalysisManager(Configurable):
default_config = {
'convtype': -1,
'binsperdec': 4,
'savedir': None,
'scratchdir': None,
'target': None,
'evfile': None,
'scfile': None,
'ltcube': None,
'galdiff': None,
'isodiff': None,
'event_types': None,
'gtbin': None,
'catalog': '2FGL',
'optimizer': 'MINUIT',
'joint': None,
'irfs': None
}
def __init__(self, config=None, **kwargs):
super(AnalysisManager, self).__init__()
self.update_default_config(SelectorTask, group='select')
self.configure(config, **kwargs)
import pprint
pprint.pprint(self.config)
self._like = SummedLikelihood()
@property
def like(self):
return self._like
@property
def logLike(self):
return self._like.logLike
def setup_roi(self, **kwargs):
target_name = self.config['target']
cat = Catalog.get('2fgl')
self.src = CatalogSource(cat.get_source_by_name(target_name))
if self.config['savedir'] is None:
self.set_config('savedir', target_name)
if not os.path.exists(self.config['savedir']):
os.makedirs(self.config['savedir'])
config = self.config
self.savestate = os.path.join(config['savedir'],
"%s_savestate.P" % target_name)
self.ft1file = os.path.join(config['savedir'],
"%s_ft1.fits" % target_name)
self.binfile = os.path.join(config['savedir'],
"%s_binfile.fits" % target_name)
self.srcmdl = os.path.join(config['savedir'],
"%s_srcmdl.xml" % target_name)
self.srcmdl_fit = os.path.join(config['savedir'],
"%s_srcmdl_fit.xml" % target_name)
if os.path.isfile(config['ltcube']) and \
re.search('\.fits?',config['ltcube']):
self.ltcube = config['ltcube']
else:
ltcube = sorted(glob.glob(config['ltcube']))
self.ltcube = os.path.join(config['savedir'],
"%s_ltcube.fits" % target_name)
lt_task = LTSumTask(self.ltcube, infile1=ltcube, config=config)
lt_task.run()
self.evfile = config['evfile'] #sorted(glob.glob(config['evfile']))
# if len(self.evfile) > 1:
# evfile_list = os.path.join(self.config('savedir'),'evfile.txt')
# np.savetxt(evfile_list,self.evfile,fmt='%s')
# self.evfile = os.path.abspath(evfile_list)
# else:
# self.evfile = self.evfile[0]
# if len(self.ltfile) > 1:
# ltfile_list = os.path.join(self.config('savedir'),'ltfile.txt')
# np.savetxt(ltfile_list,self.ltfile,fmt='%s')
# self.ltfile = os.path.abspath(ltfile_list)
# else:
# self.ltfile = self.ltfile[0]
# print self.evfile
# print self.ltfile
self.skydir = SkyDir(self.src.ra, self.src.dec)
sel_task = SelectorTask(self.evfile,
self.ft1file,
ra=self.src.ra,
dec=self.src.dec,
config=config['select'],
overwrite=False)
sel_task.run()
cat.create_roi(self.src.ra,
self.src.dec,
config['isodiff'],
config['galdiff'],
self.srcmdl,
radius=5.0)
# self.setup_pointlike()
self.components = []
for i, t in enumerate(self.config['joint']):
print 'Setting up binned analysis ', i
# kw = dict(irfs=None,isodiff=None)
# kw.update(t)
analysis = BinnedGtlike(
self.src,
target_name + '_%02i' % (i),
config,
evfile=self.ft1file,
srcmdl=self.srcmdl,
gtselect=dict(evclass=t['evclass'], evtype=t['evtype']),
# convtype=t['convtype'],
irfs=t['irfs'],
isodiff=t['isodiff'])
analysis.setup_inputs()
analysis.setup_gtlike()
self.components.append(analysis)
self._like.addComponent(analysis.like)
# for i, p in self.tied_pars.iteritems():
# print 'Tying parameters ', i, p
# self.comp_like.tieParameters(p)
self._like.energies = self.components[0].like.energies
return
for i, p in enumerate(self.components[0].like.params()):
print i, p.srcName, p.getName()
tied_params = []
for c in self.components:
tied_params.append([c.like, p.srcName, p.getName()])
self.comp_like.tieParameters(tied_params)
# self.tied_pars = {}
# for x in self.components:
# for s in x.like.sourceNames():
# p = x.like.normPar(s)
# pidx = x.like.par_index(s,p.getName())
# if not pidx in self.tied_pars:
# self.tied_pars[pidx] = []
# self.tied_pars[pidx].append([x.like,s,p.getName()])
# print s, p.getName()
# self.norm_pars.append([x.like,s,p.getName()])
# self.norm_pars.append([self.analysis1.like,src,p.getName()])
def fit(self):
saved_state = LikelihoodState(self.like)
print 'Fitting model'
self.like.fit(verbosity=2, covar=True)
source_dict = gtlike_source_dict(self.like, self.src.name)
import pprint
pprint.pprint(source_dict)
def write_xml_model(self):
for c in self.components:
c.write_model()
# c.make_srcmodel()
def make_source_model(self):
for c in self.components:
c.make_srcmodel()
# def gtlike_results(self, **kwargs):
# from lande.fermi.likelihood.save import source_dict
# return source_dict(self.like, self.name, **kwargs)
# def gtlike_summary(self):
# from lande.fermi.likelihood.printing import gtlike_summary
# return gtlike_summary(self.like,maxdist=self.config['radius'])
def free_source(self, name, free=True):
""" Free a source in the ROI
source : string or pointlike source object
free : boolean to free or fix parameter
"""
freePars = self.like.freePars(name)
normPar = self.like.normPar(name).getName()
idx = self.like.par_index(name, normPar)
if not free:
self.like.setFreeFlag(name, freePars, False)
else:
self.like[idx].setFree(True)
self.like.syncSrcParams(name)
def save(self):
from util import save_object
save_object(self, self.savestate)
def setup_pointlike(self):
if os.path.isfile(self.srcmdl): return
config = self.config
self._ds = DataSpecification(ft1files=self.ft1file,
ft2files=config['scfile'],
ltcube=self.ltcube,
binfile=self.binfile)
self._sa = SpectralAnalysis(self._ds,
binsperdec=config['binsperdec'],
emin=config['emin'],
emax=config['emax'],
irf=config['irfs'],
roi_dir=self.skydir,
maxROI=config['radius'],
minROI=config['radius'],
zenithcut=config['zmax'],
event_class=0,
conv_type=config['convtype'])
sources = []
point_sources, diffuse_sources = [], []
galdiff = config['galdiff']
isodiff = config['isodiff']
bkg_sources = self.get_background(galdiff, isodiff)
sources += filter(None, bkg_sources)
catalog = self.get_catalog(config['catalog'])
catalogs = filter(None, [catalog])
for source in sources:
if isinstance(source, PointSource): point_sources.append(source)
else: diffuse_sources.append(source)
self._roi = self._sa.roi(roi_dir=self.skydir,
point_sources=point_sources,
diffuse_sources=diffuse_sources,
catalogs=catalogs,
fit_emin=config['emin'],
fit_emax=config['emax'])
# Create model file
self._roi.toXML(self.srcmdl,
convert_extended=True,
expand_env_vars=True)
@staticmethod
def get_catalog(catalog=None, **kwargs):
if catalog is None or isinstance(catalog, SourceCatalog):
pass
elif catalog == 'PSC3Y':
catalog = Catalog3Y(
'/u/ki/kadrlica/fermi/catalogs/PSC3Y/gll_psc3yearclean_v1_assoc_v6r1p0.fit',
latextdir='/u/ki/kadrlica/fermi/catalogs/PSC3Y/',
prune_radius=0,
**kwargs)
elif catalog == '2FGL':
catalog = Catalog2FGL(
'/u/ki/kadrlica/fermi/catalogs/2FGL/gll_psc_v08.fit',
latextdir='/u/ki/kadrlica/fermi/catalogs/2FGL/Templates/',
prune_radius=0,
**kwargs)
elif catalog == "1FGL":
catalog = FermiCatalog(
'/u/ki/kadrlica/fermi/catalogs/gll_psc_v02.fit',
prune_radius=0,
**kwargs)
else:
raise Exception("Unknown catalog: %s" % catalog)
return catalog
@staticmethod
def get_background(galdiff=None, isodiff=None, limbdiff=None):
""" Diffuse backgrounds
galdiff: Galactic diffuse counts cube fits file
isodiff: Isotropic diffuse spectral text file
limbdiff: Limb diffuse counts map fits file
"""
backgrounds = []
if galdiff is None: gal = None
else:
gfile = os.path.basename(galdiff)
gal = get_diffuse_source('MapCubeFunction', galdiff, 'PowerLaw',
None,
os.path.splitext(gfile)[0])
gal.smodel.set_default_limits()
gal.smodel.freeze('index')
backgrounds.append(gal)
if isodiff is None: iso = None
else:
ifile = os.path.basename(isodiff)
iso = get_diffuse_source('ConstantValue', None, 'FileFunction',
isodiff,
os.path.splitext(ifile)[0])
iso.smodel.set_default_limits()
backgrounds.append(iso)
if limbdiff is None: limb = None
else:
lfile = basename(limbdiff)
dmodel = SpatialMap(limbdiff)
smodel = PLSuperExpCutoff(norm=3.16e-6,
index=0,
cutoff=20.34,
b=1,
e0=200)
limb = ExtendedSource(name=name,
model=smodel,
spatial_model=dmodel)
for i in range(limb.smodel.npar):
limb.smodel.freeze(i)
backgrounds.append(limb)
backgrounds.append(limb)
return backgrounds
