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 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 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 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
