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 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 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 _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 __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 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 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 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 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 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 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 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 _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
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)
like.syncSrcParams(srcName)
ul, results = calc_int(like, srcName, verbosity=1)
print results
for i in range(len(results["profile_x"])):
print results["profile_x"][i], results["profile_y"][i]
# 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':
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 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
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")
print("=========================================\n\n")
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)
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 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(" | ")
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
