Beispiel #1
0
def gtlike_analysis(pipeline, roi, name, hypothesis, upper_limit):
    print 'Performing Gtlike crosscheck for %s' % hypothesis

    gtlike = Gtlike(roi, savedir='savedir' if pipeline.cachedata else None)
    like = gtlike.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)

    like.writeXml("%s/srcmodel_gtlike_%s_%s.xml" %
                  (pipeline.dirdict['data'], hypothesis, name))

    r = source_dict(like, name)

    upper_limit_kwargs = dict()

    if upper_limit:
        pul = GtlikePowerLawUpperLimit(like, name, cl=.95, verbosity=4)
        r['powerlaw_upper_limit'] = pul.todict()

    def sed(kind, **kwargs):
        print 'Making %s SED' % kind
        s = GtlikeSED(like,
                      name,
                      always_upper_limit=True,
                      verbosity=4,
                      upper_limit_kwargs=upper_limit_kwargs,
                      **kwargs)
        s.plot('%s/sed_gtlike_%s_%s.png' %
               (pipeline.dirdict['seds'], kind, name))
        s.save('%s/sed_gtlike_%s_%s.yaml' %
               (pipeline.dirdict['seds'], kind, name))

    sed('1bpd_%s' % hypothesis, bin_edges=[10**2, 10**3, 10**4, 10**5.5])
    sed('2bpd_%s' % hypothesis, bin_edges=np.logspace(2, 5.5, 8))
    if not pipeline.fast:
        sed('4bpd_%s' % hypothesis, bin_edges=np.logspace(2, 5.5, 15))

    return r
Beispiel #2
0
def gtlike_analysis(pipeline, roi, name, hypothesis, upper_limit):
    print "Performing Gtlike crosscheck for %s" % hypothesis

    gtlike = Gtlike(roi, savedir="savedir" if pipeline.cachedata else None)
    like = gtlike.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)

    like.writeXml("%s/srcmodel_gtlike_%s_%s.xml" % (pipeline.dirdict["data"], hypothesis, name))

    r = source_dict(like, name)

    upper_limit_kwargs = dict()

    if upper_limit:
        pul = GtlikePowerLawUpperLimit(like, name, cl=0.95, verbosity=4)
        r["powerlaw_upper_limit"] = pul.todict()

    def sed(kind, **kwargs):
        print "Making %s SED" % kind
        s = GtlikeSED(like, name, always_upper_limit=True, verbosity=4, upper_limit_kwargs=upper_limit_kwargs, **kwargs)
        s.plot("%s/sed_gtlike_%s_%s.png" % (pipeline.dirdict["seds"], kind, name))
        s.save("%s/sed_gtlike_%s_%s.yaml" % (pipeline.dirdict["seds"], kind, name))

    sed("1bpd_%s" % hypothesis, bin_edges=[10 ** 2, 10 ** 3, 10 ** 4, 10 ** 5.5])
    sed("2bpd_%s" % hypothesis, bin_edges=np.logspace(2, 5.5, 8))
    if not pipeline.fast:
        sed("4bpd_%s" % hypothesis, bin_edges=np.logspace(2, 5.5, 15))

    return r
Beispiel #3
0
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)
Beispiel #4
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    fit(fit_bg_first=True)
    fit() 

    if localize:
        paranoid_localize(roi, name, verbosity=4)

    if fit_extension:
        roi.fit_extension(which=name)
        paranoid_localize(roi, name)

    fit()

    print 'Making pointlike SED for hypothesis %s' % hypothesis
    sed = PointlikeSED(roi, name, verbosity=4)
    sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' % (pipeline.dirdict['seds'],hypothesis,name))
    sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' % (pipeline.dirdict['seds'],hypothesis,name)) 

    print_summary()

    p = source_dict(roi, name)

    if upper_limit:
        pul = PointlikePowerLawUpperLimit(roi, name, cl=.95, verbosity=4)
        p['powerlaw_upper_limit']=pul.todict()

    roi.toXML(filename="%s/srcmodel_pointlike_%s_%s.xml"%(pipeline.dirdict['data'], hypothesis, name))
 
    roi.save('roi_%s_%s.dat' % (hypothesis,name))

    return p
Beispiel #5
0
    if localize:
        paranoid_localize(roi, name, verbosity=4)

    if fit_extension:
        roi.fit_extension(which=name)
        paranoid_localize(roi, name)

    fit()

    print 'Making pointlike SED for hypothesis %s' % hypothesis
    sed = PointlikeSED(roi, name, verbosity=4)
    sed.save('%s/sed_pointlike_4bpd_%s_%s.yaml' %
             (pipeline.dirdict['seds'], hypothesis, name))
    sed.plot('%s/sed_pointlike_4bpd_%s_%s.png' %
             (pipeline.dirdict['seds'], hypothesis, name))

    print_summary()

    p = source_dict(roi, name)

    if upper_limit:
        pul = PointlikePowerLawUpperLimit(roi, name, cl=.95, verbosity=4)
        p['powerlaw_upper_limit'] = pul.todict()

    roi.toXML(filename="%s/srcmodel_pointlike_%s_%s.xml" %
              (pipeline.dirdict['data'], hypothesis, name))

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

    return p