def pointlike_analysis(roi, name, hypothesis, max_free,
seddir, datadir, plotdir,
localize=False,
fit_extension=False,
cutoff=False,
cutoff_model=None,
override_localization=None,
):
""" emin + emax used for computing upper limits. """
print 'Performing Pointlike analysis for %s' % hypothesis
print_summary = lambda: roi.print_summary(galactic=True, maxdist=10)
print_summary()
emin, emax = get_full_energy_range(roi)
print roi
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)
print_summary()
def tsmap_plots(roi, name, hypothesis, datadir, plotdir, size, new_sources, tsmap_pixelsize=0.1, **common_kwargs):
""" TS maps """
emin, emax = get_full_energy_range(roi)
extra='%s_%s_%sdeg' % (hypothesis,name,size)
tsmap_kwargs = dict(size=size, pixelsize=tsmap_pixelsize, **common_kwargs)
def _plot(filename,title):
try:
print 'Making Band %s TS Map (size=%s, pixelsize=%s)' % (title,size,tsmap_pixelsize)
roi.plot_tsmap(filename='%s/tsmap_%s_%s.png' % (plotdir,filename,extra),
fitsfile='%s/tsmap_%s_%s.fits' % (datadir,filename,extra),
title='%s TS Map for %s (%s)' % (title,name,hypothesis),
**tsmap_kwargs)
except Exception, ex:
print 'ERROR making TS map:', ex
traceback.print_exc(file=sys.stdout)
if all_energy(emin,emax):
try:
print 'Making Band %s TS Map (size=%s, pixelsize=%s)' % (title,size,tsmap_pixelsize)
ROITSMapBandPlotter(roi,
title='Band %s TS Map for %s (%s)' % (title,name,hypothesis),
bin_edges=one_bin_per_dec(emin,emax),
**tsmap_kwargs).show(filename='%s/band_tsmap_%s_%s.png' % (plotdir,filename,extra))
except Exception, ex:
print 'ERROR making band TS map:', ex
traceback.print_exc(file=sys.stdout)
def compute(self):
roi=self.roi
which=self.which
roi.modify(which=which, model=self.model0, keep_old_flux=False)
if not self.quiet: print 'fit source %s with model0 (%s)' % (which,self.model0.name)
self.ll_0 = self.fit()
if not self.quiet:
print roi
sed = PointlikeSED(roi,name=which)
self.sed_points = sed.todict()
# TODO, get SED points
emin,emax=get_full_energy_range(roi)
flux=self.model0.i_flux(emin=emin,emax=emax)
self.model1.set_flux(flux,emin=emin,emax=emax)
self.comprehensive_model = ComprehensiveModel(self.model0.copy(), self.model1.copy())
self.comprehensive_model.theta=0.5
self.comprehensive_model_start = self.comprehensive_model.copy()
if not self.quiet: print 'fit source %s with model1 (%s)' % (which,self.model1.name)
roi.modify(which=self.which, model=self.comprehensive_model, keep_old_flux=False)
self.ll_1 = self.fit()
if not self.quiet:
print roi
self.TS_comp = 2*(self.ll_1-self.ll_0)
def counts_plots(roi, name, hypothesis, datadir, plotdir, size, pixelsize,
**common_kwargs):
""" Counts plots """
emin, emax = get_full_energy_range(roi)
extra = '%s_%s_%sdeg_%sdeg' % (hypothesis, name, size, pixelsize)
counts_kwargs = dict(size=size, **common_kwargs)
title = 'Counts Residual for %s (%s)' % (name, hypothesis)
print title
roi.plot_counts_map(
filename="%s/counts_residual_%s.png" % (plotdir, extra),
countsfile="%s/counts_residual_%s.fits" % (datadir, extra),
modelfile="%s/model_residual_%s.fits" % (datadir, extra),
pixelsize=pixelsize,
title=title,
**counts_kwargs)
roi.zero_source(which=name)
title = 'Counts Source for %s (%s)' % (name, hypothesis)
print title
roi.plot_counts_map(filename="%s/counts_source_%s.png" % (plotdir, extra),
countsfile="%s/counts_source_%s.fits" %
(datadir, extra),
modelfile="%s/model_source_%s.fits" % (datadir, extra),
pixelsize=pixelsize,
title=title,
**counts_kwargs)
roi.unzero_source(which=name)
title = 'Slice for %s (%s)' % (name, hypothesis)
print title
roi.plot_slice(which=name,
pixelsize=pixelsize,
filename="%s/counts_slice_%s.png" % (plotdir, extra),
datafile='%s/counts_slice_%s.dat' % (datadir, extra),
title=title)
title = 'Radial Integral for %s (%s)' % (name, hypothesis)
print title
roi.plot_radial_integral(
which=name,
pixelsize=pixelsize,
filename="%s/radial_integral_%s.png" % (plotdir, extra),
datafile='%s/radial_integral_%s.dat' % (datadir, extra),
title=title)
try:
title = 'Spectra for %s (%s)' % (name, hypothesis)
print title
roi.plot_counts_spectra(filename="%s/spectra_%s_%s.png" %
(plotdir, hypothesis, name),
title=title)
except Exception, ex:
print 'ERROR with plot_counts_spectra: ', ex
traceback.print_exc(file=sys.stdout)
def counts_plots(roi, name, hypothesis, datadir, plotdir, size, pixelsize, **common_kwargs):
""" Counts plots """
emin, emax = get_full_energy_range(roi)
extra='%s_%s_%sdeg_%sdeg' % (hypothesis,name,size,pixelsize)
counts_kwargs = dict(size=size, **common_kwargs)
title = 'Counts Residual for %s (%s)' % (name,hypothesis)
print title
roi.plot_counts_map(filename="%s/counts_residual_%s.png"%(plotdir,extra),
countsfile="%s/counts_residual_%s.fits"%(datadir,extra),
modelfile="%s/model_residual_%s.fits"%(datadir,extra),
pixelsize=pixelsize,
title=title,
**counts_kwargs)
roi.zero_source(which=name)
title = 'Counts Source for %s (%s)' % (name,hypothesis)
print title
roi.plot_counts_map(filename="%s/counts_source_%s.png"%(plotdir,extra),
countsfile="%s/counts_source_%s.fits"%(datadir,extra),
modelfile="%s/model_source_%s.fits"%(datadir,extra),
pixelsize=pixelsize,
title=title,
**counts_kwargs)
roi.unzero_source(which=name)
title = 'Slice for %s (%s)' % (name,hypothesis)
print title
roi.plot_slice(which=name,
pixelsize=pixelsize,
filename="%s/counts_slice_%s.png"%(plotdir,extra),
datafile='%s/counts_slice_%s.dat'%(datadir,extra),
title=title)
title = 'Radial Integral for %s (%s)' % (name,hypothesis)
print title
roi.plot_radial_integral(which=name,
pixelsize=pixelsize,
filename="%s/radial_integral_%s.png"%(plotdir,extra),
datafile='%s/radial_integral_%s.dat'%(datadir,extra),
title=title)
try:
title = 'Spectra for %s (%s)' % (name,hypothesis)
print title
roi.plot_counts_spectra(filename="%s/spectra_%s_%s.png"%(plotdir,hypothesis, name),
title=title)
except Exception, ex:
print 'ERROR with plot_counts_spectra: ', ex
traceback.print_exc(file=sys.stdout)
def smooth_plots(roi, name, hypothesis, datadir, plotdir, size, kernel_rad,
**common_kwargs):
""" smoothed counts maps """
emin, emax = get_full_energy_range(roi)
extra = '%s_%s_%sdeg_%sdeg' % (hypothesis, name, size, kernel_rad)
smooth_kwargs = dict(
which=name,
override_center=roi.roi_dir,
size=size,
colorbar_radius=1, # most interesting within one degrees
kernel_rad=kernel_rad,
**common_kwargs)
title = 'Source Map for %s (%s)' % (name, hypothesis)
print title
roi.plot_source(filename='%s/source_%s.png' % (plotdir, extra),
title=title,
**smooth_kwargs)
title = 'Sources Map for %s (%s)' % (name, hypothesis)
print title
roi.plot_sources(filename='%s/sources_%s.png' % (plotdir, extra),
title=title,
**smooth_kwargs)
if all_energy(emin, emax):
title = 'Band Source Map for %s (%s)' % (name, hypothesis)
print title
ROISourceBandPlotter(
roi,
bin_edges=one_bin_per_dec(emin, emax),
title=title,
**smooth_kwargs).show(filename='%s/band_source_%s.png' %
(plotdir, extra))
title = 'Band Sources Map for %s (%s)' % (name, hypothesis)
print title
ROISourcesBandPlotter(
roi,
bin_edges=one_bin_per_dec(emin, emax),
title=title,
**smooth_kwargs).show(filename='%s/band_sources_%s.png' %
(plotdir, extra))
def tsmap_plots(roi,
name,
hypothesis,
datadir,
plotdir,
size,
new_sources,
tsmap_pixelsize=0.1,
**common_kwargs):
""" TS maps """
emin, emax = get_full_energy_range(roi)
extra = '%s_%s_%sdeg' % (hypothesis, name, size)
tsmap_kwargs = dict(size=size, pixelsize=tsmap_pixelsize, **common_kwargs)
def _plot(filename, title):
try:
print 'Making Band %s TS Map (size=%s, pixelsize=%s)' % (
title, size, tsmap_pixelsize)
roi.plot_tsmap(
filename='%s/tsmap_%s_%s.png' % (plotdir, filename, extra),
fitsfile='%s/tsmap_%s_%s.fits' % (datadir, filename, extra),
title='%s TS Map for %s (%s)' % (title, name, hypothesis),
**tsmap_kwargs)
except Exception, ex:
print 'ERROR making TS map:', ex
traceback.print_exc(file=sys.stdout)
if all_energy(emin, emax):
try:
print 'Making Band %s TS Map (size=%s, pixelsize=%s)' % (
title, size, tsmap_pixelsize)
ROITSMapBandPlotter(
roi,
title='Band %s TS Map for %s (%s)' %
(title, name, hypothesis),
bin_edges=one_bin_per_dec(emin, emax),
**tsmap_kwargs).show(filename='%s/band_tsmap_%s_%s.png' %
(plotdir, filename, extra))
except Exception, ex:
print 'ERROR making band TS map:', ex
traceback.print_exc(file=sys.stdout)
def compute(self):
roi = self.roi
which = self.which
roi.modify(which=which, model=self.model0, keep_old_flux=False)
if not self.quiet:
print 'fit source %s with model0 (%s)' % (which, self.model0.name)
self.ll_0 = self.fit()
if not self.quiet:
print roi
sed = PointlikeSED(roi, name=which)
self.sed_points = sed.todict()
# TODO, get SED points
emin, emax = get_full_energy_range(roi)
flux = self.model0.i_flux(emin=emin, emax=emax)
self.model1.set_flux(flux, emin=emin, emax=emax)
self.comprehensive_model = ComprehensiveModel(self.model0.copy(),
self.model1.copy())
self.comprehensive_model.theta = 0.5
self.comprehensive_model_start = self.comprehensive_model.copy()
if not self.quiet:
print 'fit source %s with model1 (%s)' % (which, self.model1.name)
roi.modify(which=self.which,
model=self.comprehensive_model,
keep_old_flux=False)
self.ll_1 = self.fit()
if not self.quiet:
print roi
self.TS_comp = 2 * (self.ll_1 - self.ll_0)
def smooth_plots(roi, name, hypothesis, datadir, plotdir, size, kernel_rad, **common_kwargs):
""" smoothed counts maps """
emin, emax = get_full_energy_range(roi)
extra='%s_%s_%sdeg_%sdeg' % (hypothesis, name, size,kernel_rad)
smooth_kwargs = dict(which=name,
override_center=roi.roi_dir,
size=size,
colorbar_radius=1, # most interesting within one degrees
kernel_rad=kernel_rad,
**common_kwargs)
title = 'Source Map for %s (%s)' % (name,hypothesis)
print title
roi.plot_source(filename='%s/source_%s.png' % (plotdir, extra),
title=title,
**smooth_kwargs)
title = 'Sources Map for %s (%s)' % (name,hypothesis)
print title
roi.plot_sources(filename='%s/sources_%s.png' % (plotdir, extra),
title=title,
**smooth_kwargs)
if all_energy(emin,emax):
title = 'Band Source Map for %s (%s)' % (name,hypothesis)
print title
ROISourceBandPlotter(roi, bin_edges=one_bin_per_dec(emin,emax),
title=title,
**smooth_kwargs).show(filename='%s/band_source_%s.png' % (plotdir,extra))
title = 'Band Sources Map for %s (%s)' % (name,hypothesis)
print title
ROISourcesBandPlotter(roi, bin_edges=one_bin_per_dec(emin,emax),
title=title,
**smooth_kwargs).show(filename='%s/band_sources_%s.png' % (plotdir,extra))
def gtlike_analysis(roi, name, hypothesis, max_free,
seddir, datadir, plotdir,
upper_limit=False, cutoff=False,
cutoff_model=None,
do_bandfitter=False, do_sed=False,
):
print 'Performing Gtlike crosscheck for %s' % hypothesis
frozen = freeze_far_away(roi, roi.get_source(name).skydir, max_free)
gtlike=Gtlike(roi, extended_dir_name=datadir)
unfreeze_far_away(roi, frozen)
global like
like=gtlike.like
like.tol = 1e-1 # I found that the default tol '1e-3' would get the fitter stuck in infinite loops
import pyLikelihood as pyLike
like.setFitTolType(pyLike.ABSOLUTE)
emin, emax = get_full_energy_range(like)
print 'About to fit gtlike ROI'
print summary(like, maxdist=10)
paranoid_gtlike_fit(like, verbosity=4)
print 'Done fiting gtlike ROI'
print summary(like, maxdist=10)
spectrum_name = like.logLike.getSource(name).spectrum().genericName()
like.writeXml("%s/srcmodel_gtlike_%s_%s_%s.xml"%(datadir, hypothesis, spectrum_name, name))
r=source_dict(like, name)
#upper_limit_kwargs=dict(delta_log_like_limits=10)
upper_limit_kwargs=dict()
if upper_limit:
pul = GtlikePowerLawUpperLimit(like, name, emin=emin, emax=emax, cl=.95,
upper_limit_kwargs=upper_limit_kwargs,
verbosity=4,
xml_name=join("%s/srcmodel_gtlike_%s_%s_%s.xml" % (datadir, hypothesis, 'PowerLaw_Upper_Limit', name)))
r['powerlaw_upper_limit'] = pul.todict()
cul = GtlikeCutoffUpperLimit(like, name, Index=1.7, Cutoff=3e3, b=1, cl=.95,
upper_limit_kwargs=upper_limit_kwargs,
verbosity=4,
xml_name=join("%s/srcmodel_gtlike_%s_%s_%s.xml" % (datadir, hypothesis, 'PLSuperExpCutoff_Upper_Limit', name)))
r['cutoff_upper_limit'] = cul.todict()
if do_bandfitter:
if all_energy(emin,emax):
try:
bf = GtlikeBandFitter(like, name, bin_edges=one_bin_per_dec(emin,emax),
upper_limit_kwargs=upper_limit_kwargs,
verbosity=4)
bf.plot('%s/bandfits_gtlike_%s_%s.png' % (plotdir,hypothesis,name))
r['bandfits'] = bf.todict()
except Exception, ex:
print 'ERROR computing bandfit:', ex
traceback.print_exc(file=sys.stdout)