def Analysis(folder,
config,
configgeneric=None,
tag="",
convtyp='-1',
verbose=1):
import os
from enrico import utils
from enrico.gtfunction import Observation
from enrico.fitmaker import FitMaker
import Loggin
mes = Loggin.Message()
""" run an analysis"""
Obs = Observation(folder, config, tag=tag)
if verbose:
utils._log('SUMMARY: ' + tag)
Obs.printSum()
FitRunner = FitMaker(Obs, config) ##Class
if config['Spectrum']['FitsGeneration'] == 'yes':
FitRunner.FirstSelection(
configgeneric) #Generates fits files for the coarse selection
FitRunner.GenerateFits(
) #Generates fits files for the rest of the products
return FitRunner
def Analysis(folder, config, tag="", convtyp='-1', verbose = 1):
""" run an analysis"""
Obs = Observation(folder, config, convtyp, tag=tag)
if verbose:
utils._log('SUMMARY: ' + tag)
Obs.printSum()
FitRunner = FitMaker(Obs, config)##Class
if config['Spectrum']['FitsGeneration'] == 'yes':
FitRunner.GenerateFits() #Generates fits files
return FitRunner
def Analysis(folder, config, tag="", convtyp='-1', verbose=1):
""" run an analysis"""
Obs = Observation(folder, config, convtyp, tag=tag)
if verbose:
utils._log('SUMMARY: ' + tag)
Obs.printSum()
FitRunner = FitMaker(Obs, config) ##Class
if config['Spectrum']['FitsGeneration'] == 'yes':
FitRunner.GenerateFits() #Generates fits files
return FitRunner
def AppLC(infile):
'''Main function of the apperture photometrie Lightcurve script. Read the config file and run the analysis'''
ROOT.gROOT.SetBatch(ROOT.kTRUE) #Batch mode
enricodir = environ.DIRS.get('ENRICO_DIR')
fermidir = environ.DIRS.get('FERMI_DIR')
config = get_config(infile)
folder = config['out']
#Create a subfolder name LightCurve
LCoutfolder = folder + "/" + AppLCPath
os.system("mkdir -p " + LCoutfolder)
#Change the ROI to the desired radius in degree, legacy 1 deg.
try:
config['space']['rad'] = config['AppLC']['rad']
except NameError:
config['space']['rad'] = 1
Nbins = config['AppLC']['NLCbin'] #Number of bins
#Get The time bin
dt = (config['time']['tmax'] - config['time']['tmin']) / Nbins #sec
Obs = Observation(LCoutfolder, config, tag="")
if config['AppLC']["FitsGeneration"] == "yes":
_log('gtselect', 'Select data from library') #run gtselect
Obs.FirstCut()
Obs.SelectEvents()
_log('gtmktime',
'Update the GTI and cut data based on ROI') #run gtdiffresp
Obs.MkTime()
#Binning from data or using a fix bin size
if config['AppLC']['binsFromData'] == "no":
_log('gtbin', 'bin the data into a light-curve using fixe time bin'
) #run gtbin
print "Use a dt of %2.2e seconds" % (dt)
Obs.GtLCbin(dt=dt)
else:
spfile = pyfits.open(Obs.eventfile)
diff = spfile[1].data.field(9)[1:-1] - spfile[1].data.field(9)[:-2]
dt = np.min(
diff
) / 2. ##Compute the delta T as being the min delta t between 2 events divided by 2
timefile = LCoutfolder + "/Timebin.txt"
MakeTimebinFile(Obs, timefile)
_log('gtbindef', 'define de bins') #run gtbindef
Obs.GtBinDef(timefile)
_log('gtbin',
'bin the data into a light-curve using bins based on data'
) #run gtbin
Obs.GtLCbin(dt=0)
_log('gtexposure', 'compute the exposure') #run gtexposure
Obs.GtExposure()
#Get Some usefull value here. This allow PlotAppLC to be call independently
Nbins = config['AppLC']['NLCbin'] #Number of bins
#Plot the results and dump into ascii files
PlotAppLC(Nbins, LCoutfolder, Obs.lcfile)
def Analysis(folder, config, configgeneric=None, tag="", convtyp='-1', verbose = 1):
mes = Loggin.Message()
""" run an analysis"""
Obs = Observation(folder, config, tag=tag)
if verbose:
utils._log('SUMMARY: ' + tag)
Obs.printSum()
FitRunner = FitMaker(Obs, config)##Class
if config['Spectrum']['FitsGeneration'] == 'yes':
FitRunner.FirstSelection(configgeneric) #Generates fits files for the coarse selection
FitRunner.GenerateFits() #Generates fits files for the rest of the products
return FitRunner
def PlotTSmap(self) :
""" Gather the results of the evaluation of
each pixel and fill a fits file"""
folder = self.config['out']
# Read the cmap produced before to get the grid for the TS map
FitRunner = Observation(folder, self.config)
try :
header = pyfits.getheader(FitRunner.cmapfile)
except :
self.error('Count map not found.')
data = pyfits.getdata(FitRunner.cmapfile)*0.
npix_im = min(header['NAXIS1'],header['NAXIS2'])
npix = min(self.config['TSMap']['npix'],npix_im)
Xref = header['CRPIX1']
Yref = header['CRPIX2']
binsz = header['CDELT1']
import string # read the results
for i in xrange(npix):
for j in xrange(npix):
try :
lines = open(self._PixelFile(i,j),"r").readlines()
Value = float(string.split(lines[0])[2])
except :
self.warning("Cannot find, open or read "+self._PixelFile(i,j))
Value = 0.
data[Xref+ (i-npix/2.)][Yref+ (j-npix/2.)] = Value
# save in a fits files
pyfits.writeto(folder+"/"+self.TSfits,data,header)
self.info("TS Map saved in "+folder+"/"+self.TSfits)
def Runsrcprob(config):
config['space']['rad'] = config['srcprob']['rad']
Obs = Observation(config['out'], config, tag="srcprob")
_log("Summary")
Obs.printSum()
if config['srcprob']['FitsGeneration'] =='yes':
_log("Select data")
Obs.FirstCut()
if config['analysis']['ComputeDiffrsp'] == 'yes':
_log("Compute diffuse responses")
Obs.DiffResps()
_log("Run gtsrcprob")
Obs.SrcProb()
probfile=fits.open(Obs.Probfile)
srclist = open(config['srcprob']['srclist'],"r").readlines()
for src in srclist:
proba = probfile[1].data.field(string.split(src)[0])
energy = probfile[1].data.field("ENERGY")
ra = probfile[1].data.field("RA")
dec = probfile[1].data.field("DEC")
time = probfile[1].data.field("TIME")
indices = energy.argsort()
mes = Loggin.Message()
mes.info( "Results sorted by decreasing energy")
Print(indices,config,ra,dec,proba,energy,time)
print
mes.info( "Results sorted by decreasing probability")
indices = proba.argsort()
Print(indices,config,ra,dec,proba,energy,time)
def _GenFit(self):
try:
del self.Fit
except:
pass
if self.config['Spectrum']['SummedLike'] == 'yes':
Obs1 = Observation(self.folder, self.config, tag="FRONT")
Obs2 = Observation(self.folder, self.config, tag="BACK")
self.FitRunnerfront = FitMaker(Obs1, self.config)
self.FitRunnerback = FitMaker(Obs2, self.config)
self.FitRunnerfront.CreateLikeObject()
self.FitRunnerback.CreateLikeObject()
self.Fit = SummedLikelihood.SummedLikelihood()
else:
Obs = Observation(self.folder, self.config, tag="")
self.FitRunner = FitMaker(Obs, self.config) ##Class
self.Fit = self.FitRunner.CreateLikeObject()
def Analysis(folder, config, configgeneric=None, tag="", convtyp='-1', verbose = 1):
mes = Loggin.Message()
""" run an analysis"""
# If there are no xml files, create it and print a warning <--- This should be here?
#if len(glob.glob(config['file']['xml'].replace('.xml','*.xml')))==0:
if len(glob.glob(config['file']['xml']))==0: #.replace('.xml','*.xml')))==0:
mes.warning("Xml not found, creating one for the given config %s" %config['file']['xml'])
XmlMaker(config)
Obs = Observation(folder, config, tag=tag)
if verbose:
utils._log('SUMMARY: ' + tag)
Obs.printSum()
FitRunner = FitMaker(Obs, config)##Class
if config['Spectrum']['FitsGeneration'] == 'yes':
FitRunner.FirstSelection(configgeneric) #Generates fits files for the coarse selection
FitRunner.GenerateFits() #Generates fits files for the rest of the products
return FitRunner
def FindSrc(infile):
config = get_config(infile)
folder = config['out']
Obs = Observation(folder, config)
utils._log('SUMMARY: ')
Obs.printSum()
FitRunner = FitMaker(Obs, config)
if config["findsrc"]["FitsGeneration"]== "yes":
config['analysis']['likelihood'] = 'unbinned'
FitRunner.GenerateFits()
FitRunner._log('gtfindsrc', 'Optimize source position')
os.system("rm "+utils._dump_findsrcout(config))
Obs.FindSource()
try:
update_reg(config)
except:
pass
def __init__(self, config):
self.config = config
self.folder = self.config['out']
os.system("mkdir -p "+self.folder+"/TestModel")
convtype = self.config['analysis']['convtype']
if self.config['Spectrum']['SummedLike'] == 'yes':
Obs1 = Observation(self.folder, self.config, convtype=0, tag="FRONT")
Obs2 = Observation(self.folder, self.config, convtype=1, tag="BACK")
FitRunnerfront = FitMaker(Obs1, self.config)
FitRunnerback = FitMaker(Obs2, self.config)
FitRunnerfront.CreateLikeObject()
FitRunnerback.CreateLikeObject()
self.Fit = SummedLikelihood.SummedLikelihood()
else:
Obs = Observation(self.folder, self.config, convtype, tag="")
FitRunner = FitMaker(Obs, self.config)##Class
self.Fit = FitRunner.CreateLikeObject()
# Store the results in a dictionnary
self.Results = {}
self.Results["PowerLaw"] = 0
self.Results["LogParabola"] = 0
self.Results["PLSuperExpCutoff"] = 0
def __init__(self,config,infile):
super(TSMap,self).__init__()
Loggin.Message.__init__(self)
self.config = config
self.config['Spectrum']['FitsGeneration'] = 'no'
self.tsfolder = self.config['out']+"/"+TSMapPath
self.TSfits = self.config['target']['name']+'_'+self.config['file']['tag']+"_TSMap.fits"
self.infile = infile
self.npix = self.config['TSMap']['npix']
# Read the cmap produced before to get the grid for the TS map
FitRunner = Observation(self.config['out'], self.config)
try :
cmap = pyfits.open(FitRunner.cmapfile)
except :
self.error('Count map not found.')
npix_im = min(cmap[0].header['NAXIS1'],cmap[0].header['NAXIS2'])
self.npix = min(self.npix,npix_im)
self.RAref = cmap[0].header['CRVAL1']
self.DECref = cmap[0].header['CRVAL2']
self.binsz = cmap[0].header['CDELT1']
f.tick_labels.set_style('colons')
#f.tick_labels.set_font(size='small')
f.axis_labels.set_xtext('Right Ascension (deg)')
f.axis_labels.set_ytext('Declination (deg)')
try:
infile = sys.argv[1]
except:
"Please provide a configuration file"
sys.exit(1)
print
config = get_config(infile)
obs = Observation(config["out"],
config,
convtyp=config['analysis']['convtype'])
counts = dict(label='Counts', filename=obs.cmapfile)
model = dict(label='Model', filename=obs.ModelMap)
residuals = dict(label='Residuals',
filename=config['out'] + "/" + config['target']['name'] +
'_Residual_Model_cmap.fits')
images = [counts, model, residuals][::-1]
# Determine image center and width / height
dpi = 2000
header = fits.getheader(images[0]['filename'])
wcs = WCS(header)
header['NAXIS1'] / dpi
header['NAXIS2'] / dpi
f.tick_labels.set_xformat('dd')
f.tick_labels.set_yformat('dd')
f.tick_labels.set_style('colons')
#f.tick_labels.set_font(size='small')
f.axis_labels.set_xtext('Right Ascension (deg)')
f.axis_labels.set_ytext('Declination (deg)')
try:
infile = sys.argv[1]
except:
"Please provide a configuration file"
sys.exit(1)
print
config = get_config(infile)
obs = Observation(config["out"], config)
counts = dict(label='Counts', filename=obs.cmapfile)
model = dict(label='Model', filename=obs.ModelMap)
residuals = dict(label='Residuals', filename=config['out'] + "/" + config['target']['name']+'_Residual_Model_cmap.fits')
images = [counts, model, residuals][::-1]
# Determine image center and width / height
dpi = 2000
header = fits.getheader(images[0]['filename'])
wcs = WCS(header)
header['NAXIS1'] / dpi
header['NAXIS2'] / dpi
lon, lat = header['NAXIS1'] / 2., header['NAXIS2'] / 2.
x_center, y_center = wcs.wcs_pix2world(lon, lat, 0)
