Exemple #1
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def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
    print 'Performing Gtlike crosscheck for %s' % hypothesis

    gtlike=Gtlike(roi)
    like=gtlike.like
    like.fit(covar=True)

    r=sourcedict(like, name)

    if upper_limit:
        r['upper_limit'] = powerlaw_upper_limit(like, name, emin=emin, emax=emax, cl=.95)
    
    if cutoff:
        r['test_cutoff']=test_cutoff(like,name)

    for kind, kwargs in [['4bpd',dict(bin_edges=np.logspace(2,5,13))],
                         ['1bpd',dict(bin_edges=np.logspace(2,5,4))]]:

        print 'Making %s SED' % kind
        sed = SED(like, name, **kwargs)
        sed.plot('sed_gtlike_%s_%s_%s.png' % (kind,hypothesis,name)) 
        sed.verbosity=True
        sed.save('sed_gtlike_%s_%s_%s.dat' % (kind,hypothesis,name))

    return r
Exemple #2
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def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
    print "Performing Gtlike crosscheck for %s" % hypothesis

    gtlike = Gtlike(roi)
    like = gtlike.like
    like.fit(covar=True)

    r = sourcedict(like, name)

    if upper_limit:
        r["upper_limit"] = powerlaw_upper_limit(like, name, emin=emin, emax=emax, cl=0.95)

    if cutoff:
        r["test_cutoff"] = test_cutoff(like, name)

    for kind, kwargs in [
        ["4bpd", dict(bin_edges=np.logspace(2, 5, 13))],
        ["1bpd", dict(bin_edges=np.logspace(2, 5, 4))],
    ]:

        print "Making %s SED" % kind
        sed = SED(like, name, **kwargs)
        sed.plot("sed_gtlike_%s_%s_%s.png" % (kind, hypothesis, name))
        sed.verbosity = True
        sed.save("sed_gtlike_%s_%s_%s.dat" % (kind, hypothesis, name))

    return r
Exemple #3
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def gtlike_analysis(roi, name, emin, emax, hypothesis, snrsize, upper_limit=False):
    """ perform spectral fit with gtlike to crosscheck the point-like anlaysis. """

    print '\n\nPerforming Gtlike analysis\n\n'

    gtlike=Gtlike(roi,binsz=1./8)
    like=gtlike.like

    like.fit(covar=True)

    results = sourcedict(like,name,emin=emin,emax=emax)
    if upper_limit:
        # *) Perform upper limits assuming spectral index 2

        # N.B., for the E>10GeV analysis we are very much in the Poisson instead
        # of Gaussian regime. The likelihood function will be VERY linear. As a result,
        # delta_log_like_limits = 50 should be much more reasonable (not quite sure
        # how to quantify this right now...)
        results['upper_limit'] = powerlaw_upper_limit(like,name, delta_log_like_limits=50, verbosity=2, emin=emin, emax=emax)

    return results
Exemple #4
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    if fit_extension:
        try:
            roi.fit_extension(name)
            roi.localize(name, update=True)
        except Exception, ex:
            print 'ERROR localizing: ', ex
        fit()

    p = sourcedict(roi, name)

    if extension_ul:
        print 'UNABLE To Calculate Extension Upper limit'

    if upper_limit:
        p['upper_limit'] = powerlaw_upper_limit(roi, name, emin=emin, emax=emax, cl=.95)
    if cutoff:
        p['test_cutoff']=test_cutoff(roi,name)

    roi.plot_sed(which=name,filename='sed_pointlike_%s_%s.pdf' % (hypothesis,name), use_ergs=True)
 
    roi.save('roi_%s_%s.dat' % (hypothesis,name))

    #plot(roi, hypothesis)
    return p

def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
    print 'Performing Gtlike crosscheck for %s' % hypothesis

    gtlike=Gtlike(roi)
    like=gtlike.like
Exemple #5
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            roi.fit_extension(name)
            roi.localize(name, update=True)
        except Exception, ex:
            print "ERROR localizing: ", ex
        fit()

    p = sourcedict(roi, name)

    if extension_upper_limit:
        print "Calculating extension upper limit"
        p["extension_upper_limit"] = roi.extension_upper_limit(
            which=name, confidence=0.95, spatial_model=Gaussian(), npoints=10
        )

    if upper_limit:
        p["upper_limit"] = powerlaw_upper_limit(roi, name, emin=emin, emax=emax, cl=0.95)
    if cutoff:
        p["test_cutoff"] = test_cutoff(roi, name)

    roi.plot_sed(which=name, filename="sed_pointlike_%s_%s.pdf" % (hypothesis, name), use_ergs=True)

    roi.save("roi_%s_%s.dat" % (hypothesis, name))

    if do_plots:
        plots(roi, hypothesis)
    return p


def gtlike_analysis(roi, hypothesis, upper_limit=False, cutoff=False):
    print "Performing Gtlike crosscheck for %s" % hypothesis
Exemple #6
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        try:
            print 'First, localize with GridLocalize (helps with convergence)'
            size=max(snrsize,0.5)
            grid=GridLocalize(roi,which=name,size=size,pixelsize=size/10)
            skydir = grid.best_position()
            print 'Using Grid Localize, best position is (l,b)=(%.3f,%.3f)' % (skydir.l(),skydir.b())

            roi.modify(which=name, skydir=skydir)

            roi.localize(which=name, update=True)
        except Exception, ex:
            print 'ERROR localizing: ',ex

    if fit_extension:
        fit()
        roi.fit_extension(which=name)

    fit()

    print 'Final Spectral Model for %s hypothesis:' % hypothesis
    roi.print_summary(galactic=True)

    results=sourcedict(roi,name,emin=emin,emax=emax)

    if upper_limit:
        results['upper_limit'] = powerlaw_upper_limit(roi,name, emin=emin, emax=emax, verbosity=2)

    roi.save('roi_%s.dat' % hypothesis)

    return results