def initAltFit(self,opt="MINUIT"):
"""Initiallizes a minuit optimizer to use as a backup to the
DRM optimizer. This function is used internally in the fitDRM
function so you probably will never use it. You need to run
makeObs before you run this function. If it hasn't been run,
this function will exit."""
try:
self.obs
except AttributeError:
self.logger.critical("Obs object does not exist. Create it first with the makeObs function")
return
try:
checkForFiles(self.logger,[self.likelihoodConf['model']])
if(self.commonConf['binned']):
self.ALTFIT = BinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer=opt)
else:
self.ALTFIT = UnbinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer=opt)
self.ALTFIT.tol = float(self.likelihoodConf['drmtol'])
self.ALTFITobj = pyLike.Minuit(self.ALTFIT.logLike)
self.logger.info(self.ret.subn(', ',str(self.ALTFIT))[0])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def loadUnbinnedObs(self, f, verbosity=0):
if verbosity:
print 'Loading unbinned observation:',f['ft1']
obs = UA.UnbinnedObs(eventFile=f['ft1'], scFile=f['ft2'],
expMap=f['emap'],expCube=f['ecube'],
irfs=f['irfs'])
like = UA.UnbinnedAnalysis(obs, srcModel=self.model,
optimizer=self.optimizer)
return [ obs, like ]
def unBinnedlikeFit2(self, optmz, write):
""" Computig a second likelihood fit needed to compute the SED"""
global like2
like2 = UnbinnedAnalysis(obs,str(self.src)+"_model_likehoodFit1.xml" ,optimizer=optmz)
like2.tol = 1e-8
like2obj = pyLike.NewMinuit(like2.logLike)
like2.fit(verbosity=1,covar=True,optObject=like2obj)
if write:
like2.logLike.writeXml(str(self.src)+"_model_likehoodFit2.xml")
def main(NAME, RA, DEC, EMIN, EMAX, SC, ROIfile, xmlmodelname, expmap,
expcube):
obs = UnbinnedObs(ROIfile,
SC,
expMap=expmap,
expCube=expcube,
irfs='P7REP_SOURCE_V15')
like1 = UnbinnedAnalysis(obs, xmlmodelname, optimizer='MINUIT')
like1.fit(verbosity=0)
ul = UpperLimits(like1)
UL = ul[NAME].compute(emin=float(EMIN), emax=float(EMAX))
results_ul = UL[1] * 1E-9
err = 0
print "'UL': ", results_ul
def _new_log_like(self, event_file):
new_obs = FastUnbinnedObs(event_file, self._orig_log_like.observation)
# Create empty XML to trick UnbinnedAnalysis in not reading up any source.
# We will then add the source that have been already loaded in the original likelihood object.
# This saves a lot because sources like the Galactic template do not need to be reloaded again from disk
with open("__empty_xml.xml", "w+") as f:
f.write('<source_library title="source library"></source_library>')
# Load pyLike (we use DRMNFB because it is fast, much faster than Minuit, and we do not care about the errors)
new_like = UnbinnedAnalysis.UnbinnedAnalysis(new_obs,
"__empty_xml.xml",
optimizer=self._optimizer)
# Now load the sources from the other object
for source_name in self._orig_log_like.sourceNames():
if source_name[-1] == 'e' and source_name.find("Template") < 0:
# Extended source, jump it (we didn't compute gtdiffrsp because it crashes)
continue
new_like.addSource(self._orig_log_like.logLike.source(source_name))
return new_like
def set_model(self, likelihoodModel, source_name=None):
"""
Set the model to be used in the joint minimization.
Must be a LikelihoodModel instance.
This method can also set or override a previously set source name.
"""
# with suppress_stdout():
if self._source_name is not None:
if (source_name
is not None) and (source_name != self._source_name):
log.warning('Changing target source from %s to %s' %
(self._source_name, source_name))
self._source_name = source_name
assert self._source_name in likelihoodModel.point_sources, (
'Source %s is not a source in the likelihood model! ' %
self._source_name)
self.lmc = LikelihoodModelConverter(likelihoodModel,
self.irf,
source_name=self._source_name)
self.lmc.setFileSpectrumEnergies(self.emin, self.emax, self.Nenergies)
xmlFile = str("%s.xml" % get_random_unique_name())
temp_files = self.lmc.writeXml(xmlFile, self.ra, self.dec, self.rad)
if self.kind == "BINNED":
self.like = BinnedAnalysis.BinnedAnalysis(self.obs,
xmlFile,
optimizer="DRMNFB")
else:
#import pdb;pdb.set_trace()
self.like = UnbinnedAnalysis.UnbinnedAnalysis(self.obs,
xmlFile,
optimizer="DRMNFB")
self.likelihoodModel = likelihoodModel
# Here we need also to compute the logLike value, so that the model
# in the XML file will be chanded if needed
dumb = self.get_log_like()
# Since now the Galactic template is in RAM, we can remove the temporary file
os.remove(self.lmc._unique_filename)
os.remove(xmlFile)
# Delete temporary spectral files
for temp_file in temp_files:
os.remove(temp_file)
# Build the list of the nuisance parameters
new_nuisance_parameters = self._setNuisanceParameters()
self.update_nuisance_parameters(new_nuisance_parameters)
def __init__(self,
ft1,
ft2,
expmap,
ltcube,
xml_file,
path_of_tar_file_with_simulated_ft1_files,
tsmap_spec=None,
srcname='GRB'):
# Process the simulations applying the same cuts as in the data file
sp = SimulationProcessor(ft1, ft2,
path_of_tar_file_with_simulated_ft1_files)
ra_center, dec_center, radius = sp.roi
# Now create the likelihood object
obs = MyUnbinnedObs(ft1, ft2, expMap=expmap, expCube=ltcube)
like = UnbinnedAnalysis.UnbinnedAnalysis(obs, xml_file, "MINUIT")
fast_ts = FastTS(like, ts_map_spec=tsmap_spec, target_source=srcname)
# Get the TSs
self._tss = fast_ts.process_ft1s(sp.processed_ft1s,
ra_center=ra_center,
dec_center=dec_center)
def initDRM(self):
"""Initializes the DRM optimizer (either binned or unbinned).
This is usually the second function that you run when using
this module. You need to run makeObs before you run this
function. If it hasn't been run, this function will exit."""
try:
self.obs
except AttributeError:
self.logger.critical(
"Obs object does not exist. Create it first with the makeObs function"
)
return
try:
qU.checkForFiles(self.logger, [self.likelihoodConf['model']])
if (self.commonConf['binned']):
self.DRM = BAn.BinnedAnalysis(self.obs,
self.likelihoodConf['model'],
optimizer="DRMNGB")
else:
self.DRM = UbAn.UnbinnedAnalysis(self.obs,
self.likelihoodConf['model'],
optimizer="DRMNGB")
self.DRM.tol = float(self.likelihoodConf['drmtol'])
self.logger.info(self.ret.subn(', ', str(self.DRM))[0])
except (qU.FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def initAltFit(self, opt="MINUIT"):
"""Initiallizes a minuit optimizer to use as a backup to the
DRM optimizer. This function is used internally in the fitDRM
function so you probably will never use it. You need to run
makeObs before you run this function. If it hasn't been run,
this function will exit."""
try:
self.obs
except AttributeError:
self.logger.critical(
"Obs object does not exist. Create it first with the makeObs function"
)
return
try:
qU.checkForFiles(self.logger, [self.likelihoodConf['model']])
if (self.commonConf['binned']):
self.ALTFIT = BAn.BinnedAnalysis(self.obs,
self.likelihoodConf['model'],
optimizer=opt)
else:
self.ALTFIT = UbAn.UnbinnedAnalysis(
self.obs, self.likelihoodConf['model'], optimizer=opt)
self.ALTFIT.tol = float(self.likelihoodConf['drmtol'])
self.ALTFITobj = pyLike.Minuit(self.ALTFIT.logLike)
self.logger.info(self.ret.subn(', ', str(self.ALTFIT))[0])
except (qU.FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def run(Name, Ra, Dec, minEnergy, maxEnergy, SCFile, ulTS, NMtol,evclass,model):
if evclass == 512:
irf = "P8R2_ULTRACLEAN_V6"
elif evclass == 128:
irf = "P8R2_SOURCE_V6"
elif evclass == 256:
irf = "P8R2_CLEAN_V6"
elif evclass == 1024:
irf = "P8R2_ULTRACLEANVETO_V6"
print "This is multiUBLike.\nPlease make sure that you have added a model for your source with the name: " + str(Name)
print "Calcualting the diffuse response for photons in this bin."
my_apps.diffResps['evfile'] = Name + '_gtmktime.fits'
my_apps.diffResps['scfile'] = SCFile
my_apps.diffResps['srcmdl'] = model
my_apps.diffResps['irfs'] = irf
my_apps.diffResps.run()
print "Finished calculating diffuse response. Now moving to conduct a UNBINNED likelihood analysis."
obs = UnbinnedObs(Name + '_gtmktime.fits', SCFile,expMap= Name + '_expMap.fits',expCube= Name + '_ltcube.fits', irfs=irf)
analysis = UnbinnedAnalysis(obs,model,optimizer='NewMinuit')
likeObj = pyLike.NewMinuit(analysis.logLike)
analysis.tol = NMtol
lkl = analysis.fit(verbosity=0,covar=True,optObject=likeObj)
analysis.writeXml( Name + '_output_model.xml')
fit = likeObj.getRetCode()
print "Likelihood has converged whith Code " + str(likeObj.getRetCode())
multiLike.printResults(analysis,Name, minEnergy,maxEnergy)
print "Fit has likelihood: " + str(lkl)
print "\nThe TS is below the threshold, calculating 95% confidence-level Bayesian upper limit."
limit=0#,results = IUL.calc_int(analysis,Name,cl=0.95,emin=minEnergy, emax=maxEnergy)
print "Bayesian upper limit: " + str(limit) + " photons/cm^2/s"
#Calls for the prefactor/err, index/err, and scale
N0 = analysis.model[Name].funcs['Spectrum'].getParam('Prefactor').value()
N0_err = analysis.model[Name].funcs['Spectrum'].getParam('Prefactor').error()
gamma = analysis.model[Name].funcs['Spectrum'].getParam('Index').value()
gamma_err = analysis.model[Name].funcs['Spectrum'].getParam('Index').error()
E0 = analysis.model[Name].funcs['Spectrum'].getParam('Scale').value()
#Array that returns the results of the unbinned analysis
#log-likelihood,flux,flux_err,test statisitc
Return = [lkl,analysis.flux(Name, emin=minEnergy, emax=maxEnergy),analysis.fluxError(Name, emin=minEnergy, emax=maxEnergy),limit,analysis.Ts(Name,reoptimize=False),N0,N0_err,gamma,gamma_err,E0]
return Return
def runFit(basename,directory,model,irfs,srcName,Emin,Emax):
"""
Performs the fit using maximum likelihood estimation.
"""
evtfile = directory + "/" + basename + "_filtered_gti.fits"
SC = basename + "_SC.fits"
expmap = directory + "/" + basename + "_expMap.fits"
ltcube = directory + "/" + basename + "_ltcube.fits"
obs = UnbinnedObs(eventFile=evtfile, scFile=SC, expMap=expmap,
expCube=ltcube, irfs=irfs)
analysis = UnbinnedAnalysis(obs, srcModel=model, optimizer='NEWMINUIT')
likeObj = pyLike.NewMinuit(analysis.logLike)
analysis.fit(verbosity=0,covar=True,optObject=likeObj)
num = int(directory[directory.find('bin')+3:])
return "{:8d}{:14.4e}{:14.4e}{:10.4f}{:12.4f}{:12.2f}".format(num, \
analysis.flux(srcName,emin=100,emax=300000), \
analysis.fluxError(srcName,emin=100,emax=300000), \
analysis.model[srcName].funcs['Spectrum'].getParam('Index').value(), \
analysis.model[srcName].funcs['Spectrum'].getParam('Index').error(), \
analysis.Ts(srcName))
def initMIN(self, useBadFit=False):
"""Initiallizes a New Minuit optimizer to use as a backup to
the DRM optimizer. This is usually run after you have
initially run fitDRM and created a <basename>_likeDRM.xml
model file which is used a seed for the New Minuit optimizer.
You can skip the DRM process if you like but you need to have
the proper model file (<basename>_likeDRM.xml) present in the
working directory. You need to run makeObs before you run this
function. If it hasn't been run, this function will exit. If
you want to use the non convergant fit from fitDRM, set
useBadFit to True."""
try:
self.obs
except AttributeError:
self.logger.critical("Obs object does not exist. Create it first with the makeObs function.")
return
if(useBadFit):
model = self.commonConf['base']+'_badDRMFit.xml'
else:
model = self.commonConf['base']+'_likeDRM.xml'
try:
checkForFiles(self.logger,[model])
if(self.commonConf['binned']):
self.MIN = BinnedAnalysis(self.obs,model,optimizer='NewMinuit')
else:
self.MIN = UnbinnedAnalysis(self.obs,model,optimizer='NewMinuit')
self.MIN.tol = float(self.likelihoodConf['mintol'])
self.MINobj = pyLike.NewMinuit(self.MIN.logLike)
self.logger.info(self.ret.subn(', ',str(self.MIN))[0])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def initDRM(self):
"""Initializes the DRM optimizer (either binned or unbinned).
This is usually the second function that you run when using
this module. You need to run makeObs before you run this
function. If it hasn't been run, this function will exit."""
try:
self.obs
except AttributeError:
self.logger.critical("Obs object does not exist. Create it first with the makeObs function")
return
try:
checkForFiles(self.logger,[self.likelihoodConf['model']])
if(self.commonConf['binned']):
self.DRM = BinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer="DRMNGB")
else:
self.DRM = UnbinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer="DRMNGB")
self.DRM.tol = float(self.likelihoodConf['drmtol'])
self.logger.info(self.ret.subn(', ',str(self.DRM))[0])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def set_model(self, likelihoodModel):
"""
Set the model to be used in the joint minimization.
Must be a LikelihoodModel instance.
"""
with suppress_stdout():
self.lmc = LikelihoodModelConverter(likelihoodModel, self.irf)
self.lmc.setFileSpectrumEnergies(self.emin, self.emax,
self.Nenergies)
xmlFile = str("%s.xml" % get_random_unique_name())
temp_files = self.lmc.writeXml(xmlFile, self.ra, self.dec,
self.rad)
if self.kind == "BINNED":
self.like = BinnedAnalysis.BinnedAnalysis(self.obs,
xmlFile,
optimizer="DRMNFB")
else:
self.like = UnbinnedAnalysis.UnbinnedAnalysis(self.obs,
xmlFile,
optimizer="DRMNFB")
self.likelihoodModel = likelihoodModel
# Here we need also to compute the logLike value, so that the model
# in the XML file will be chanded if needed
dumb = self.get_log_like()
# Since now the Galactic template is in RAM, we can remove the temporary file
os.remove(self.lmc._unique_filename)
os.remove(xmlFile)
# Delete temporary spectral files
for temp_file in temp_files:
os.remove(temp_file)
# Build the list of the nuisance parameters
new_nuisance_parameters = self._setNuisanceParameters()
self.update_nuisance_parameters(new_nuisance_parameters)
def initMIN(self, useBadFit=False, modelFile="", useEdisp=False):
"""Initiallizes a New Minuit optimizer to use as a backup to
the DRM optimizer. This is usually run after you have
initially run fitDRM and created a <basename>_likeDRM.xml
model file which is used a seed for the New Minuit optimizer.
You can skip the DRM process if you like but you need to have
the proper model file (<basename>_likeDRM.xml) present in the
working directory. You need to run makeObs before you run this
function. If it hasn't been run, this function will exit. If
you want to use the non convergant fit from fitDRM, set
useBadFit to True. You can also pass a custom model file name
via the modelFile parameter."""
try:
self.obs
except AttributeError:
self.logger.critical(
"Obs object does not exist. Create it first with the makeObs function."
)
return
if (useBadFit):
model = self.commonConf['base'] + '_badDRMFit.xml'
else:
model = self.commonConf['base'] + '_likeDRM.xml'
if (modelFile):
model = modelFile
try:
qU.checkForFiles(self.logger, [model])
if (self.commonConf['binned']):
self.MIN = BAn.BinnedAnalysis(self.obs,
model,
optimizer='NewMinuit')
else:
self.MIN = UbAn.UnbinnedAnalysis(self.obs,
model,
optimizer='NewMinuit')
self.MIN.tol = float(self.likelihoodConf['mintol'])
self.MINobj = pyLike.NewMinuit(self.MIN.logLike)
self.pristine = LikelihoodState(self.MIN)
self.logger.info(self.ret.subn(', ', str(self.MIN))[0])
if (useEdisp):
self.MIN.logLike.set_edisp_flag(useEdisp)
except (qU.FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def unBinnedlikeFit1(self, optmz, emin, emax, plot, write, analysisType):
"""Computing a first likelihood fit."""
# global obs
# obs = UnbinnedObs(self.gtiFile, self.scFile, expMap=self.expmapFile, expCube=self.ltcubeFile, irfs=self.irfs)
like1 = UnbinnedAnalysis(obs,self.model,optimizer=optmz)
like1.tol = 0.1
# like1.setEnergyRange(emin, emax)
like1.fit(verbosity=1)
if plot:
like1.plot()
if write:
like1.logLike.writeXml(str(self.src)+"_model_likehoodFit1.xml")
def makeObs(self):
"""Creates either a binned or unbinned observation object for
use in the likelihood analysis. This function checks for all
of the needed files first. If you do not have a needed file,
see the quickAnalysis module for creation. This function
should be run before any of the init or fit functions."""
if (self.commonConf['binned']):
try:
qU.checkForFiles(self.logger, [
self.commonConf['base'] + '_srcMaps.fits',
self.commonConf['base'] + '_ltcube.fits',
self.commonConf['base'] + '_BinnedExpMap.fits'
])
self.obs = BAn.BinnedObs(
srcMaps=self.commonConf['base'] + '_srcMaps.fits',
expCube=self.commonConf['base'] + '_ltcube.fits',
binnedExpMap=self.commonConf['base'] +
'_BinnedExpMap.fits',
irfs=self.commonConf['irfs'])
except (qU.FileNotFound):
self.logger.critical("One or more needed files do not exist")
sys.exit()
else:
try:
qU.checkForFiles(self.logger, [
self.commonConf['base'] + '_filtered_gti.fits',
self.commonConf['base'] + '_SC.fits',
self.commonConf['base'] + '_expMap.fits',
self.commonConf['base'] + '_ltcube.fits'
])
self.obs = UbAn.UnbinnedObs(
self.commonConf['base'] + '_filtered_gti.fits',
self.commonConf['base'] + '_SC.fits',
expMap=self.commonConf['base'] + '_expMap.fits',
expCube=self.commonConf['base'] + '_ltcube.fits',
irfs=self.commonConf['irfs'])
except (qU.FileNotFound):
self.logger.critical("One or more needed files do not exist")
sys.exit()
self.logger.info(self.ret.subn(', ', str(self.obs))[0])
def _new_log_like(self, event_file):
new_obs = FastUnbinnedObs(event_file, self._orig_log_like.observation)
# Create empty XML to trick UnbinnedAnalysis in not reading up any source.
# We will then add the source that have been already loaded in the original likelihood object.
# This saves a lot because sources like the Galactic template do not need to be reloaded again from disk
with open("__empty_xml.xml", "w+") as f:
f.write('<source_library title="source library"></source_library>')
# Load pyLike
new_like = UnbinnedAnalysis.UnbinnedAnalysis(new_obs, "__empty_xml.xml",
optimizer="MINUIT")
# Now load the sources from the other object
for source_name in self._sources_to_keep:
#print("Adding %s" % source_name)
new_like.addSource(self._orig_log_like.logLike.source(source_name))
return new_like
# Each event must have a separate response precomputed for each diffuse component in the source model. The precomputed responses for Pass 7 (V6) data are for the gll_iem_v05, iso_source_v05.txt, and iso_clean_05.txt diffuse models.
os.system('cp '+filteredLATFile+' '+filteredLATFile_withDiffResps) #Make a copy of the filteredLATFile
my_apps.diffResps['evfile']=filteredLATFile_withDiffResps
my_apps.diffResps['scfile']=spacecraftFile
my_apps.diffResps['srcmdl']=modelFile
my_apps.diffResps['irfs']=irfsType
my_apps.diffResps.run()
# Run the Likelihood Analysis
print colors.OKBLUE+"Performing Liklihood Analysis"+colors.ENDC
import pyLikelihood
from UnbinnedAnalysis import *
obs = UnbinnedObs(filteredLATFile_withDiffResps,spacecraftFile,expMap=expMapFile,expCube=ltCubeFile,irfs=irfsType)
like = UnbinnedAnalysis(obs,modelFile,optimizer=optimizerType)
# Cuts Complete
print colors.OKGREEN+"################ Analysis Complete ################"
print obs
print like
print "###################################################"+colors.ENDC
################################
###### Adjust Source Model #####
################################
like.tol
like.tolType
like.tol = 0.0001
if optimizerType=='Minuit':
# Find expomap
expmaps = glob.glob("%s_expomap.fit*" % root_name)
assert len(expmaps) == 1, "Couldn't find exopmap"
expmap = expmaps[0]
# Find XML model output of gtdolike
xmls = glob.glob("%s_likeRes.xml" % root_name)
assert len(xmls) == 1, "Couldn't find XML"
xml_res = xmls[0]
obs = UnbinnedAnalysis.UnbinnedObs(filteredeventfile, dataset['ft2file'], expMap=expmap, expCube=ltcube)
like = UnbinnedAnalysis.UnbinnedAnalysis(obs, xml_res, 'MINUIT')
ftm = FastTSMap(like)
(bestra, bestdec), maxTS = ftm.search_for_maximum(args.ra, args.dec, float(half_size), int(n_side), verbose=False)
#Now append the results for this interval
grb = filter(lambda x:x.name.find("GRB")>=0,sources)[0]
if args.tsmap_spec is not None:
if maxTS > grb.TS:
print("\n\n=========================================")
print(" Fast TS Map has found a better position")
def runLikelihood(subdir, tpl_file):
'''This runction runs the likelihood code on a set of pixels in a
subdirectory. It takes as input the subdirectory to work on and a
template counts map. It reads it's configuration from a pickle
file (par.pck) that should be located in the subdirectory and the
pixel locations from another pickle file (pixel.pck). It then
creats an overall likelihood object, does a quick global fit and
then loops over the pixels. At each pixel, it creats a test
source, fits that source, calculates the TS of the source and
writes the results to an output file in the subdirectory called
ts_results.dat.'''
parfile = open("par.pck", "r")
pars = pickle.load(parfile)
pixelfile = open("pixel.pck", "r")
pixels = pickle.load(pixelfile)
pixel_coords = PixelCoords(tpl_file)
obs = UnbinnedObs(resolve_fits_files(pars['evfile']),
resolve_fits_files(pars['scfile']),
expMap='../'+pars['expmap'],
expCube='../'+pars['expcube'],
irfs=pars['irfs'])
like = UnbinnedAnalysis(obs, '../'+pars['srcmdl'], pars['optimizer'])
like.setFitTolType(pars['toltype'])
like.optimize(0)
loglike0 = like()
test_src = getPointSource(like)
target_name = 'testSource'
test_src.setName(target_name)
outfile = 'ts_results.dat'
finished_pixels = []
if os.path.isfile(outfile):
input = open(outfile, 'r')
for line in input:
tokens = line.strip().split()
ij = int(tokens[0]), int(tokens[1])
finished_pixels.append(ij)
input.close()
output = open(outfile, 'a')
for indx, i, j in pixels:
if (i, j) in finished_pixels:
continue
ra, dec = pixel_coords(i, j)
test_src.setDir(ra, dec, True, False)
like.addSource(test_src)
like.optimize(0)
ts = -2*(like() - loglike0)
output.write("%3i %3i %.3f %.3f %.5f\n" % (i, j, ra, dec, ts))
output.flush()
like.deleteSource(target_name)
output.close()
def runFermiTools(Name, RA, DEC, minEnergy, maxEnergy, SCFile, radius, binsz,
TSTART, TSTOP, Evfile, bins, zmax, evclass, evtype, TSul,
NMtol, lc_bin_num, runMRM):
print "Working on bin " + str(lc_bin_num) + " for the light curve."
f = FermiObject()
"""
Following steps execute Fermi Tool gtselect
"""
print('\nWorking on file.')
print('Cutting file to fit desired parameters . . .\n')
f._setEvclass(evclass)
f._setEvtype(evtype)
f._setRa(RA)
f._setDec(DEC)
f._setRad(radius)
f._setEmin(minEnergy)
f._setEmax(maxEnergy)
f._setZmax(zmax)
f._setTmin(TSTART)
f._setTmax(TSTOP)
f._setInfile(Evfile)
f._setOutfile(Name + '_gtselect' + str(lc_bin_num) + '_lc.fits')
f.amonSelect()
print(
'File cuts have been made. Now making cuts for GTI using spacecraft file.'
)
"""
Following steps execute Fermi Tool gtmktime
"""
f._setScfile(SCFile)
f._setRoicut('no')
f._setEvfile(Name + '_gtselect' + str(lc_bin_num) + '_lc.fits')
f._setOutfile(Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits')
###############################################
# Filter expression #
Filter = '(DATA_QUAL>0)&&(LAT_CONFIG==1)'
###############################################
f._setFilter(Filter)
print('Working on file ' + str(f.getOutfile()) + '. . .')
f.amonTime()
print('File cuts have been made.')
print('Using XML model from whole dataset.\n Moving on to gtltcube.')
print "Now working on ltcube file using gtltcube\n"
my_apps.expCube['evfile'] = Name + '_gtmktime' + str(
lc_bin_num) + '_lc.fits'
my_apps.expCube['scfile'] = SCFile
my_apps.expCube['outfile'] = Name + '_ltcube' + str(
lc_bin_num) + '_lc.fits'
my_apps.expCube['dcostheta'] = 0.025
my_apps.expCube['binsz'] = 1
my_apps.expCube['phibins'] = 0
my_apps.expCube['zmax'] = zmax
my_apps.expCube['chatter'] = 0
my_apps.expCube.run()
print "\nltcube complete.\nMoving to compute exposure map with gtexpmap.\n"
my_apps.expMap['evfile'] = Name + '_gtmktime' + str(
lc_bin_num) + '_lc.fits'
my_apps.expMap['scfile'] = SCFile
my_apps.expMap['expcube'] = Name + '_ltcube' + str(lc_bin_num) + '_lc.fits'
my_apps.expMap['outfile'] = Name + '_expMap' + str(lc_bin_num) + '_lc.fits'
my_apps.expMap['irfs'] = 'CALDB'
my_apps.expMap['srcrad'] = radius + 10
my_apps.expMap['nlong'] = 4 * (radius + 10)
my_apps.expMap['nlat'] = 4 * (radius + 10)
ebin = int(10 * log10(maxEnergy / minEnergy))
print "There are " + str(ebin) + " energy bans."
my_apps.expMap['nenergies'] = ebin
my_apps.expMap.run()
print "Finnished making exposure map.\n"
print "Calcualting the diffuse response for photons in this bin."
my_apps.diffResps['evfile'] = Name + '_gtmktime' + str(
lc_bin_num) + '_lc.fits'
my_apps.diffResps['scfile'] = SCFile
my_apps.diffResps['srcmdl'] = Name + '_output_model.xml'
my_apps.diffResps['irfs'] = 'CALDB'
my_apps.diffResps.run()
print "Finished calculating diffuse response. Now moving to conduct a UNBINNED likelihood analysis."
obs = UnbinnedObs(Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits',
SCFile,
expMap=Name + '_expMap' + str(lc_bin_num) + '_lc.fits',
expCube=Name + '_ltcube' + str(lc_bin_num) + '_lc.fits',
irfs='P8R2_SOURCE_V6')
analysis = UnbinnedAnalysis(obs,
Name + '_output_model.xml',
optimizer='NewMinuit')
likeObj = pyLike.NewMinuit(analysis.logLike)
analysis.tol = NMtol
LIKE = analysis.fit(verbosity=0, covar=True, optObject=likeObj)
fit = likeObj.getRetCode()
print "Likelihood has converged whith Code " + str(likeObj.getRetCode())
Flux = analysis.flux(Name, emin=minEnergy, emax=maxEnergy)
Ferr = analysis.fluxError(Name, emin=minEnergy, emax=maxEnergy)
MeVtoErg = 1.602e-6
ef = analysis.energyFlux(Name, minEnergy, maxEnergy) * MeVtoErg
ef_err = analysis.energyFluxError(Name, minEnergy, maxEnergy) * MeVtoErg
UL = False
TSUM = TSTART + TSTOP
TMID = TSUM / 2
limit = Flux
if analysis.Ts(Name) < TSul:
UL = True
limit, results = IUL.calc_int(analysis,
Name,
cl=0.90,
emin=minEnergy,
emax=maxEnergy)
#Do second likelihood with constant flux to calculate the TS variability
obsC = UnbinnedObs(Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits',
SCFile,
expMap=Name + '_expMap' + str(lc_bin_num) + '_lc.fits',
expCube=Name + '_ltcube' + str(lc_bin_num) + '_lc.fits',
irfs='P8R2_SOURCE_V6')
analysisC = UnbinnedAnalysis(obsC,
Name + '_var_model.xml',
optimizer='NewMinuit')
likeObjC = pyLike.NewMinuit(analysisC.logLike)
analysisC.tol = NMtol
LIKEC = analysisC.fit(verbosity=0, covar=True, optObject=likeObjC)
#Run gtselect to make smaller data fits file to compute the exposure, set to 3 degrees around source of interest
f._setRad(3)
f._setInfile(Evfile)
f._setOutfile(Name + '_gtselect' + str(lc_bin_num) + '_exposure.fits')
print "Creating file " + Name + "_gtselect_exposure.fits"
f.amonSelect()
#Run gtmaketime on this small region
f._setEvfile(Name + '_gtselect' + str(lc_bin_num) + '_exposure.fits')
f._setOutfile(Name + '_gtmktime' + str(lc_bin_num) + '_exposure.fits')
print('Working on file ' + str(f.getOutfile()))
f.amonTime()
my_apps.evtbin['algorithm'] = 'LC'
my_apps.evtbin['evfile'] = f.getOutfile()
my_apps.evtbin['outfile'] = Name + '_LC' + str(
lc_bin_num) + '_exposure.fits'
my_apps.evtbin['scfile'] = f.getScfile()
my_apps.evtbin['tbinalg'] = 'LIN'
my_apps.evtbin['tstart'] = f.getTmin()
my_apps.evtbin['tstop'] = f.getTmax()
my_apps.evtbin['dtime'] = TSTOP - TSTART
my_apps.evtbin.run()
yes = subprocess.call([
'gtexposure', Name + '_LC' + str(lc_bin_num) + '_exposure.fits',
f.getScfile(), 'P8R2_SOURCE_V6', Name + '_output_model.xml', Name
])
if yes == 0:
print "Exposure map has been created"
else:
print "Subprocessing failed. Unable to create exposure map with gtexposure."
print "Time bin complete."
hdulist = pyfits.open(Name + '_LC' + str(lc_bin_num) + '_exposure.fits')
tbdata = hdulist[1].data
z = tbdata['EXPOSURE']
exp = z[0]
################################################################
# This portion prints to the text file #
################################################################
f = open("lc_output.txt", "a")
f.write(
str(Flux) + ',' + str(Ferr) + ',' + str(ef) + ',' + str(ef_err) + ',' +
str(limit) + ',' + str(analysis.Ts(Name)) + ',' + str(UL) + ',' +
str(TMID) + ',' + str(exp) + ',' + str(LIKE) + ',' + str(LIKEC) + '\n')
f.close()
print "Likelihood analysis on this band is complete."
yes = subprocess.call([
'rm', Name + '_gtselect' + str(lc_bin_num) + '_lc.fits',
Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits',
Name + '_cmap' + str(lc_bin_num) + '_lc.fits',
Name + '_ccube' + str(lc_bin_num) + '_lc.fits',
Name + '_ltcube' + str(lc_bin_num) + '_lc.fits',
Name + '_expMap' + str(lc_bin_num) + '_lc.fits',
Name + '_LC' + str(lc_bin_num) + '_exposure.fits',
Name + '_srcmaps' + str(lc_bin_num) + '_lc.fits',
Name + '_gtselect' + str(lc_bin_num) + '_exposure.fits',
Name + '_gtmktime' + str(lc_bin_num) + '_exposure.fits'
])
if yes == 0:
print 'Files for bin have been deleted'
else:
print "Subprocessing failed. Unable to delete files for bin."
def __init__(
self,
name,
eventFile,
ft2File,
livetimeCube,
kind,
exposureMap=None,
sourceMaps=None,
binnedExpoMap=None,
source_name=None,
):
# Initially the nuisance parameters dict is empty, as we don't know yet
# the likelihood model. They will be updated in set_model
super(FermiLATLike, self).__init__(name, {})
# Read the ROI cut
cc = pyLike.RoiCuts()
cc.readCuts(eventFile, "EVENTS")
self.ra, self.dec, self.rad = cc.roiCone()
# Read the IRF selection
c = pyLike.Cuts(eventFile, "EVENTS")
self.irf = c.CALDB_implied_irfs()
self.ft2File = ft2File
self.livetimeCube = livetimeCube
# These are the boundaries and the number of energies for the computation
# of the model (in keV)
self.emin = 1e4
self.emax = 5e8
self.Nenergies = 200
# This is the limit on the effective area correction factor,
# which is a multiplicative factor in front of the whole model
# to account for inter-calibration issues. By default it can vary
# by 10%. This can be changed by issuing:
# FermiLATUnbinnedLikeInstance.effCorrLimit = [new limit]
# where for example a [new limit] of 0.2 allow for an effective
# area correction up to +/- 20 %
self.effCorrLimit = 0.1
if kind.upper() != "UNBINNED" and kind.upper() != "BINNED":
raise RuntimeError("Accepted values for the kind parameter are: " +
"binned, unbinned. You specified: %s" % (kind))
else:
self.kind = kind.upper()
if kind.upper() == "UNBINNED":
assert exposureMap is not None, "You have to provide an exposure map"
self.eventFile = eventFile
self.exposureMap = exposureMap
# Read the files and generate the pyLikelihood object
self.obs = UnbinnedAnalysis.UnbinnedObs(
self.eventFile,
self.ft2File,
expMap=self.exposureMap,
expCube=self.livetimeCube,
irfs=self.irf,
)
elif kind.upper() == "BINNED":
assert sourceMaps is not None, "You have to provide a source map"
assert (
binnedExpoMap
is not None), "You have to provided a (binned) exposure map"
self.sourceMaps = sourceMaps
self.binnedExpoMap = binnedExpoMap
self.obs = BinnedAnalysis.BinnedObs(
srcMaps=self.sourceMaps,
expCube=self.livetimeCube,
binnedExpMap=self.binnedExpoMap,
irfs=self.irf,
)
pass
# Activate inner minimization by default
self.setInnerMinimization(True)
self._source_name = source_name
class quickLike:
""" This is the base class. A usual likelihood analysis will
consists of running the following functions (assuming you have a
configuration file):
* qL = quickLike('MySource', True)
* qL.makeObs()
* qL.initDRM()
* qL.fitDRM()
* qL.initMIN()
* qL.fitMIN()
This will set up all of the objects needed for the analysis and do an
initial fit with one of the DRM optimizers. It'll save these results
and use them for the second fit with one of the Minuit optimizers.
If you do not have a configuration file, you'll need to input all
of the options for this module when you create the quickLike
object (see the various options below). You can create a
configuration file by executing writeConfig().
* qL.writeConfig()
This module will catch any failures from the optimizers and will
report them to the user. There are a few functions that are useful to
use in this case:"""
def __init__(self,
base = 'MySource',
configFile = False,
likelihoodConfig = {"model" : "MySource_model.xml",
"sourcename" : "Source Name",
"drmtol" : 0.1,
"mintol" : 1e-4},
commonConfig = {"base" : 'MySource',
"eventclass" : 2,
"binned" : False,
"irfs" : "P7SOURCE_V6",
"verbosity" : 0}):
commonConfig['base'] = base
self.logger = initLogger(base, 'quickLike')
if(configFile):
try:
commonConfigRead,analysisConfigRead,likelihoodConfigRead,plotConfigRead = readConfig(self.logger,base)
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
try:
commonConfig = checkConfig(self.logger,commonConfig,commonConfigRead)
except(KeyError):
return
try:
likelihoodConfig = checkConfig(self.logger,likelihoodConfig,likelihoodConfigRead)
except(KeyError):
return
self.commonConf = commonConfig
self.likelihoodConf = likelihoodConfig
self.ret = re.compile('\n')
self.fitbit = False
self.Print()
def writeConfig(self):
"""Writes all of the initialization variables to the config
file called <basename>.cfg"""
writeConfig(quickLogger=self.logger,
commonDictionary=self.commonConf,
likelihoodDictionary=self.likelihoodConf)
def Print(self):
"""Prints out information about the various objects to the
terminal and to the log file."""
logString = "Created quickLike object: "
for variable, value in self.commonConf.iteritems():
logString += variable+"="+str(value)+","
for variable, value in self.likelihoodConf.iteritems():
logString += variable+"="+str(value)+","
self.logger.info(logString)
def makeObs(self):
"""Creates either a binned or unbinned observation object for
use in the likelihood analysis. This function checks for all
of the needed files first. If you do not have a needed file,
see the quickAnalysis module for creation. This function
should be run before any of the init or fit functions."""
if(self.commonConf['binned']):
try:
checkForFiles(self.logger,[self.commonConf['base']+'_srcMaps.fits',
self.commonConf['base']+'_ltcube.fits',
self.commonConf['base']+'_BinnedExpMap.fits'])
self.obs = BinnedObs(srcMaps=self.commonConf['base']+'_srcMaps.fits',
expCube=self.commonConf['base']+'_ltcube.fits',
binnedExpMap=self.commonConf['base']+'_BinnedExpMap.fits',
irfs=self.commonConf['irfs'])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
else:
try:
checkForFiles(self.logger,[self.commonConf['base']+'_filtered_gti.fits',
self.commonConf['base']+'_SC.fits',
self.commonConf['base']+'_expMap.fits',
self.commonConf['base']+'_ltcube.fits'])
self.obs = UnbinnedObs(self.commonConf['base']+'_filtered_gti.fits',
self.commonConf['base']+'_SC.fits',
expMap=self.commonConf['base']+'_expMap.fits',
expCube=self.commonConf['base']+'_ltcube.fits',
irfs=self.commonConf['irfs'])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
self.logger.info(self.ret.subn(', ',str(self.obs))[0])
def initDRM(self):
"""Initializes the DRM optimizer (either binned or unbinned).
This is usually the second function that you run when using
this module. You need to run makeObs before you run this
function. If it hasn't been run, this function will exit."""
try:
self.obs
except AttributeError:
self.logger.critical("Obs object does not exist. Create it first with the makeObs function")
return
try:
checkForFiles(self.logger,[self.likelihoodConf['model']])
if(self.commonConf['binned']):
self.DRM = BinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer="DRMNGB")
else:
self.DRM = UnbinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer="DRMNGB")
self.DRM.tol = float(self.likelihoodConf['drmtol'])
self.logger.info(self.ret.subn(', ',str(self.DRM))[0])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def initAltFit(self,opt="MINUIT"):
"""Initiallizes a minuit optimizer to use as a backup to the
DRM optimizer. This function is used internally in the fitDRM
function so you probably will never use it. You need to run
makeObs before you run this function. If it hasn't been run,
this function will exit."""
try:
self.obs
except AttributeError:
self.logger.critical("Obs object does not exist. Create it first with the makeObs function")
return
try:
checkForFiles(self.logger,[self.likelihoodConf['model']])
if(self.commonConf['binned']):
self.ALTFIT = BinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer=opt)
else:
self.ALTFIT = UnbinnedAnalysis(self.obs,self.likelihoodConf['model'],optimizer=opt)
self.ALTFIT.tol = float(self.likelihoodConf['drmtol'])
self.ALTFITobj = pyLike.Minuit(self.ALTFIT.logLike)
self.logger.info(self.ret.subn(', ',str(self.ALTFIT))[0])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def initMIN(self, useBadFit=False):
"""Initiallizes a New Minuit optimizer to use as a backup to
the DRM optimizer. This is usually run after you have
initially run fitDRM and created a <basename>_likeDRM.xml
model file which is used a seed for the New Minuit optimizer.
You can skip the DRM process if you like but you need to have
the proper model file (<basename>_likeDRM.xml) present in the
working directory. You need to run makeObs before you run this
function. If it hasn't been run, this function will exit. If
you want to use the non convergant fit from fitDRM, set
useBadFit to True."""
try:
self.obs
except AttributeError:
self.logger.critical("Obs object does not exist. Create it first with the makeObs function.")
return
if(useBadFit):
model = self.commonConf['base']+'_badDRMFit.xml'
else:
model = self.commonConf['base']+'_likeDRM.xml'
try:
checkForFiles(self.logger,[model])
if(self.commonConf['binned']):
self.MIN = BinnedAnalysis(self.obs,model,optimizer='NewMinuit')
else:
self.MIN = UnbinnedAnalysis(self.obs,model,optimizer='NewMinuit')
self.MIN.tol = float(self.likelihoodConf['mintol'])
self.MINobj = pyLike.NewMinuit(self.MIN.logLike)
self.logger.info(self.ret.subn(', ',str(self.MIN))[0])
except(FileNotFound):
self.logger.critical("One or more needed files do not exist")
return
def fitDRM(self):
"""Performs a DRM inital fit on your data using the
<basename>_model.xml model file. It tries an intial fit and
if that fails, tries a tighter tolerance. If that fails, it
tries a looser tolerance. If that fails, it tries to do this
initial fit with the MINUIT optimizer. If that fails, this
function bails. If the fit converges, it saves the results to
<basename>_likeDRM.xml which will be used in the NewMinuit
fit. If no fit is found, it will save the results to
<basename>_badDRMFit.xml. You can use this in the NewMinuit fit
if you use the useBadFit option in initMIN. You need to have run
initDRM before you run this function."""
try:
self.DRM
except AttributeError:
self.logger.critical("DRM object does not exist. Create it first with the initDRM function.")
return
altfit=False
try:
self.DRM.fit(verbosity=int(self.commonConf['verbosity']))
except:
self.logger.error("Initial DRM Fit Failed")
try:
self.logger.info("Trying tighter tolerance (DRMtol*0.1)")
self.DRM.tol = float(self.likelihoodConf['drmtol']) * 0.1
self.DRM.fit(verbosity= int(self.commonConf['verbosity']))
except:
self.logger.error("Second DRM Fit Failed")
try:
self.logger.info("Trying looser tolerance (drmtol*10.)")
self.DRM.tol = float(self.likelihoodConf['drmtol']) * 10.
self.DRM.fit(verbosity= int(self.commonConf['verbosity']))
except:
self.logger.error("Third DRM Fit Failed")
try:
self.logger.info("Trying alternate fit algorithm (MINUIT)")
self.initAltFit()
self.ALTFIT.fit(verbosity=int(self.commonConf['verbosity']),covar=True,optObject=self.ALTFITobj)
print self.ALTFITobj.getQuality()
altfit = True
except:
self.logger.error("Alternative fit algorithm failed, bailing")
self.logger.error(self.decodeRetCode('Minuit',self.ALTFITobj.getRetCode()))
self.ALTFIT.logLike.writeXml(self.commonConf['base']+'_badDRMFit.xml')
self.logger.info("Saved ALTFIT as "+self.commonConf['base']+"_badDRMFit.xml")
return
if(altfit):
self.logger.info("ALTFIT Fit Finished. Total TS: "+str(self.ALTFIT.logLike.value()))
self.ALTFIT.logLike.writeXml(self.commonConf['base']+'_likeDRM.xml')
self.logger.info("Saved ALTFIT as "+self.commonConf['base']+"_likeDRM.xml")
else:
self.DRM.logLike.writeXml(self.commonConf['base']+'_likeDRM.xml')
self.logger.info("DRM Fit Finished. Total TS: "+str(self.DRM.logLike.value()))
self.logger.info("Saved DRM as "+self.commonConf['base']+"_likeDRM.xml")
def fitMIN(self):
"""Does a New Minuit fit on your data based on the model
output by the fitDRM function. You need to have run initMIN
before running this function. Saves the results to
<basename>_likeMIN.xml if there is convergence. If
convergence is not found, saves the results to
<basename>_badMINFit.xml."""
try:
self.MIN
except AttributeError:
self.logger.critical("MIN object does not exist. Create it first with the initMIN function.")
return
self.MIN.fit(covar=True, optObject=self.MINobj,verbosity=int(self.commonConf['verbosity']))
self.MIN.logLike.writeXml(self.commonConf['base']+'_likeMinuit.xml')
self.logger.info("NEWMINUIT Fit Finished. Total TS: "+str(self.MIN.logLike.value()))
self.logger.info("NEWMINUIT Fit Status: "+str(self.MINobj.getRetCode()))
self.logger.info("NEWMINUIT fit Distance: "+str(self.MINobj.getDistance()))
self.fitbit = True
if(self.MINobj.getRetCode() > 0):
self.logger.error("NEWMINUIT DID NOT CONVERGE!!!")
self.logger.error("The fit failed the following tests: "+self.decodeRetCode('NewMinuit',self.MINobj.getRetCode()))
self.MIN.fit(covar=True, optObject=self.MINobj,verbosity=int(self.commonConf['verbosity']))
self.MIN.logLike.writeXml(self.commonConf['base']+'_badMINFit.xml')
def printSource(self,source,Emin=100,Emax=300000):
"""Prints various details for a source in your model."""
try:
self.MIN
except AttributeError:
self.logger.critical("MIN object does not exist. "+\
"Create it first with the initMIN function and then fit it with the fitMIN function.")
return
if(not self.fitbit):
self.logger.warn("Fit isn't current, these values might not be correct. Fun fitMIN first.")
logString = source
TS = self.MIN.Ts(source)
print "TS: ",TS
logString += " TS: " + str(TS)
NPred = self.MIN.NpredValue(source)
print "Npred: ",NPred
logString += " NPred: " + str(NPred)
flux = self.MIN.flux(source,emin=Emin,emax=Emax)
print "Flux: ",flux
logString += "Flux: "+str(flux)
if(self.fitbit):
fluxErr = self.MIN.fluxError(source,emin=Emin,emax=Emax)
print "Flux Error: ",fluxErr
logString += "Flux Error: "+str(fluxErr)
for paramName in self.MIN.model[source].funcs['Spectrum'].paramNames:
paramValue = self.MIN.model[source].funcs['Spectrum'].getParam(paramName).value()
print paramName,": ",paramValue
logString += paramName + ": " + str(paramValue) + " "
self.logger.info(logString)
def customERange(self,Emin,Emax):
"""Sets a smaller energy range for the fitting of both the DRM
and MIN optimization steps."""
try:
self.DRM
except AttributeError:
self.logger.warn("DRM object doesn't exist. Energy range not modified.")
else:
self.DRM.setEnergyRange(Emin,Emax)
self.logger.info("Set energy range for DRM to "+str(self.DRM.emin)+","+str(self.DRM.emax))
try:
self.MIN
except AttributeError:
self.logger.warn("MIN object doesn't exist. Energy range not modified.")
else:
self.MIN.setEnergyRange(Emin,Emax)
self.logger.info("Set energy range for MIN to "+str(self.MIN.emin)+","+str(self.MIN.emax))
def calcUpper(self,source,Emin=100,Emax=300000):
"""Calculates an upper limit for a source in your model."""
self.ul = UpperLimits(self.MIN)
self.ul[source].compute(emin=Emin,emax=Emax)
print self.ul[source].results
self.logger.info(source+" UL: "+str(self.ul[source].results[0]))
def removeWeak(self,mySource = '',tslimit=0,distlimit=0,RemoveFree=False,RemoveFixed=False):
"""This function has two main uses: it will print out details
on all of the sources in your model and it will remove sources
according to different requirements. If you just want to
print out details, execute it this way:
<obj>.removeWeak(<my_source>)
Where <obj> is the quickLike object you're using here and
<my_source> is the name of your source of interest. You can
then remove some of these sources from the model if you like.
For example, if you want to remove all of the fixed sources
with TS values less than 1, execute it this way:
<obj>.removeWeak(<my_source>,tslimit=1,RemoveFixed=True)
You can mix and match any of the options. You could remove
all sources (fixed and free) that are below a TS value of 3
and are 10 degrees from your source of interest by executing:
<obj>.removeWeak(<my_source>,tslimit=3,distlimit=10,RemoveFree=True,RemoveFixed=True)"""
try:
self.MIN
except AttributeError:
self.logger.critical("MIN object does not exist. "+\
"Create it first with the initMIN function and then fit it with the fitMIN function.")
return
if(not self.fitbit):
self.logger.warn("Fit isn't current, these values might not be correct. Run fitMIN first.")
if(mySource == ''):
mySource = self.likelihoodConf['sourcename']
for name in self.MIN.sourceNames():
remove = False
distance = 0
sourceTS = self.MIN.Ts(name)
if(self.MIN.model[name].src.getType() == 'Point'):
distance = self.MIN._separation(self.MIN.model[mySource].src,self.MIN.model[name].src)
if(self.MIN.freePars(name).size() > 0):
indexFree = "Free"
if( (sourceTS < tslimit) and (distance > distlimit) and RemoveFree ):
remove = True
else:
indexFree = "Fixed"
if( (sourceTS < tslimit) and (distance > distlimit) and RemoveFixed ):
remove = True
if( remove ):
self.logger.info("Removing "+name+", TS: "+str(sourceTS)+", Frozen?: "+str(indexFree)+", Distance: "+str(distance))
self.MIN.deleteSource(name)
else:
self.logger.info("Retaining "+name+", TS: "+str(sourceTS)+", Frozen?: "+str(indexFree)+", Distance: "+str(distance))
def paramsAtLimit(self, limit = 0.1):
"""This function will print out any sources whoes parameters
are close to their limits. You could use this to find sources
that are having issues being fit. This function is useful
when you're having trouble getting convergence from the New
Minuit fit routine. The limit is in percentage difference of a
bound. If one of the bounds is zero it uses the value of the
parameter to check for closeness (absolute instead of percent
differenct). The default is 0.1 (1%) difference for a measure
of closeness."""
try:
self.MIN
except AttributeError:
self.logger.critical("MIN object does not exist. "+\
"Create it first with the initMIN function and then fit it with the fitMIN function.")
return
if(not self.fitbit):
self.logger.warn("Fit isn't current, these values might not be correct. Run fitMIN first.")
for src in self.MIN.sourceNames():
for name in self.MIN.model[src].funcs['Spectrum'].paramNames:
bounds = self.MIN.model[src].funcs['Spectrum'].getParam(name).getBounds()
value = self.MIN.model[src].funcs['Spectrum'].getParam(name).value()
try:
distToLower = abs((value - bounds[0])/bounds[0])
except ZeroDivisionError:
distToLower = abs(value)
try:
distToUpper = abs((value - bounds[1])/bounds[1])
except ZeroDivisionError:
distToUpper = abs(value)
if( distToLower < limit ):
self.logger.error("The "+name+" ("+str(value)+") of "+src+" is close ("\
+str(distToLower)+") to its lower limit ("+str(bounds[0])+")")
if( distToUpper < limit):
self.logger.error("The "+name+" ("+str(value)+") of "+src+" is close ("\
+str(distToUpper)+") to its upper limit ("+str(bounds[1])+")")
def decodeRetCode(self, optimizer, retCode):
"""Decodes the return codes from the Minuit and New Minuit fit
functions. Used in the fitting functions in this module.
You'll probably never use this function."""
if(optimizer == 'NewMinuit'):
retCode -= 100
failure = ""
if(retCode & 1):
failure += " IsAboveMaxEdm"
if(retCode & 2):
failure += " HasCovariance"
if(retCode & 4):
failure += " HesseFailed"
if(retCode & 8):
failure += " HasMadePosDefCovar"
if(retCode & 16):
failure += " HasPosDefCovar"
if(retCode & 32):
failure += " HasAccurateCovar"
if(retCode & 64):
failure += " HasValidCovariance"
if(retCode & 128):
failure += " HasValidParameters"
if(retCode & 256):
failure += " IsValid"
return failure
if(optimizer == 'Minuit'):
if(retCode == 0):
failure = "Error matrix not calculated at all"
if(retCode == 1):
failure = "Diagonal approximation only, not accurate"
if(retCode == 2):
failure = "Full matrix, but forced positive-definite (i.e. not accurate)"
if(retCode == 3):
failure = "Full accurate covariance matrix (After MIGRAD, this is the indication of normal convergence.)"
return failure
def main(NAME, RA, DEC, TSTART, TSTOP, EMIN, EMAX, SC, ROIu, xml):
ROIue = float(ROIu) + 10
os.system('ls -1 *PH*.fits > %s_events.list' % (NAME))
my_apps.filter['evclass'] = 128
my_apps.filter['evtype'] = 3
my_apps.filter['ra'] = RA
my_apps.filter['dec'] = DEC
my_apps.filter['rad'] = ROIu
my_apps.filter['emin'] = EMIN
my_apps.filter['emax'] = EMAX
my_apps.filter['zmax'] = 90
my_apps.filter['tmin'] = TSTART
my_apps.filter['tmax'] = TSTOP
my_apps.filter['infile'] = '@%s_events.list' % (NAME)
my_apps.filter['outfile'] = '%s_filtered.fits' % (NAME)
my_apps.filter.run()
# maketime
my_apps.maketime['scfile'] = SC
my_apps.maketime['filter'] = '(DATA_QUAL>0)&&(LAT_CONFIG==1)'
my_apps.maketime['roicut'] = 'no'
my_apps.maketime['evfile'] = '%s_filtered.fits' % (NAME)
my_apps.maketime['outfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.maketime.run()
#
my_apps.counts_map['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.counts_map['scfile'] = SC
my_apps.counts_map['outfile'] = '%s_CountMap.fits' % (NAME)
# my_apps.counts_map.run()
#
my_apps.expCube['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.expCube['scfile'] = SC
my_apps.expCube['zmax'] = 90
my_apps.expCube['outfile'] = 'expCube.fits'
my_apps.expCube['dcostheta'] = 0.025
my_apps.expCube['binsz'] = 1
my_apps.expCube.run()
my_apps.expMap['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.expMap['scfile'] = SC
my_apps.expMap['expcube'] = 'expCube.fits'
my_apps.expMap['outfile'] = 'expMap.fits'
my_apps.expMap['irfs'] = 'CALDB'
my_apps.expMap['srcrad'] = ROIue
my_apps.expMap['nlong'] = 120
my_apps.expMap['nlat'] = 120
my_apps.expMap['nenergies'] = 20
my_apps.expMap.run()
# sara xml model
roiname = '%s_filtered_gti.fits' % NAME
if float(xml) == 0:
xml_creator_P8_v1.main(NAME, float(RA), float(DEC), float(EMIN), float(EMAX), 15)
xmlmodelname = '%s_model.xml' % NAME
my_apps.diffResps['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.diffResps['scfile'] = SC
my_apps.diffResps['srcmdl'] = xmlmodelname
my_apps.diffResps['irfs'] = 'CALDB'
my_apps.diffResps.run()
xmlfitname = '%s_fit1.xml' % NAME
obs = UnbinnedObs(roiname, SC, expMap='expMap.fits', expCube='expCube.fits', irfs='CALDB')
# like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='MINUIT')
like1 = UnbinnedAnalysis(obs, xmlmodelname, optimizer='NewMinuit')
likeobj = pyLike.NewMinuit(like1.logLike)
like1.fit(verbosity=0, optObject=likeobj)
print likeobj.getRetCode()
sourceDetails = {}
for source in like1.sourceNames():
sourceDetails[source] = like1.Ts(source)
for source, TS in sourceDetails.iteritems():
if (TS < 2):
print "Deleting...", source, " TS = ", TS
like1.deleteSource(source)
like1.fit(verbosity=0, optObject=likeobj)
print "0 is converged", likeobj.getRetCode()
like1.logLike.writeXml(xmlfitname)
numl = search(NAME, xmlfitname)
numlg = str(numl + 3)
os.system("sed '" + numlg + "," + numlg + " s/free=\"1\"/free=\"0\"/' " + xmlfitname + " > xml_sed.xml ")
inputs = likeInput(like1, NAME, model="xml_sed.xml", nbins=6, phCorr=1.0)
inputs.plotBins()
inputs.fullFit(CoVar=True)
sed = likeSED(inputs)
sed.getECent()
sed.fitBands()
sed.Plot()
result = like1.model[NAME]
TS = like1.Ts(NAME)
flux = like1.flux(NAME, emin=100)
gamma = like1.model[NAME].funcs['Spectrum'].getParam('Index').value()
cov_gg = like1.model[NAME].funcs['Spectrum'].getParam('Index').error()
# cov_II = like1.model[NAME].funcs['Spectrum'].getParam('Integral').error()
flux_err = like1.fluxError(NAME, emin=100)
like1.plot()
fitsedname = '%s_6bins_likeSEDout.fits' % NAME
sedtool(fitsedname)
print NAME, " TS=", TS
print result
if float(xml) == 1:
xmlmodelname = '%s_model.xml' % NAME
xmlfitname = '%s_fit1.xml' % NAME
obs = UnbinnedObs(roiname, SC, expMap='expMap.fits', expCube='expCube.fits', irfs='CALDB')
# like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='MINUIT')
like1 = UnbinnedAnalysis(obs, xmlmodelname, optimizer='NewMinuit')
likeobj = pyLike.NewMinuit(like1.logLike)
like1.fit(verbosity=0, optObject=likeobj)
print likeobj.getRetCode()
sourceDetails = {}
for source in like1.sourceNames():
sourceDetails[source] = like1.Ts(source)
for source, TS in sourceDetails.iteritems():
if (TS < 2):
print "Deleting...", source, " TS = ", TS
like1.deleteSource(source)
like1.fit(verbosity=0, optObject=likeobj)
print "0 is converged", likeobj.getRetCode()
like1.logLike.writeXml(xmlfitname)
numl = search(NAME, xmlfitname)
numlg = str(numl + 3)
os.system("sed '" + numlg + "," + numlg + " s/free=\"1\"/free=\"0\"/' " + xmlfitname + " > xml_sed.xml ")
inputs = likeInput(like1, NAME, model="xml_sed.xml", nbins=6, phCorr=1.0)
inputs.plotBins()
inputs.fullFit(CoVar=True)
sed = likeSED(inputs)
sed.getECent()
sed.fitBands()
sed.Plot()
result = like1.model[NAME]
TS = like1.Ts(NAME)
flux = like1.flux(NAME, emin=100)
gamma = like1.model[NAME].funcs['Spectrum'].getParam('Index').value()
cov_gg = like1.model[NAME].funcs['Spectrum'].getParam('Index').error()
# cov_II = like1.model[NAME].funcs['Spectrum'].getParam('Integral').error()
flux_err = like1.fluxError(NAME, emin=100)
like1.plot()
fitsedname = '%s_6bins_likeSEDout.fits' % NAME
sedtool(fitsedname)
print NAME, " TS=", TS
print result
def bayesian_ul(**kwargs):
# Instance the unbinned analysis
print("Instancing pyLikelihood...")
unbinned_observation = UnbinnedAnalysis.UnbinnedObs(
kwargs['ft1'], kwargs['ft2'], kwargs['expomap'], kwargs['ltcube'],
'CALDB')
pylike_instance = UnbinnedAnalysis.UnbinnedAnalysis(
unbinned_observation, kwargs['xml'], kwargs['engine'])
print("done")
# Let's start by computing the semi-Bayesian UL from the Science Tools
print("Semi-bayesian upper limit computation with ST...")
# Sync and fit
pylike_instance.syncSrcParams()
pylike_instance.fit()
# Compute ST upper limit
ul = UpperLimits.UpperLimit(pylike_instance, kwargs['src'])
try:
st_bayes_ul, parameter_value = ul.bayesianUL(0.95,
emin=kwargs['emin'],
emax=kwargs['emax'])
except:
# This fails sometimes with RuntimeError: Attempt to set parameter value outside bounds.
print("\n\nWARNING: upper limit computation with ST has failed! \n\n")
st_bayes_ul = -1
st_bayes_ul_ene = -1
# Get back to a good state
pylike_instance = UnbinnedAnalysis.UnbinnedAnalysis(
unbinned_observation, kwargs['xml'], kwargs['engine'])
pylike_instance.fit()
else:
# Convert to energy flux
best_fit_photon_index = pylike_instance[
kwargs['src']].src.spectrum().parameter('Index').getValue()
st_bayes_ul_ene = st_bayes_ul * get_conversion_factor(
best_fit_photon_index, kwargs)
print("done")
# Now find out our free parameters, and define a prior for them
# Prepare the dictionary of parameters. Note that by default they get a uniform prior
# between the current min and max values
free_parameters = collections.OrderedDict()
for p in pylike_instance.model.params:
if p.isFree():
source_name = p.srcName
parameter_name = p.parameter.getName()
p.parameter.setScale(1.0)
free_parameters[(source_name, parameter_name)] = MyParameter(p)
# Now set the priors and the boundaries
# Update boundaries (they will be propagated to the prior as well)
# Isotropic template
if (kwargs['iso'], 'Normalization') in free_parameters:
try:
free_parameters[(kwargs['iso'], 'Normalization')].bounds = (0, 100)
except:
# This happens if the best fit value is outside those boundaries
free_parameters[(kwargs['iso'], 'Normalization')].value = 1.0
free_parameters[(kwargs['iso'], 'Normalization')].bounds = (0, 100)
else:
print("WARNING: Isotropic template is not free to vary (or absent)")
# Galactic template (Truncated Gaussian with systematic error)
if (kwargs['gal'], 'Value') in free_parameters:
try:
free_parameters[(kwargs['gal'], 'Value')].bounds = (0.1, 10.0)
free_parameters[(kwargs['gal'],
'Value')].prior = TruncatedGaussianPrior(
1.0, kwargs['gal_sys_err'])
except:
# This happens if the best fit value is outside those boundaries
free_parameters[(kwargs['gal'], 'Value')].value = 1.0
free_parameters[(kwargs['gal'], 'Value')].bounds = (0.1, 10.0)
else:
print("WARNING: Galactic template is not free to vary (or absent)")
# Photon flux (uniform prior)
if (kwargs['src'], 'Integral') in free_parameters:
try:
free_parameters[(kwargs['src'], 'Integral')].bounds = (0, 10)
except:
free_parameters[(kwargs['src'], 'Integral')].value = 1e-7
free_parameters[(kwargs['src'], 'Integral')].bounds = (0, 10)
else:
raise RuntimeError(
"The Integral parameter must be a free parameter of source %s" %
kwargs['src'])
# Photon index
if (kwargs['src'], 'Index') in free_parameters:
try:
free_parameters[(kwargs['src'],
'Index')].bounds = (kwargs['min_index'],
kwargs['max_index'])
except:
raise RuntimeError(
"It looks like the best fit photon index is outside the boundaries "
"provided in the command line")
else:
raise RuntimeError(
"The Index parameter must be a free parameter of source %s" %
kwargs['src'])
# Execute a fit to get to a good state with the new boundaries
pylike_instance.fit()
# Print the configuration
print("\nFree parameters:")
print("----------------\n")
for k, v in free_parameters.iteritems():
print("* %s of %s (%s)" % (k[1], k[0], v.prior.name))
print("")
# Generate the randomized starting points for the Emcee sampler
ndim, nwalkers = len(free_parameters), kwargs['n_walkers']
p0 = [
map(lambda p: p.get_random_init(0.1), free_parameters.values())
for i in range(nwalkers)
]
# Instance the sampler
posterior = Posterior(free_parameters.values(), pylike_instance)
# Now check that the starting points we have are good (otherwise the sampler will go awry)
for pp in p0:
this_ln = posterior.lnprob(pp)
if not np.isfinite(this_ln):
raise RuntimeError(
"Infinite for values %s while setting up walkers" % pp)
sampler = emcee.EnsembleSampler(nwalkers, ndim, posterior.lnprob)
print("Burn in...")
pos, prob, state = sampler.run_mcmc(p0, kwargs['burn_in'])
print("done")
sampler.reset()
print("Sampling...")
samples = sampler.run_mcmc(pos, kwargs['n_samples'])
print("done")
print("Mean acceptance fraction: {0:.3f}".format(
np.mean(sampler.acceptance_fraction)))
# Make the corner plot
samples = sampler.flatchain
labels = map(lambda x: "%s" % (x[1]), free_parameters.keys())
print("Producing corner plot...")
fig = corner.corner(samples,
show_titles=True,
quantiles=[0.5, 0.50, 0.95],
title_fmt=u'.2g',
labels=labels,
plot_contours=True,
plot_density=False)
fig.tight_layout()
fig.savefig(kwargs['corner_plot'])
print("done")
# Now compute the upper limits
# Find index of normalization
norm_index = free_parameters.keys().index((kwargs['src'], 'Integral'))
# Find index of photon index
ph_index_index = free_parameters.keys().index((kwargs['src'], 'Index'))
photon_fluxes = np.zeros(samples.shape[0])
energy_fluxes = np.zeros(samples.shape[0])
conversion_factors = np.zeros(samples.shape[0])
for i, current_sample in enumerate(samples):
# Set the Integral parameter to the current value
free_parameters[(kwargs['src'],
'Integral')].scaled_value = current_sample[norm_index]
# Set the photon index to the current value
current_photon_index = current_sample[ph_index_index]
free_parameters[(kwargs['src'],
'Index')].scaled_value = current_photon_index
pylike_instance.syncSrcParams()
# Get photon flux for this sample
photon_flux = pylike_instance[kwargs['src']].flux(
kwargs['emin'], kwargs['emax'])
# Get energy flux for this value
conv = get_conversion_factor(current_photon_index, kwargs)
energy_flux = photon_flux * conv
# Save the results
photon_fluxes[i] = photon_flux
energy_fluxes[i] = energy_flux
conversion_factors[i] = conv
# Now compute the 95 percentile
photon_flux_p95 = np.percentile(photon_fluxes, 95)
energy_flux_p95 = np.percentile(energy_fluxes, 95)
# Save the samples
np.savez(kwargs['output_file'] + "_samples", samples=samples)
np.savez(kwargs['output_file'],
photon_fluxes=photon_fluxes,
energy_fluxes=energy_fluxes,
photon_flux_p95=photon_flux_p95,
energy_flux_p95=energy_flux_p95,
st_bayes_ul=st_bayes_ul,
st_bayes_ul_ene=st_bayes_ul_ene)
# Now summarize the results
print("\nUpper limit computation results:")
print("----------------------------------\n")
print("Photon flux:\n")
print(" * Semi-bayes from ST : %g" % (st_bayes_ul))
print(" * Bayesian : %g" % photon_flux_p95)
print("\nEnergy flux:\n")
print(" * Semi-bayes from ST : %g" % st_bayes_ul_ene)
print(" * Bayesian : %g" % energy_flux_p95)
if not irfsType == 'CALDB':
my_apps.diffResps['evfile'] = filteredLATFile
my_apps.diffResps['scfile'] = spacecraftFile
my_apps.diffResps['srcmdl'] = modelFile
my_apps.diffResps['irfs'] = irfsType
my_apps.diffResps.run()
# Run the Likelihood Analysis
import pyLikelihood
from UnbinnedAnalysis import *
obs = UnbinnedObs(filteredLATFile,
spacecraftFile,
expMap=expMapFile,
expCube=ltCubeFile,
irfs=irfsType)
like = UnbinnedAnalysis(obs, modelFile, optimizer='Minuit')
# Analysis Complete
print "################ Analysis Complete ################"
print obs
print like
print "###################################################"
# Some plots!
like.tol
like.tolType
like.tol = 0.0001
likeobj = pyLike.Minuit(like.logLike)
like.fit(verbosity=0, covar=True,
optObject=likeobj) # Warning: This takes VERY long ~ 30 minutes
def main(NAME,RA,DEC,TSTART,TSTOP,EMIN,EMAX,Np, path, ROIu):
#outdir = os.environ["FERMI_TMPLATAREA"]
gtliketxt=open("%s/%s_gtlike.txt"%(path,Np),'w')
gtsedtxt=open("%s/%s_sed.txt"%(path,Np),'w')
SCC='%s_SC00.fits'%(Np)
SC=path+SCC
Npp=path+Np
print SC
ROIue=float(ROIu)+10
os.system("ls -1 '"+Npp+"'_PH*.fits > %s/%s_events.list" %(path,Np))
# os.system('ls -1 'Np'+'PH*.fits > %s/%s_events.list' %(path,Np)
my_apps.filter['evclass'] = 128
my_apps.filter['evtype'] = 3
# my_apps.filter['evclsmin'] = 3
# my_apps.filter['evclsmax'] = 4
my_apps.filter['ra'] = RA
my_apps.filter['dec'] = DEC
my_apps.filter['rad'] = ROIu
my_apps.filter['emin'] = EMIN
my_apps.filter['emax'] = EMAX
my_apps.filter['zmax'] = 90
my_apps.filter['tmin'] = TSTART
my_apps.filter['tmax'] = TSTOP
my_apps.filter['infile'] = '@%s/%s_events.list' %(path,Np)
my_apps.filter['outfile'] = '%s/%s_filtered.fits'%(path,Np)
my_apps.filter.run()
# maketime
my_apps.maketime['scfile'] = SC
my_apps.maketime['filter'] = '(DATA_QUAL>0)&&(LAT_CONFIG==1)'
my_apps.maketime['roicut'] = 'no'
my_apps.maketime['evfile'] = '%s/%s_filtered.fits' %(path,Np)
my_apps.maketime['outfile'] = '%s/%s_filtered_gti.fits' %(path,Np)
my_apps.maketime.run()
#
# my_apps.counts_map['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
# my_apps.counts_map['scfile'] = SC
# my_apps.counts_map['outfile'] = '%s/%s_CountMap.fits'%(path,Np)
# my_apps.counts_map.run()
#
my_apps.expCube['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
my_apps.expCube['scfile'] = SC
my_apps.expCube['zmax'] = 90
my_apps.expCube['outfile'] = '%s/%s_expCube.fits' %(path,Np)
my_apps.expCube['dcostheta'] = 0.025
my_apps.expCube['binsz'] = 1
my_apps.expCube.run()
my_apps.expMap['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
my_apps.expMap['scfile'] = SC
my_apps.expMap['expcube'] ='%s/%s_expCube.fits' %(path,Np)
my_apps.expMap['outfile'] ='%s/%s_expMap.fits' %(path,Np)
# my_apps.expMap['irfs'] ='P7REP_SOURCE_V15'
my_apps.expMap['irfs'] ='CALDB'
my_apps.expMap['srcrad'] = ROIue
my_apps.expMap['nlong'] =120
my_apps.expMap['nlat'] =120
my_apps.expMap['nenergies'] =20
my_apps.expMap.run()
#sara xml model
roiname='%s/%s_filtered_gti.fits' %(path,Np)
xml_creator_P7_v1.main(path,NAME,float(RA),float(DEC),float(EMIN), float(EMAX), 20,Np)
xmlmodelname='%s/%s_model.xml' %(path,Np)
my_apps.diffResps['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
my_apps.diffResps['scfile'] = SC
my_apps.diffResps['srcmdl'] = xmlmodelname
my_apps.diffResps['irfs'] = 'CALDB'
my_apps.diffResps.run()
xmlfitname='%s/%s_fit1.xml' %(path,Np)
expMapFile='%s/%s_expMap.fits' %(path,Np)
expCubeFile='%s/%s_expCube.fits' %(path,Np)
obs = UnbinnedObs(roiname,SC ,expMap=expMapFile,expCube=expCubeFile,irfs='CALDB')
like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='NewMinuit')
like1.fit(verbosity=0)
like1.logLike.writeXml(xmlfitname)
# numl=search(NAME,xmlfitname)
# numlg=str(numl+3)
# os.system("sed '"+numlg+","+numlg+" s/free=\"1\"/free=\"0\"/' "+xmlfitname+ " > xml_sed.xml ")
# inputs=likeInput(like1,NAME,model="xml_sed.xml",nbins=9,phCorr=1.0)
#low_edges = [200.,914.61,1955.87,8944.27,19127.05,40902.61]
#high_edges = [427.69,1955.87,8944.27,19127.05,40902.61,187049.69]
#centers = [0.2767, 1.265, 5.787, 12.37, 26.46, 86.60]
#inputs.customBins(low_edges,high_edges)
# inputs.plotBins()
# inputs.fullFit(CoVar=True)
# sed = likeSED(inputs)
# sed.getECent()
# sed.fitBands()
# sed.Plot()
result=like1.model[NAME]
TS=like1.Ts(NAME)
# I = like1.model[NAME].funcs['Spectrum'].getParam('Integral').value()
flux = like1.flux(NAME,emin=100)
# flux=I*1e-9
gamma = like1.model[NAME].funcs['Spectrum'].getParam('Index').value()
cov_gg =like1.model[NAME].funcs['Spectrum'].getParam('Index').error()
# cov_II = like1.model[NAME].funcs['Spectrum'].getParam('Integral').error()
flux_err = like1.fluxError(NAME,emin=100)
# flux_err=cov_II*1e-9
e=1000.0
a=1
b=1.e-18
lenergy_bin=log10(double(EMIN))+(log10(double(EMAX))-log10(double(EMIN)))/2
energy_bin=pow(10,lenergy_bin)
freq=2.42e22*energy_bin/100.0
ums = 1.-gamma
conv=ums*pow(energy_bin,(-gamma))/(pow(double(EMAX),ums)-pow(double(EMIN),ums))*6.62e-2*(energy_bin/100.0)
# conv is in Jy
# now convert in nufnu erg/cm2/s
convjy=conv*freq*1.e-23
nufnu=flux*convjy
b=flux_err*convjy
err_log=log10((nufnu+b)/nufnu)
#cout<<freq<<" "<<a<<" "<<nufnu<<" "<<b<<endl;
#cout<<log10(freq)<<" "<<log10(a)<<" "<<log10(nufnu)<<" "<<err_log<<endl;
date_start=computeDate(float(TSTART))
date_stop=computeDate(float(TSTOP))
# like1.plot()
# fitsedname='%s_9bins_likeSEDout.fits' %NAME
# sedtool(fitsedname)
print NAME, " TS=", TS
# print result
# print like1.model
print "spectral index= ", gamma, " +/-", cov_gg
print " Flux=", flux, "+/-", flux_err
print "freq", freq, " nuFnu=", nufnu, b,
# print "'UL': ", results_ul, err
gtliketxt.write(NAME)
gtliketxt.write(" RA=")
gtliketxt.write(RA)
gtliketxt.write(" DEC= ")
gtliketxt.write(DEC)
gtliketxt.write(" TS= ")
gtliketxt.write(str(TS))
gtliketxt.write("\n")
gtliketxt.write(" Time Interval (MJD) ")
gtliketxt.write(str(date_start))
gtliketxt.write(" ")
gtliketxt.write(str(date_stop))
gtliketxt.write("\n ")
gtliketxt.write("Flux ")
if TS <25:
obs = UnbinnedObs(roiname,SC ,expMap=expMapFile,expCube=expCubeFile,irfs='CALDB')
like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='NewMinuit')
like1.fit(verbosity=0)
ul=UpperLimits(like1)
UL=ul[NAME].compute(emin=double(EMIN),emax=double(EMAX))
results_ul=UL[1]*1E-9
err=0
print "'UL': ", results_ul, err
gamma_ul=2.0
ums_ul = 1.-gamma_ul
conv_ul=ums_ul*pow(energy_bin,(-gamma_ul))/(pow(double(EMAX),ums_ul)-pow(double(EMIN),ums_ul))*6.62e-2*(energy_bin/100.0)
convjy_ul=conv_ul*freq*1.e-23
nufnu_ul=results_ul*convjy_ul
b=err*convjy_ul
print "freq", freq, "0 nuFnu=", nufnu_ul, b,
gtliketxt.write(str(results_ul))
gtliketxt.write(" 0 ")
#gtliketxt.write(err)
gtsedtxt.write(str(freq))
gtsedtxt.write(" | 0 ")
gtsedtxt.write(" | ")
gtsedtxt.write(str(nufnu_ul))
gtsedtxt.write(" | ")
gtsedtxt.write(str(b))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_start))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_stop))
gtsedtxt.write(" | ")
gtsedtxt.write(" UL ")
gtsedtxt.write(" | ")
else:
gtliketxt.write(str(flux))
gtliketxt.write(" ")
gtliketxt.write(str(flux_err))
gtliketxt.write("\n")
gtliketxt.write("Spectral Index = ")
gtliketxt.write(str(gamma))
gtliketxt.write(" ")
gtliketxt.write(str(cov_gg))
gtsedtxt.write(" ")
gtsedtxt.write(str(freq))
gtsedtxt.write(" | 0 ")
gtsedtxt.write(" | ")
gtsedtxt.write(str(nufnu))
gtsedtxt.write(" | ")
gtsedtxt.write(str(b))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_start))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_stop))
gtsedtxt.write(" | ")
expMap['scfile'] = str(sc_file)
expMap['expcube'] = str(name)+'ltcube'+str(this_bin)+'.fits'
expMap['outfile'] = str(name)+'expmap'+str(this_bin)+'.fits'
expMap['irfs'] ='P7SOURCE_V6'
expMap['srcrad'] = radius + 10
expMap['nlong'] =120
expMap['nlat'] =120
expMap['nenergies'] = 20
expMap['debug'] = 'yes'
expMap.run()
print '\n***Running likelihood analysis***'
try:
obs = UnbinnedObs(str(name)+'mktime'+str(this_bin)+'.fits',str(sc_file),expMap=str(name)+'expmap'+str(this_bin)+'.fits',expCube=str(name)+'ltcube'+str(this_bin)+'.fits',irfs='P7SOURCE_V6')
like1 = UnbinnedAnalysis(obs,str(xml_file),optimizer='DRMNFB')
like1.fit(verbosity=0)
like1.logLike.writeXml(str(name)+'fit'+str(this_bin)+'.xml')
like2 = UnbinnedAnalysis(obs,srcModel=str(name)+'fit'+str(this_bin)+'.xml',optimizer='NewMinuit')
obj = pyLike.Minuit(like2.logLike)
like2.fit(verbosity=0, covar=True, optObject=obj)
like2.plot()
try:
flux = like2.model['_2FGLJ'+str(cat_name)].funcs['Spectrum'].getParam('Integral').value()
errFlux = like2.model['_2FGLJ'+str(cat_name)].funcs['Spectrum'].getParam('Integral').error()
scale = like2.model['_2FGLJ'+str(cat_name)].funcs['Spectrum'].getParam('Integral').getScale()
except:
flux = 0
errFlux = 0
scale = 0
def PerformLikelihoodAnalysis(self):
print "\nPerforming likelihood analysis on position: ra=%s, dec=%s" % (self.xref, self.yref)
# Wait a random amount of time between 1 and 5 minutes before starting in order to not crash the asf/nsf disks at SLAC
waitTime = random.random()*300
time.sleep(waitTime)
# Defind the scratch directory
JobID = os.environ.get('LSB_JOBID')
Username = getpass.getuser()
ScratchDirectory = "/scratch/%s/%s/" % (Username, JobID)
# Define the pfile directory
if JobID == 'None':
PFILESDirectory = "%s/pfiles_%s/" % (self.outdir, self.binNumber)
else:
PFILESDirectory = "%s/pfiles/" % ScratchDirectory
# Create the output directory if it doesn't already exist
if(os.path.isdir(self.outdir)==False):
print "\n >> Creating Directory: " + self.outdir
cmd = "mkdir " + self.outdir
os.system(cmd)
# Define where to save the results
likelihoodResults = '%s/likelihoodResults_bin%s.txt' % (self.outdir, self.binNumber)
# Remove any pre-existing pfiles
if(os.path.isdir(PFILESDirectory)==True):
cmd = "rm -r %s" % PFILESDirectory
os.system(cmd)
# Set the new pfiles directory
SetPfilesDirectory(PFILESDirectory)
# Make a copy of the source model
xmlModelWithPutativeSource = '%s/ModelSource_bin%s.xml' % (self.outdir, self.binNumber)
cmd = "cp " + self.srcmdl + " " + xmlModelWithPutativeSource
print cmd
os.system(cmd)
# Add a putative point source at the requested location
# AddCandidateSource(self.xref, self.yref, xmlModelWithPutativeSource)
ModifySourceModel(xmlModelWithPutativeSource, self.xref, self.yref)
# # Import the necessary gtapps
# gtlike = GtApp('gtlike')
# # Run the likelihood analysis
# print '\nPerforming the likelihood fit:'
# gtlike.run(statistic=self.statistic,
# scfile=self.scfile,
# evfile=self.evfile,
# expmap=self.expmap,
# expcube=self.expcube,
# srcmdl=xmlModelWithPutativeSource,
# irfs=self.irfs,
# optimizer=self.optimizer,
# results=likelihoodResults,
# plot='no',
# save='yes')
# Setup the unbinned likelihood object
print '\nPerforming the likelihood fit:'
try:
obs = UnbinnedObs(self.evfile,self.scfile,expMap=self.expmap,expCube=self.expcube,irfs=self.irfs)
# Define the likelihood object
#like = UnbinnedAnalysis(obs,xmlModelWithPutativeSource,optimizer=self.optimizer)
like = UnbinnedAnalysis(obs,xmlModelWithPutativeSource,optimizer='MINUIT')
# Setup the likelihood parameters
Source = 'CandidateSource'
Integral = like.par_index(Source, 'Integral')
Index = like.par_index(Source, 'Index')
LowerLimit = like.par_index(Source, 'LowerLimit')
UpperLimit = like.par_index(Source, 'UpperLimit')
# Setup the likelihood bounds
like[Integral].setScale(1e-3)
like[Index].setBounds(-5, -0.5)
# like[LowerLimit] = emin
# like[UpperLimit] = emax
# Perform the likelihood fit
#optObject = pyLike.NewMinuit(like.logLike)
#like.fit(verbosity=0,covar=True,tol=0.02,optObject=optObject)
like.fit(verbosity=1,covar=True,tol=1e-10,optimizer='MINUIT', optObject=None)
# Extract the best fit index
IndexValue = like[Index].value()
IndexError = like[Index].error()
# Extract the best fit flux
FluxValue = like.flux(Source, emin=100, emax=3e5)
FluxError = like.fluxError(Source, emin=100, emax=3e5)
# Extract likelihood fit results
print '\nLikelihood Results:'
print like.model[Source]
print "TS = %s" % like.Ts(Source)
print "Flux = %s +/- %s" % (FluxValue, FluxError)
print "Index = %s +/- %s" % (IndexValue, IndexError)
# Save the xml file
like.writeXml(xmlFile=xmlModelWithPutativeSource)
except Exception, message:
print traceback.format_exc()
def runLike(likeIn,ecent,ftol,tslim,ttype,opt,rescaleAll,lastbinUL,wx):
pts=[]
errs=[]
tsPts=[]
gamma=[]
Src=likeIn.source
NBins=likeIn.NBins
IRFs=likeIn.IRFs
expCube=likeIn.expCube
ft2=likeIn.ft2
bandModel=likeIn.bandModel
nbins=likeIn.nbins
phCorr=likeIn.phCorr
flux=likeIn.ubAn.flux
for i in range(0,nbins): #first, set up for a likelihood run
print ' -Runnng Likelihood for band%i-' %i
ev='%s_%ibins_band%i.fits' %(Src.replace(' ','_'),NBins,i)
em='%s_%ibins_band%i_%s_em.fits' %(Src.replace(' ','_'),NBins,i,IRFs)
band_obs=UnbinnedObs((ev),ft2,irfs=IRFs,expMap=em,expCube=expCube)
band_like=UnbinnedAnalysis(band_obs,bandModel,opt)
band_like.setFitTolType(ttype)
stype=band_like.model.srcs[Src].spectrum().genericName()
emin,emax=band_like.observation.roiCuts().getEnergyCuts()
if phCorr!=1:
for src in band_like.sourceNames():
par=band_like.normPar(src)
par.setValue(par.getValue()*phCorr)
#then set the scale factor to the center of the energy band, make sure it's frozen, and get index for prefactor while you're at it
Ts=band_like.Ts
freeze=band_like.freeze
fit=band_like.fit
DO=1
if stype=='PowerLaw':
scale=getParamIndx(band_like,Src,'Scale') #this is where you have to use PowerLaw, for PowerLaw2 these parameters don't exist and this will cause problems
pref=getParamIndx(band_like,Src,'Prefactor')
freeze(scale)
band_like[scale].setBounds(20,5e5)
band_like[scale].setScale(1)
band_like[scale]=1000.*ecent[i] #put center energies in units of GeV but xml files use MeV
#multiplier=band_like[pref].getScale() #need to get the scale of the prefactor so the values will not be too large
try:
logFlux=log10(flux(Src,emin=emin,emax=emax)/(emax-emin))
except:
logFlux=-14
newScale=max(int(floor(logFlux)),-14)
print newScale
band_like[pref].setScale(10.**newScale)
multiplier=10.**newScale
#cycle through the other point sources and adjust parameters of free point sources with PowerLaw2 models
for src in band_like.sourceNames():
spec=band_like[src].funcs['Spectrum']
par=spec.normPar()
if par.isFree()==True and band_like.model.srcs[src].spectrum().genericName()=='PowerLaw2':
HIGH=getParamIndx(band_like,src,'UpperLimit')
LOW=getParamIndx(band_like,src,'LowerLimit')
band_like[HIGH].setBounds(20,5e5) #just in case, make sure no out of range error gets thrown
band_like[LOW].setBounds(20,5e5)
band_like[HIGH].setScale(1.) #just in case, make sure scale is MeV
band_like[LOW].setScale(1.)
band_like[HIGH]=emax
band_like[LOW]=emin
freeze(HIGH) #just in case, make sure these aren't fit values
freeze(LOW)
if rescaleAll==True:
try:
logFlux=log10(flux(src,emin=emin,emax=emax))
except:
logFlux=-14
newScale=max(int(floor(logFlux)),-14)
par.setScale(10**newScale)
if rescaleAll==True and src!=Src and par.isFree()==True and band_like.model.srcs[src].spectrum().genericName()=='PowerLaw':
try:
logFlux=log10(flux(src,emin=emin,emax=emax)/(emax-emin))
except:
logFlux=-14
newScale=max(int(floor(logFlux)),-14)
par.setScale(10**newScale)
try:
fit(tol=1,verbosity=0,optimizer=opt)
fail=0
except:
try:
fit(tol=1*10,verbosity=0,optimizer=opt)
fail=0
except:
try:
fit(tol=1./10,verbosity=0,optimizer=opt)
fail=0
except:
print "Fit with optimizer %s with tolerances ~1 to look for negative or zero TS sources failed, if error bars are unrealistically small you may need to redo the fit for energy band %i manually" %(opt,i)
fail=1
if fail==0:
for src in band_like.sourceNames():
if src!=Src:
par=band_like.normPar(src)
if band_like[src].type=='PointSource' and Ts(src)<=0 and par.isFree()==True:
band_like.deleteSource(src)
print " -Removing %s from the model" %src
scale=getParamIndx(band_like,Src,'Scale')
pref=getParamIndx(band_like,Src,'Prefactor')
#do the actual fit
try:
fit(tol=ftol,verbosity=0)
except:
try:
print 'Trying lower tolerance of %s for band%i.' %(ftol/10,i)
fit(tol=ftol/10,verbosity=0)
except:
try:
print 'Trying higher tolerance of %s for band%i.' %(ftol*10,i)
fit(tol=ftol*10,verbosity=0)
except:
print 'No convergence for band%i, skipping.' %i
pts+=[0]
errs+=[0]
tsPts+=[0]
pass
#get the prefactor, error, and ts values
val=band_like[pref].value()
err=band_like[pref].error()
TS=Ts(Src)
if(TS<tslim or (i==(nbins-1) and lastbinUL==True)): #calculate 95% upperlimit if source TS<tslim, 25 by default (corresponds to ~5sigma)
try:
freeze(pref)
ul=UpperLimits(band_like)
UL=ul[Src].compute(emin=emin,emax=emax)
val=UL[1]
err=0
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, quoting 95% upper limit on flux.'
except:
try:
print ' Tyring higher tolerance of %s for band %i to get good starting point for upper limit calculations.' %(ftol*10,i)
band_like[pref].setFree(1)
fit(tol=ftol*10,verbosity=0)
TS=Ts(Src)
if(TS<tslim or (i==(nbins-1) and lastbinUL==True)):
freeze(pref)
ul=UpperLimits(band_like)
UL=ul[Src].compute(emin=emin,emax=emax)
val=UL[1]
err=0
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, quoting 95% upper limit on flux.'
else:
val=band_like[pref].value()
err=band_like[pref].error()
except:
try:
print ' Tyring lower tolerance of %s for band %i to get good starting point for upper limit calculations.' %(ftol/10,i)
band_like[pref].setFree(1)
fit(tol=ftol/10,verbosity=0)
TS=Ts(Src)
if(TS<tslim or (i==(nbins-1) and lastbinUL==True)):
freeze(pref)
ul=UpperLimits(band_like)
UL=ul[Src].compute(emin=emin,emax=emax)
val=UL[1]
err=0
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, quoting 95% upper limit on flux.'
else:
val=band_like[pref].value()
err=band_like[pref].error()
except:
err=0
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, TS<%s' %tslim,'but UpperLimits computation failed.'
print ' Quoting best fit value with zero error.'
tsPts+=[TS]
pts+=[val*multiplier/phCorr]
errs+=[err*multiplier/phCorr]
elif stype=='PowerLaw2':
Upper=getParamIndx(band_like,Src,'UpperLimit')
Lower=getParamIndx(band_like,Src,'LowerLimit')
Integral=getParamIndx(band_like,Src,'Integral')
Index=getParamIndx(band_like,Src,'Index')
freeze(Upper)
freeze(Lower)
band_like[Upper].setBounds(20,5e5)
band_like[Lower].setBounds(20,5e5)
band_like[Lower].setScale(1)
band_like[Lower].setScale(1)
band_like[Upper]=emax
band_like[Lower]=emin
#multiplier=band_like[Integral].getScale()
try:
logFlux=log10(flux(Src,emin=emin,emax=emax))
except:
logFlux=-14
newScale=max(int(floor(logFlux)),-14)
band_like[Integral].setScale(10**newScale)
multiplier=10**newScale
indxMult=band_like[Index].getScale()
for src in band_like.sourceNames():
if src!=Src:
spec=band_like[src].funcs['Spectrum']
par=spec.normPar()
if par.isFree()==True and band_like.model.srcs[src].spectrum().genericName()=='PowerLaw2':
HIGH=getParamIndx(band_like,src,'UpperLimit')
LOW=getParamIndx(band_like,src,'LowerLimit')
band_like[HIGH].setBounds(20,5e5) #just in case, make sure no out of range error gets thrown
band_like[LOW].setBounds(20,5e5)
band_like[HIGH].setScale(1.) #just in case, make sure scale is MeV
band_like[LOW].setScale(1.)
band_like[HIGH]=emax
band_like[LOW]=emin
freeze(HIGH) #just in case, make sure these aren't fit values
freeze(LOW)
if rescaleAll==True:
try:
logFlux=log10(flux(src,emin=emin,emax=emax))
except:
logFlux=-14
newScale=max(int(floor(logFlux)),-14)
par.setScale(10**newScale)
if rescaleAll==True and src!=Src and par.isFree()==True and band_like.model.srcs[src].spectrum().genericName()=='PowerLaw':
try:
logFlux=log10(flux(src,emin=emin,emax=emax)/(emax-emin))
except:
logFlux=-14
newScale=max(int(floor(logFlux)),-14)
par.setScale(10**newScale)
try:
fit(tol=1,verbosity=0,optimizer=opt)
fail=0
except:
try:
fit(tol=1*10,verbosity=0,optimizer=opt)
fail=0
except:
try:
fit(tol=1./10,verbosity=0,optimizer=opt)
fail=0
except:
print "Fit with optimizer %s with tolerances ~1 to look for negative or zero TS sources failed, if error bars are unrealistically small you may need to redo the fit for energy band %i manually" %(opt,i)
fail=1
if not fail:
for src in band_like.sourceNames():
if src!=Src:
par=band_like.normPar(src)
if band_like[src].type=='PointSource' and Ts(src)<=0 and par.isFree()==True:
band_like.deleteSource(src)
print " -Removing %s from the model" %src
Upper=getParamIndx(band_like,Src,'UpperLimit')
Lower=getParamIndx(band_like,Src,'LowerLimit')
Integral=getParamIndx(band_like,Src,'Integral')
Index=getParamIndx(band_like,Src,'Index')
try:
fit(tol=ftol,verbosity=0)
except:
try:
print 'Trying lower tolerance of %s for band%i.' %(ftol/10,i)
fit(tol=ftol/10,verbosity=0)
except:
try:
print 'Trying higher tolerance of %s for band%i.' %(ftol*10,i)
fit(tol=ftol*10,verbosity=0)
except:
print 'No convergence for band%i, skipping.' %i
pts+=[0]
errs+=[0]
tsPts+=[0]
gamma+=[0]
DO=0
if DO:
val=band_like[Integral].value()
err=band_like[Integral].error()
TS=Ts(Src)
gam=band_like[Index].value()*indxMult*-1.
if(TS<tslim or (i==(nbins-1) and lastbinUL==True)): #calculate 95% upperlimit if source TS<9, i.e. less than 3 sigma detection in each energy band
try:
freeze(Integral)
ul=UpperLimits(band_like)
UL=ul[Src].compute(emin=emin,emax=emax)
val=UL[1]
err=0
#need to redo the fit with band_like set to Upper Limit value to get correct spectral index for that value
band_like[Integral]=val
freeze(Integral)
fit(tol=ftol,verbosity=0)
gam=band_like[Index].value()*indxMult*-1.
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, quoting 95% upper limit on flux.'
except:
try:
print ' Tyring higher tolerance of %s for band %i to get good starting point for upper limit calculations.' %(ftol*10,i)
band_like[Integral].setFree(1)
fit(tol=ftol*10,verbosity=0)
TS=Ts(Src)
if(TS<tslim or (i==(nbins-1) and lastbinUL==True)):
freeze(Integral)
ul=UpperLimits(band_like)
UL=ul[Src].compute(emin=emin,emax=emax)
val=UL[1]
err=0
band_like[Integral]=val
freeze(Integral)
fit(tol=ftol*10,verbosity=0)
gam=band_like[Index].value()*indxMult*-1.
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, quoting 95% upper limit on flux.'
else:
val=band_like[Integral].value()
err=band_like[Integral].error()
gam=band_like[Index].value()*indxMult*-1.
except:
try:
print ' Tyring lower tolerance of %s for band %i to get good starting point for upper limit calculations.' %(ftol/10,i)
band_like[Integral].setFree(1)
fit(tol=ftol/10,verbosity=0)
TS=Ts(Src)
if(TS<tslim or (i==(nbins-1) and lastbinUL==True)):
freeze(Integral)
ul=UpperLimits(band_like)
UL=ul[Src].compute(emin=emin,emax=emax)
val=UL[1]
err=0
band_like[Integral]=val
freeze(Integral)
fit(tol=ftol/10,verbosity=0)
gam=band_like[Index].value()*indxMult*-1.
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, quoting 95% upper limit on flux.'
else:
val=band_like[Integral].value()
err=band_like[Integral].error()
gam=band_like[Index].value()*indxMult*-1.
except:
err=0
print ' NOTE: Band%i,' %i,'with center energy',ecent[i],'GeV, TS<%s' %tslim,'but UpperLimits computation failed.'
print ' Quoting best fit value with zero error.'
tsPts+=[TS]
pts+=[val*multiplier/phCorr]
errs+=[err*multiplier/phCorr]
gamma+=[gam]
else:
print '%s needs to have PowerLaw or PowerLaw2 spectral model, not %s' %(Src,stype)
print 'exiting without running likelihood in the energy bands'
return None,None,None,None
if wx:
band_like.writeXml('%s_%ibins_band%i_fitmodel.xml'%(Src.replace(' ','_'),NBins,i))
del band_like
del band_obs
return pts,errs,tsPts,gamma
def PerformLikelihoodAnalysis(self):
print "\nPerforming likelihood analysis on position: ra=%s, dec=%s" % (
self.xref, self.yref)
# Wait a random amount of time between 1 and 5 minutes before starting in order to not crash the asf/nsf disks at SLAC
waitTime = random.random() * 300
time.sleep(waitTime)
# Defind the scratch directory
JobID = os.environ.get('LSB_JOBID')
Username = getpass.getuser()
ScratchDirectory = "/scratch/%s/%s/" % (Username, JobID)
# Define the pfile directory
if JobID == 'None':
PFILESDirectory = "%s/pfiles_%s/" % (self.outdir, self.binNumber)
else:
PFILESDirectory = "%s/pfiles/" % ScratchDirectory
# Create the output directory if it doesn't already exist
if (os.path.isdir(self.outdir) == False):
print "\n >> Creating Directory: " + self.outdir
cmd = "mkdir " + self.outdir
os.system(cmd)
# Define where to save the results
likelihoodResults = '%s/likelihoodResults_bin%s.txt' % (self.outdir,
self.binNumber)
# Remove any pre-existing pfiles
if (os.path.isdir(PFILESDirectory) == True):
cmd = "rm -r %s" % PFILESDirectory
os.system(cmd)
# Set the new pfiles directory
SetPfilesDirectory(PFILESDirectory)
# Make a copy of the source model
xmlModelWithPutativeSource = '%s/ModelSource_bin%s.xml' % (
self.outdir, self.binNumber)
cmd = "cp " + self.srcmdl + " " + xmlModelWithPutativeSource
print cmd
os.system(cmd)
# Add a putative point source at the requested location
# AddCandidateSource(self.xref, self.yref, xmlModelWithPutativeSource)
ModifySourceModel(xmlModelWithPutativeSource, self.xref, self.yref)
# # Import the necessary gtapps
# gtlike = GtApp('gtlike')
# # Run the likelihood analysis
# print '\nPerforming the likelihood fit:'
# gtlike.run(statistic=self.statistic,
# scfile=self.scfile,
# evfile=self.evfile,
# expmap=self.expmap,
# expcube=self.expcube,
# srcmdl=xmlModelWithPutativeSource,
# irfs=self.irfs,
# optimizer=self.optimizer,
# results=likelihoodResults,
# plot='no',
# save='yes')
# Setup the unbinned likelihood object
print '\nPerforming the likelihood fit:'
try:
obs = UnbinnedObs(self.evfile,
self.scfile,
expMap=self.expmap,
expCube=self.expcube,
irfs=self.irfs)
# Define the likelihood object
#like = UnbinnedAnalysis(obs,xmlModelWithPutativeSource,optimizer=self.optimizer)
like = UnbinnedAnalysis(obs,
xmlModelWithPutativeSource,
optimizer='MINUIT')
# Setup the likelihood parameters
Source = 'CandidateSource'
Integral = like.par_index(Source, 'Integral')
Index = like.par_index(Source, 'Index')
LowerLimit = like.par_index(Source, 'LowerLimit')
UpperLimit = like.par_index(Source, 'UpperLimit')
# Setup the likelihood bounds
like[Integral].setScale(1e-3)
like[Index].setBounds(-5, -0.5)
# like[LowerLimit] = emin
# like[UpperLimit] = emax
# Perform the likelihood fit
#optObject = pyLike.NewMinuit(like.logLike)
#like.fit(verbosity=0,covar=True,tol=0.02,optObject=optObject)
like.fit(verbosity=1,
covar=True,
tol=1e-10,
optimizer='MINUIT',
optObject=None)
# Extract the best fit index
IndexValue = like[Index].value()
IndexError = like[Index].error()
# Extract the best fit flux
FluxValue = like.flux(Source, emin=100, emax=3e5)
FluxError = like.fluxError(Source, emin=100, emax=3e5)
# Extract likelihood fit results
print '\nLikelihood Results:'
print like.model[Source]
print "TS = %s" % like.Ts(Source)
print "Flux = %s +/- %s" % (FluxValue, FluxError)
print "Index = %s +/- %s" % (IndexValue, IndexError)
# Save the xml file
like.writeXml(xmlFile=xmlModelWithPutativeSource)
except Exception, message:
print traceback.format_exc()
def runLikelihood(subdir, tpl_file):
'''This runction runs the likelihood code on a set of pixels in a
subdirectory. It takes as input the subdirectory to work on and a
template counts map. It reads it's configuration from a pickle
file (par.pck) that should be located in the subdirectory and the
pixel locations from another pickle file (pixel.pck). It then
creats an overall likelihood object, does a quick global fit and
then loops over the pixels. At each pixel, it creats a test
source, fits that source, calculates the TS of the source and
writes the results to an output file in the subdirectory called
ts_results.dat.'''
parfile = open("par.pck", "r")
pars = pickle.load(parfile)
pixelfile = open("pixel.pck", "r")
pixels = pickle.load(pixelfile)
pixel_coords = PixelCoords(tpl_file)
if pars['statistic'] == 'UNBINNED':
import UnbinnedAnalysis as UBAn
obs = UBAn.UnbinnedObs(resolve_fits_files(pars['evfile']),
resolve_fits_files(pars['scfile']),
expMap='../' + pars['expmap'],
expCube='../' + pars['expcube'],
irfs=pars['irfs'])
like = UBAn.UnbinnedAnalysis(obs, '../' + pars['srcmdl'],
pars['optimizer'])
elif pars['statistic'] == 'BINNED':
import BinnedAnalysis as BAn
obs = BAn.BinnedObs(srcMaps='../' + pars['srcmaps'],
expCube='../' + pars['expcube'],
binnedExpMap='../' + pars['bexpmap'],
irfs=pars['irfs'])
like = BAn.BinnedAnalysis(obs, '../' + pars['srcmdl'],
pars['optimizer'])
like.setFitTolType(pars['toltype'])
like.optimize(0)
loglike0 = like()
test_src = getPointSource(like)
target_name = 'testSource'
test_src.setName(target_name)
outfile = 'ts_results.dat'
finished_pixels = []
if os.path.isfile(outfile):
input = open(outfile, 'r')
for line in input:
tokens = line.strip().split()
ij = int(tokens[0]), int(tokens[1])
finished_pixels.append(ij)
input.close()
output = open(outfile, 'a')
for indx, i, j in pixels:
if (i, j) in finished_pixels:
continue
ra, dec = pixel_coords(i, j)
test_src.setDir(ra, dec, True, False)
like.addSource(test_src)
like.optimize(0)
ts = -2 * (like() - loglike0)
output.write("%3i %3i %.3f %.3f %.5f\n" % (i, j, ra, dec, ts))
output.flush()
like.deleteSource(target_name)
output.close()
poi_values=poi_values,
poi_probs=poi_probs,
poi_dlogL=poi_dlogL,
flux_emin=emin,
flux_emax=emax)
return ul_flux, results
if __name__ == "__main__":
import sys
srcName = "EMS0001"
obs = UnbinnedAnalysis.UnbinnedObs('ft1_roi.fits',
scFile='ft2.fits',
expMap='expMap.fits',
expCube='expCube.fits',
irfs='P6_V9_DIFFUSE')
#min_opt = 'InteractiveMinuit,MIN 0 $TOL,HESSE,.q'
#pro_opt = 'InteractiveMinuit,SET STR 0,MIN 0 $TOL,.q'
min_opt = 'MINUIT'
pro_opt = None
like = UnbinnedAnalysis.UnbinnedAnalysis(obs, 'model.xml', min_opt)
src_spectrum = like[srcName].funcs['Spectrum']
par = src_spectrum.getParam("Index")
if par:
par.setFree(False)
par.setValue(-2.0)
import sys
from UnbinnedAnalysis import *
from UpperLimits import UpperLimits
filename, par_srcname, par_irfs = sys.argv[1: 4]
par_emin = float(sys.argv[4])
par_emax = float(sys.argv[5])
file_eventFile, file_scFile, file_expMap, file_expCube, file_srcModel = sys.argv[6: 11]
obs = UnbinnedObs(eventFile=file_eventFile, scFile=file_scFile,
expMap=file_expMap, expCube=file_expCube, irfs=par_irfs)
like = UnbinnedAnalysis(obs, srcModel=file_srcModel, optimizer='NewMinuit')
like.fit(verbosity=0, covar=True)
like.Ts(par_srcname)
ul = UpperLimits(like)
ul[par_srcname].compute(emin=par_emin, emax=par_emax)
f = open(filename, 'w')
print >> f, ul[par_srcname].results
f.close()
