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)
def build_roi(name, snrdata, latdata):
snrdata=loaddict(snrdata)
latdata=loaddict(latdata)
roi_dir = SkyDir(*snrdata[name]['cel'])
snrsize = snrdata[name]['size']
if isinstance(snrsize,list) and len(snrsize) == 2:
snrradius = math.sqrt(snrsize[0]*snrsize[1])/2.0
else:
snrradius = snrsize/2.0
ds = DataSpecification(**latdata['data'])
sa = SpectralAnalysis(ds,
binsperdec = 4,
emin = 1e4,
emax = 10**5.5,
irf = "P7SOURCE_V6",
roi_dir = roi_dir,
maxROI = 10,
minROI = 10,
event_class= 0)
diffuse_sources = get_default_diffuse(**latdata['diffuse'])
catalog = Catalog2FGL(**latdata['catalog'])
roi=sa.roi(point_sources=[],
diffuse_sources=diffuse_sources,
catalogs=catalog)
print 'bins',roi.bin_edges
for source in roi.get_sources():
if np.degrees(source.skydir.difference(roi_dir)) < snrradius + 0.5:
roi.del_source(source)
snr = ExtendedSource(
name = name,
model = PowerLaw(),
spatial_model = Disk(sigma=snrradius, center=roi_dir)
)
roi.add_source(snr)
return roi
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)
def setup_roi(name, snrdata, catalog_dict=dict(), roi_dict=dict()):
snr=yaml.load(open(snrdata))[name]
skydir=SkyDir(*snr['cel'])
size=snr['size']
ds=DataSpecification(
ft1files='$FERMILANDE/data/PWNCAT2/nov_30_2011/ft1_PWNCAT2_allsky.fits',
ft2files='$FERMILANDE/data/PWNCAT2/nov_30_2011/ft2_PWNCAT2_allsky.fits',
ltcube='$FERMILANDE/data/PWNCAT2/nov_30_2011/ltcube_PWNCAT2_allsky.fits',
binfile='$FERMILANDE/data/PWNCAT2/nov_30_2011/binned_%s.fits' % 4)
sa=SpectralAnalysis(ds,
binsperdec = 4,
roi_dir = skydir,
irf = 'P7SOURCE_V6',
maxROI = 10,
minROI = 10,
event_class = 0)
diffuse_sources = diffuse_sources = get_default_diffuse(
diffdir=e('$FERMILANDE/diffuse'),
gfile='ring_2year_P76_v0.fits',
ifile='isotrop_2year_P76_source_v0.txt')
catalogs = Catalog2FGL('$FERMILANDE/catalogs/gll_psc_v05.fit',
latextdir='$FERMILANDE/extended_archives/gll_psc_v05_templates/',
**catalog_dict)
roi = sa.roi(
catalogs = catalogs,
diffuse_sources = diffuse_sources,
**roi_dict)
return roi
def time_cut(roi, tstart, tstop, subdir, use_pointlike_ltcube, verbosity):
""" Create a new ROI given a time cut. """
sa = roi.sa
ds = sa.dataspec
get_defaults=lambda obj: [k[0] for k in obj.defaults if not isinstance(k,str)]
get_kwargs=lambda obj: {k:obj.__dict__[k] for k in get_defaults(obj)}
ds_kwargs, sa_kwargs, roi_kwargs = map(get_kwargs,[ds,sa,roi])
point_sources = [i.copy() for i in roi.psm.point_sources]
diffuse_sources = [i.copy() for i in roi.dsm.diffuse_sources]
if sa_kwargs['tstart'] !=0 or sa_kwargs['tstop'] !=0:
raise Exception("sanity check")
# * cut ft1file on time using gtselect
ft2files=roi.sa.pixeldata.ft2files
if len(ft2files) > 1: raise Exception("...")
ft2file=ft2files[0]
evfile=Gtlike.make_evfile(roi,subdir)
cut_evfile=join(subdir,"cut_ft1_%s_%s.fits" % (tstart, tstop))
if not exists(cut_evfile):
if verbosity: print 'Running gtselect'
gtselect=GtApp('gtselect', 'dataSubselector')
gtselect.run(infile=evfile,
outfile=cut_evfile,
ra=0, dec=0, rad=180,
tmin=tstart, tmax=tstop,
emin=1, emax=1e7,
zmax=180)
else:
if verbosity: print '... Skiping gtselect for %s to %s' % (tstart,tstop)
ds_kwargs['ft1files'] = cut_evfile
# * create new binfile and ltcube
ds_kwargs['binfile'] = join(subdir,'binned_%s_%s.fits' % (tstart, tstop))
# save this to see if it has been phased by
# the function uw.utilities.phasetools.phase_ltcube
all_time_ltcube = ds_kwargs['ltcube']
new_ltcube = join(subdir,'ltcube_%s_%s.fits' % (tstart, tstop))
if not exists(new_ltcube):
if verbosity: print 'Running gtltcube for %s to %s' % (tstart,tstop)
if use_pointlike_ltcube:
pointlike_ltcube(evfile=cut_evfile,
scfile=ft2file,
outfile=new_ltcube,
dcostheta=0.025,
binsz=1,
zmax=roi.sa.zenithcut,
cone_angle=roi.sa.exp_radius,
dir=roi.roi_dir)
else:
gtltcube=GtApp('gtltcube', 'Likelihood')
gtltcube.run(evfile=cut_evfile,
scfile=ft2file,
outfile=new_ltcube,
dcostheta=0.025,
binsz=1)
else:
if verbosity: print '... Skiping gtltcube for %s to %s' % (tstart,tstop)
# next, check if ltcube is phased, kind of a kluge
f = pyfits.open(all_time_ltcube)
if f['exposure'].header.has_key('PHASE'):
assert f['exposure'].header['PHASE'] == f['weighted_exposure'].header['PHASE']
# If so, phase new ltcube
phase = f['exposure'].header['PHASE']
phased_ltcube = join(subdir,'phased_ltcube_%s_%s.fits' % (tstart, tstop))
if not exists(phased_ltcube):
phase_ltcube(new_ltcube, phased_ltcube, phase)
else:
if verbosity: print '... Skiping gtltcube phasing for %s to %s' % (tstart,tstop)
ds_kwargs['ltcube'] = phased_ltcube
else:
ds_kwargs['ltcube'] = new_ltcube
# * create new ds, sa, and roi
new_ds = DataSpecification(**ds_kwargs)
sa = SpectralAnalysis(new_ds, **sa_kwargs)
return sa.roi(
point_sources = point_sources,
diffuse_sources = diffuse_sources,
**roi_kwargs)
def build_roi(self, name, fast):
if fast:
roi_size=5
binsperdec = 2
max_free=2
free_radius=2
else:
roi_size = 10
binsperdec = 4
max_free=5
free_radius=5
catalog = Catalog2FGL('$FERMI/catalogs/gll_psc_v05.fit',
latextdir='$FERMI/extended_archives/gll_psc_v05_templates',
prune_radius=0,
max_free=max_free,
free_radius=free_radius,
limit_parameters=True)
ft1 = self.radiopsr_loader.get_ft1(name)
ft2 = self.radiopsr_loader.get_ft2(name)
ltcube = self.radiopsr_loader.get_ltcube(name)
binfile = self.radiopsr_loader.get_binfile(name, binsperdec)
roi_dir = self.radiopsr_loader.get_skydir(name)
ds = DataSpecification(
ft1files = ft1,
ft2files = ft2,
ltcube = ltcube,
binfile = binfile)
sa = SpectralAnalysis(ds,
binsperdec = binsperdec,
emin = 100,
emax = 1000000,
irf = "P7SOURCE_V6",
roi_dir = roi_dir,
maxROI = roi_size,
minROI = roi_size,
event_class= 0)
fit_emin = 1e2
fit_emax = 10**5.5
model=PowerLaw(index=2, e0=np.sqrt(fit_emin*fit_emax))
model.set_limits('index',-5,5)
ps = PointSource(name=name, model=model, skydir=roi_dir)
point_sources = [ps]
diffuse_sources = get_default_diffuse(diffdir="/afs/slac/g/glast/groups/diffuse/rings/2year",
gfile="ring_2year_P76_v0.fits",
ifile="isotrop_2year_P76_source_v0.txt",
limit_parameters=True)
roi=sa.roi(point_sources=point_sources,
diffuse_sources=diffuse_sources,
catalogs=catalog,
fit_emin=fit_emin, fit_emax=fit_emax)
return roi
def get_roi(self, name, phase,
fit_emin, fit_emax,
binsperdec,
extended=False,
roi_size=10,
catalog_kwargs=dict(),
**kwargs):
""" Sets up the ROI for studying a LAT Pulsar in the off pulse. """
sourcedict=yaml.load(open(self.pwndata))[name]
ltcube=sourcedict['ltcube']
pulsar_position=SkyDir(*sourcedict['cel'])
ft1=sourcedict['ft1']
ft2=sourcedict['ft2']
source = PWNRegion.get_source(name,
position = pulsar_position,
fit_emin = fit_emin,
fit_emax = fit_emax,
sigma = 0.1,
extended=extended)
sources = [source]
roi_dir = pulsar_position
phase = PhaseRange(phase)
point_sources, diffuse_sources = [],[]
for source in sources:
if isinstance(source,PointSource):
point_sources.append(source)
else:
diffuse_sources.append(source)
diffuse_sources += PWNRegion.get_background()
catalog=PWNRegion.get_catalog(**catalog_kwargs)
binfile=join(self.savedir,'binned_phased.fits')
phased_ltcube=PWNRegion.phase_ltcube(ltcube,phase,self.savedir)
phased_ft1=PWNRegion.phase_ft1(ft1,phase,self.savedir)
ds = DataSpecification(
ft1files = phased_ft1,
ft2files = ft2,
ltcube = phased_ltcube,
binfile = binfile)
print 'For now, 4 bins per decade. Eventually, this will have to be better.'
sa = SpectralAnalysis(ds,
binsperdec = binsperdec,
emin = 100,
emax = 1000000,
irf = "P7SOURCE_V6",
roi_dir = roi_dir,
maxROI = roi_size,
minROI = roi_size,
event_class= 0)
roi=sa.roi(point_sources=point_sources,
diffuse_sources=diffuse_sources,
catalogs=catalog,
phase_factor=1,
fit_emin=fit_emin, fit_emax=fit_emax,
**kwargs)
print 'bins ',roi.bin_edges
roi.extra = dict(
unphased_ft1 = ft1,
unphased_ltcube = ltcube,
phase = phase)
self.roi = roi
return roi
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
mc.simulate()
print 'Done Simulating, now fitting'
ds=DataSpecification(
ft1files = ft1,
ft2files = ft2,
ltcube = ltcube,
binfile = binfile)
sa=SpectralAnalysis(ds,
binsperdec = 8,
irf = irf,
roi_dir = skydir_mc,
maxROI = 10,
minROI = 10,
event_class = 0,
)
roi=sa.roi(
roi_dir=skydir_mc,
diffuse_sources=[bg.copy()],
fit_emin = emin,
fit_emax = emax
)
roi.add_source(ps.copy())
roi.print_summary()
roi.fit()
def time_cut(roi, tstart, tstop, subdir, use_pointlike_ltcube, verbosity):
""" Create a new ROI given a time cut. """
sa = roi.sa
ds = sa.dataspec
get_defaults = lambda obj: [
k[0] for k in obj.defaults if not isinstance(k, str)
]
get_kwargs = lambda obj: {
k: obj.__dict__[k]
for k in get_defaults(obj)
}
ds_kwargs, sa_kwargs, roi_kwargs = map(get_kwargs, [ds, sa, roi])
point_sources = [i.copy() for i in roi.psm.point_sources]
diffuse_sources = [i.copy() for i in roi.dsm.diffuse_sources]
if sa_kwargs['tstart'] != 0 or sa_kwargs['tstop'] != 0:
raise Exception("sanity check")
# * cut ft1file on time using gtselect
ft2files = roi.sa.pixeldata.ft2files
if len(ft2files) > 1: raise Exception("...")
ft2file = ft2files[0]
evfile = Gtlike.make_evfile(roi, subdir)
cut_evfile = join(subdir, "cut_ft1_%s_%s.fits" % (tstart, tstop))
if not exists(cut_evfile):
if verbosity: print 'Running gtselect'
gtselect = GtApp('gtselect', 'dataSubselector')
gtselect.run(infile=evfile,
outfile=cut_evfile,
ra=0,
dec=0,
rad=180,
tmin=tstart,
tmax=tstop,
emin=1,
emax=1e7,
zmax=180)
else:
if verbosity:
print '... Skiping gtselect for %s to %s' % (tstart, tstop)
ds_kwargs['ft1files'] = cut_evfile
# * create new binfile and ltcube
ds_kwargs['binfile'] = join(subdir,
'binned_%s_%s.fits' % (tstart, tstop))
# save this to see if it has been phased by
# the function uw.utilities.phasetools.phase_ltcube
all_time_ltcube = ds_kwargs['ltcube']
new_ltcube = join(subdir, 'ltcube_%s_%s.fits' % (tstart, tstop))
if not exists(new_ltcube):
if verbosity:
print 'Running gtltcube for %s to %s' % (tstart, tstop)
if use_pointlike_ltcube:
pointlike_ltcube(evfile=cut_evfile,
scfile=ft2file,
outfile=new_ltcube,
dcostheta=0.025,
binsz=1,
zmax=roi.sa.zenithcut,
cone_angle=roi.sa.exp_radius,
dir=roi.roi_dir)
else:
gtltcube = GtApp('gtltcube', 'Likelihood')
gtltcube.run(evfile=cut_evfile,
scfile=ft2file,
outfile=new_ltcube,
dcostheta=0.025,
binsz=1)
else:
if verbosity:
print '... Skiping gtltcube for %s to %s' % (tstart, tstop)
# next, check if ltcube is phased, kind of a kluge
f = pyfits.open(all_time_ltcube)
if f['exposure'].header.has_key('PHASE'):
assert f['exposure'].header['PHASE'] == f[
'weighted_exposure'].header['PHASE']
# If so, phase new ltcube
phase = f['exposure'].header['PHASE']
phased_ltcube = join(subdir,
'phased_ltcube_%s_%s.fits' % (tstart, tstop))
if not exists(phased_ltcube):
phase_ltcube(new_ltcube, phased_ltcube, phase)
else:
if verbosity:
print '... Skiping gtltcube phasing for %s to %s' % (
tstart, tstop)
ds_kwargs['ltcube'] = phased_ltcube
else:
ds_kwargs['ltcube'] = new_ltcube
# * create new ds, sa, and roi
new_ds = DataSpecification(**ds_kwargs)
sa = SpectralAnalysis(new_ds, **sa_kwargs)
return sa.roi(point_sources=point_sources,
diffuse_sources=diffuse_sources,
**roi_kwargs)
latextdir=expandvars('$extended_archives/gll_psc_v07_templates'),
free_radius=5,
)
if args.test:
roi_size=5
else:
roi_size=10*np.sqrt(2)
print 'conv',conv_type
sa = SpectralAnalysis(ds,
emin = emin,
emax = emax,
binsperdec=binsperdec,
roi_dir=roi_dir,
minROI=roi_size,
maxROI=roi_size,
conv_type = conv_type,
irf='P7SOURCE_V6')
# finally, we can build the ROI for LAT analysis
roi=sa.roi(
catalogs=catalog,
diffuse_sources=diffuse_sources,
fit_emin = emin,
fit_emax = emax,
)
gtifile=ltcube,
zmax=100)
mc.simulate()
ds = DataSpecification(
ft1files = ft1,
ft2files = ft2,
ltcube = ltcube,
binfile = join(simdir,'binned.fits'))
sa = SpectralAnalysis(ds,
binsperdec=8,
emin = args.emin,
emax = args.emax,
irf=args.irf,
event_class=0,
roi_dir=roi_dir,
minROI=roi_size,
maxROI=roi_size,
use_weighted_livetime=True,
zenithcut=100,
)
roi = sa.roi(roi_dir=roi_dir,
point_sources=point_sources,
diffuse_sources=diffuse_sources,
)
state = PointlikeState(roi)
results = r = dict(argparse_kwargs(args))
mc=sourcedict(roi, name, save_TS=False, errors=False)
def setup_pwn(name,pwndata,phase, free_radius=5, tempdir=None, **kwargs):
"""Name of the source
pwndata Yaml file
returns pointlike ROI.
"""
sources=yaml.load(open(pwndata))
catalog_name=sources[name]['catalog']
ltcube=sources[name]['ltcube']
pulsar_position=SkyDir(*sources[name]['dir'])
ft2=sources[name]['ft2']
ft1=sources[name]['ft1']
# in case no list was passed
if len(phase)==2 and isinstance(phase[0],numbers.Real) and \
isinstance(phase[1],numbers.Real):
# write in case phase wraps around.
if phase[0]>phase[1]:
phase=[[phase[0],1.0],[0.0,phase[1]]]
else:
phase = [phase]
phase_factor=get_phase_factor(phase)
catalog=FermiCatalog(e("$FERMI/catalogs/gll_psc_v02.fit"),free_radius=free_radius)
catalog_source=[i for i in catalog.get_sources(SkyDir(),180) if i.name==catalog_name][0]
center=catalog_source.skydir
if tempdir is None: tempdir=mkdtemp(prefix='/scratch/')
binfile=j(tempdir,'binned_phased.fits')
# apply phase cut to ft1 file
phased_ft1 = j(tempdir,'ft1_phased.fits')
phasetools.phase_cut(ft1,phased_ft1,phaseranges=phase)
# create a temporary ltcube scaled by the phase factor
# phased_ltcube=j(tempdir,'phased_ltcube.fits')
# phase_ltcube(ltcube,phased_ltcube, phase=[0.0,1.0])
phased_ltcube=ltcube
from uw.like.pointspec import DataSpecification
data_specification = DataSpecification(
ft1files = phased_ft1,
ft2files = ft2,
ltcube = phased_ltcube,
binfile = binfile)
spectral_analysis = SpectralAnalysis(data_specification,
binsperdec = 4,
emin = 100,
emax = 100000,
irf = "P6_V3_DIFFUSE",
roi_dir = center,
maxROI = 10,
minROI = 10)
roi=spectral_analysis.roi(
roi_dir=center,
diffuse_sources=get_default_diffuse(diffdir=e("$FERMI/diffuse"),
gfile="gll_iem_v02.fit",
ifile="isotropic_iem_v02.txt"),
catalogs = catalog,
phase_factor = phase_factor,
**kwargs) # phaseing already done to the ltcube
print "phase_factor=%.2f"%phase_factor
# keep overall flux of catalog source,
# but change the starting index to 2.
roi.modify(which=catalog_name, name=name, index=2,
keep_old_flux=True)
roi.toXML(filename="essai")
print roi
roi.print_summary()
for names in roi.get_names():
try :
roi.modify(names,Norm=roi.get_model(names)[0]*roi.phase_factor)
except :
try :
roi.modify(names,Int_flux=roi.get_model(names)[0]*roi.phase_factor)
except :
print names
table=roi.get_names()
print roi.modify(which=table[len(table)-2],model=PowerLaw(p=[1.0*phase_factor,0.1]),free=[True,False])
print roi.modify(which=table[len(table)-1],model=PowerLaw(p=[1.0*phase_factor,0.1]),free=[True,False])
# print roi.modify(which='eg_v02',free=[False])
print roi
return roi
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
def __init__(self,**kw):
self.init(**kw)
SpectralAnalysis.__init__(self,self.data,**kw)
def setup_pointlike(name,pwnlist,phasing=True):
"""Name of the source
pwnlist Yaml file
phasing=true : apply phase cut
phasing=false : don't do it"""
sources=yaml.load(open(pwnlist))
catalog_name=sources[name]['catalog']
phase=sources[name]['phase']
ltcube=sources[name]['ltcube']
pulsar_position=SkyDir(*sources[name]['dir'])
if phasing==True:
phase_factor=phase[1]-phase[0] if phase[1]>phase[0] else (1-phase[0]) + (phase[1]-0)
ft1=sources[name]['ft1']
else :
phase_factor=1.0
raise Exception("Unable to phase data")
catalog=FermiCatalog(e("$FERMI/catalogs/gll_psc_v02.fit"),free_radius=5)
catalog_source=[i for i in catalog.get_sources(SkyDir(),180) if i.name==catalog_name][0]
center=catalog_source.skydir
from uw.like.pointspec import DataSpecification
data_specification = DataSpecification(
ft1files = ft1,
ltcube = ltcube,
binfile = "binned_%s.fits" % name)
spectral_analysis = SpectralAnalysis(data_specification,
binsperdec = 8,
emin = 100,
emax = 100000,
irf = "P6_V3_DIFFUSE",
roi_dir = center,
maxROI = 10,
minROI = 10)
roi=LandeROI(spectral_analysis.roi(
roi_dir=center,
diffuse_sources=get_default_diffuse(diffdir=e("$FERMI/diffuse"),
gfile="gll_iem_v02.fit",
ifile="isotropic_iem_v02.txt"),
catalogs = catalog,
fit_emin = 100,
fit_emax = 100000,
catalog_include_radius = 20,
phase_factor = phase_factor)
)
roi.del_source(catalog_name)
# make residual TS map
# add in PWN Candidate
source=PointSource(
name=name,
model=PowerLaw(p=[1,2]),
skydir=pulsar_position
)
source.model.set_flux(1e-7,emin=100,emax=100000)
roi.add_source(source)
return roi
def setup_region(name,pwndata,phase, free_radius=5, tempdir=None, maxroi=10,
xml=None, **kwargs):
"""Name of the source
pwndata Yaml file
returns pointlike ROI.
"""
phase = PhaseRange(phase)
sources=yaml.load(open(pwndata))
catalog_name=sources[name]['catalog']['2fgl']
ltcube=sources[name]['ltcube']
pulsar_position=SkyDir(*sources[name]['dir'])
ft2=sources[name]['ft2']
ft1=sources[name]['ft1']
catalog=FermiCatalog(e("$FERMI/catalogs/gll_psc_v02.fit"))
catalog=Catalog2FGL('$FERMI/catalogs/gll_psc_v05.fit',
latextdir='$FERMI/extended_archives/gll_psc_v05_templates',
free_radius=free_radius)
catalog_source=catalog.get_source(catalog_name)
center=catalog_source.skydir
if tempdir is None: tempdir=mkdtemp(prefix='/scratch/')
binfile=j(tempdir,'binned_phased.fits')
if np.allclose(phase.phase_fraction,1):
phased_ltcube = ltcube
phased_ft1 = ft1
else:
# create a temporary ltcube scaled by the phase factor
phased_ltcube=j(tempdir,'phased_ltcube.fits')
phase_ltcube(ltcube,phased_ltcube, phase=phase)
# apply phase cut to ft1 file
phased_ft1 = j(tempdir,'ft1_phased.fits')
phasetools.phase_cut(ft1,phased_ft1,phaseranges=phase.tolist(dense=False))
from uw.like.pointspec import DataSpecification
ds = DataSpecification(
ft1files = phased_ft1,
ft2files = ft2,
ltcube = phased_ltcube,
binfile = binfile)
sa = SpectralAnalysis(ds,
binsperdec = 8,
emin = 100,
emax = 100000,
irf = "P6_V11_DIFFUSE",
roi_dir = center,
maxROI = maxroi,
minROI = maxroi)
if xml is None:
roi=sa.roi(
diffuse_sources=get_default_diffuse(diffdir="/afs/slac/g/glast/groups/diffuse/mapcubes",
gfile="gll_iem_v02.fit",
ifile="isotropic_iem_v02.txt"),
catalogs = catalog,
phase_factor =1,
**kwargs)
else:
roi=sa.roi_from_xml(
roi_dir=center,
xmlfile = xml,
phase_factor =1,
**kwargs)
print 'bins ',roi.bin_edges
roi.del_source(catalog_name)
return roi
def setup_pwn(name,pwndata,phase, free_radius=5, tempdir=None, emin=1.0e2, emax=1.0e5,maxroi=10,model=None,**kwargs):
"""Name of the source
pwndata Yaml file
returns pointlike ROI.
"""
sources=yaml.load(open(pwndata))
catalog_name=sources[name]['catalog']
ltcube=sources[name]['ltcube']
pulsar_position=SkyDir(*sources[name]['dir'])
ft2=sources[name]['ft2']
ft1=sources[name]['ft1']
# in case no list was passed
if len(phase)==2 and isinstance(phase[0],numbers.Real) and \
isinstance(phase[1],numbers.Real):
# write in case phase wraps around.
if phase[0]>phase[1]:
phase=[[phase[0],1.0],[0.0,phase[1]]]
else:
phase = [phase]
phase_factor=get_phase_factor(phase)
print "phase"
print phase
print "phase_factor=%.2f"%phase_factor
catalog=FermiCatalog(e("$FERMI/catalogs/gll_psc_v02.fit"),free_radius=free_radius)
catalog_source=[i for i in catalog.get_sources(SkyDir(),180) if i.name==catalog_name][0]
center=catalog_source.skydir
if tempdir is None: tempdir=mkdtemp(prefix='/scratch/')
binfile=j(tempdir,'binned_phased.fits')
# apply phase cut to ft1 file
phased_ft1 = j(tempdir,'ft1_phased.fits')
phasetools.phase_cut(ft1,phased_ft1,phaseranges=phase)
# create a temporary ltcube scaled by the phase factor
# phased_ltcube=j(tempdir,'phased_ltcube.fits')
# phase_ltcube(ltcube,phased_ltcube, phase=phase)
phased_ltcube=ltcube
from uw.like.pointspec import DataSpecification
data_specification = DataSpecification(
ft1files = phased_ft1,
ft2files = ft2,
ltcube = phased_ltcube,
binfile = binfile)
spectral_analysis = SpectralAnalysis(data_specification,
binsperdec = 4,
emin = 100,
emax = 100000,
irf = "P6_V3_DIFFUSE",
roi_dir = center,
maxROI = maxroi,
minROI = maxroi)
if model == None :
roi=spectral_analysis.roi(
roi_dir=center,
diffuse_sources=get_default_diffuse(diffdir=e("$FERMI/diffuse"),
gfile="gll_iem_v02.fit",
ifile="isotropic_iem_v02.txt"),
catalogs = catalog,
phase_factor = 1.0,
fit_emin = [emin,emin],
fit_emax = [emax,emax],
**kwargs)
else :
roi=spectral_analysis.roi(
roi_dir=center,
xmlfile = model,
phase_factor =1.0,
fit_emin = [emin,emin],
fit_emax = [emax,emax],
**kwargs)
print "---------------------Energy range--------------------"
print "emin="+str(roi.bands[0].emin)+"\n"
print "emax="+str(roi.bands[len(roi.bands)-1].emax)+"\n"
# keep overall flux of catalog source,
# but change the starting index to 2.
roi.modify(which=catalog_name, name=name, index=2,
keep_old_flux=True)
return roi
print "No simulated photons for source %s" % source_str
all_binfile = diffuse_binfile
ds = DataSpecification(ft2files=ft2, ltcube=ltcube, binfile=all_binfile)
results_dict.append(dict(i=i, istr=istr, flux_mc=flux_mc, index_mc=index_mc))
for fit_irf in ["P7SOURCE_V6", "P7SOURCE_V4"]:
print "Using fit_irf %s" % fit_irf
sa = SpectralAnalysis(
ds,
irf=fit_irf,
roi_dir=skydir_mc,
maxROI=10,
minROI=10,
event_class=0, # not this is necessary for MC data
emin=emin,
emax=emax,
)
source_guess = PointSource(name=name_mc, skydir=skydir_mc, model=model_mc.copy())
roi = sa.roi(
roi_dir=skydir_mc, diffuse_sources=[j.copy() for j in diffuse_sources], point_sources=[source_guess]
)
print "bins = ", roi.bin_edges
print "Fitting unmodified ROI"
roi_dir = SkyDir(94.310,22.580)
name = 'IC443'
ltcube = "/u/gl/funk/data/GLAST/ExtendedSources/NewAnalysis/gtlike/IC443/ltcube_239557414_334152002.fits"
data_specification = DataSpecification(
ft1files = join(simdir,"simulated_ft1.fits"),
ft2files = "/u/gl/bechtol/disk/drafts/radio_quiet/36m_gtlike/trial_v1/ft2-30s_239557414_334152027.fits",
ltcube = ltcube,
binfile = "binned.fits")
sa = SpectralAnalysis(data_specification,
binsperdec = 8,
emin = 10, # MeV
emax = 1e6, # MeV
irf = 'P7SOURCE_V6',
roi_dir = roi_dir,
maxROI = 10,
event_class= 0,
minROI = 10,
use_weighted_livetime=True)
roi = sa.roi_from_xml(
xmlfile=expandvars(join(simdir,"gtlike_model.xml")),
roi_dir=roi_dir,
fit_emin=10**1.75, # 56 MeV
fit_emax=10**3.25, # 1778 MeV
)
state=PointlikeState(roi)
results=dict()
ltcube = dict2fgl['ltcube']
ds = DataSpecification(
ft1files = ft1,
ft2files = ft2,
ltcube = ltcube,
binfile = binfile)
emin=1e3
emax=1e5
sa = SpectralAnalysis(ds,
emin = emin,
emax = emax,
irf='P7SOURCE_V6',
roi_dir=roi_dir,
minROI=10,
maxROI=10,
event_class=0
)
diffdir='/afs/slac/g/glast/groups/diffuse/rings/2year/'
diffuse_sources = get_default_diffuse(diffdir=diffdir,
gfile='ring_2year_P76_v0.fits',
ifile='isotrop_2year_P76_source_v0.txt')
roi = sa.roi(roi_dir=roi_dir, diffuse_sources = diffuse_sources)
roi.plot_counts_map(filename='counts_map_%s.png' % extra, size=10*np.sqrt(2), pixelsize=0.5)
outfile={'start_cel':[skydir.ra(),skydir.dec()],
'start_gal':[skydir.l(),skydir.b()],
'name':name}
ds = DataSpecification(
binfile = datafiles.binfile,
ltcube = datafiles.ltcube,
ft1files = datafiles.ft1files,
ft2files = datafiles.ft2file)
sa = SpectralAnalysis(ds,
binsperdec = datafiles.binsperdecade,
emin = datafiles.emin,
emax = datafiles.emax,
roi_dir = skydir,
irf = args.irf,
psf_irf = args.psf_irf,
exp_radius = args.roi+5,
maxROI = args.roi,
minROI = args.roi,
event_class = 0) # p7 has funny event classes
for hypothesis in args.hypothesis:
print
print '#'*60
print
print 'Beginning hypothesis %s' % hypothesis
print
def get_roi(self,
name,
phase,
fit_emin,
fit_emax,
binsperdec,
extended=False,
roi_size=10,
catalog_kwargs=dict(),
**kwargs):
""" Sets up the ROI for studying a LAT Pulsar in the off pulse. """
sourcedict = yaml.load(open(self.pwndata))[name]
ltcube = sourcedict['ltcube']
pulsar_position = SkyDir(*sourcedict['cel'])
ft1 = sourcedict['ft1']
ft2 = sourcedict['ft2']
source = PWNRegion.get_source(name,
position=pulsar_position,
fit_emin=fit_emin,
fit_emax=fit_emax,
sigma=0.1,
extended=extended)
sources = [source]
roi_dir = pulsar_position
phase = PhaseRange(phase)
point_sources, diffuse_sources = [], []
for source in sources:
if isinstance(source, PointSource):
point_sources.append(source)
else:
diffuse_sources.append(source)
diffuse_sources += PWNRegion.get_background()
catalog = PWNRegion.get_catalog(**catalog_kwargs)
binfile = join(self.savedir, 'binned_phased.fits')
phased_ltcube = PWNRegion.phase_ltcube(ltcube, phase, self.savedir)
phased_ft1 = PWNRegion.phase_ft1(ft1, phase, self.savedir)
ds = DataSpecification(ft1files=phased_ft1,
ft2files=ft2,
ltcube=phased_ltcube,
binfile=binfile)
print 'For now, 4 bins per decade. Eventually, this will have to be better.'
sa = SpectralAnalysis(ds,
binsperdec=binsperdec,
emin=100,
emax=1000000,
irf="P7SOURCE_V6",
roi_dir=roi_dir,
maxROI=roi_size,
minROI=roi_size,
event_class=0)
roi = sa.roi(point_sources=point_sources,
diffuse_sources=diffuse_sources,
catalogs=catalog,
phase_factor=1,
fit_emin=fit_emin,
fit_emax=fit_emax,
**kwargs)
print 'bins ', roi.bin_edges
roi.extra = dict(unphased_ft1=ft1, unphased_ltcube=ltcube, phase=phase)
self.roi = roi
return roi