def main(cmd_line):
#takes integer arguement specifying the simulation number
sim = cmd_line[1]
indir = "/zfs/astrohe/ckarwin/Machine_Learning_GC/Sim_2/Dame_Maps/"
outdir = indir + "Simulation_Output/sim_%s" % sim
if (os.path.isdir(outdir) == True):
shutil.rmtree(outdir)
os.system('mkdir %s' % outdir)
os.chdir(outdir)
#A single simulation should first be ran, which will generate all the needed data products that can be reused for subsequent simulations.
#The data products that are copied below are for subsequent simulations after the first run.
shutil.copy2('%s/srcmap_00.fits' % indir, 'srcmap_00.fits')
shutil.copy2('%s/bexpmap_00.fits' % indir, 'bexpmap_00.fits')
shutil.copy2('%s/ccube_00.fits' % indir, 'ccube_00.fits')
shutil.copy2('%s/config.yaml' % indir, 'config.yaml')
shutil.copy2('%s/ft1_00.fits' % indir, 'ft1_00.fits')
shutil.copy2('%s/LAT_Final_Excess_Template.fits' % indir,
'LAT_Final_Excess_Template.fits')
#setup analysis:
gta = GTAnalysis('config.yaml', logging={'verbosity': 3})
gta.setup()
#gta.load_roi("after_setup")
#set components to zero for simulations:
gta.set_norm("MapSource", 0.0) #excess template
gta.set_norm("galdiff04", 0.0) #CO12_0-5
gta.set_norm("galdiff05", 0.0) #CO12_6-9
gta.set_norm("galdiff06", 0.0) #CO12_10-12
gta.set_norm("galdiff07", 0.0) #CO12_13-16
#run simulations:
gta.write_roi('before_sim')
gta.simulate_roi(randomize=True)
#delete sources that were simulated:
gta.delete_source("galdiff00", delete_source_map=False)
gta.delete_source("galdiff01", delete_source_map=False)
gta.delete_source("galdiff02", delete_source_map=False)
gta.delete_source("galdiff03", delete_source_map=False)
#set random normalizations of sources for performing fit:
#n4 = np.random.normal(1.0,0.2)
#n5 = np.random.normal(1.0,0.2)
#n6 = np.random.normal(1.0,0.2)
#nms = np.random.normal(1e-4,0.5e-4)
gta.set_norm("galdiff04", 0.8)
gta.set_norm("galdiff05", 0.8)
gta.set_norm("galdiff06", 1.2)
gta.set_norm("galdiff07", 1.2)
#perform fit for null hypothesis:
gta.free_sources(free=True)
gta.free_source("galdiff07", free=False)
gta.free_source("MapSource", free=False)
Fit = gta.fit()
null = Fit["loglike"]
gta.write_roi('after_null_fit')
gta.write_model_map("null_model")
#set normalizations of sources for performing alternative fit:
gta.set_norm("galdiff04", 0.8)
gta.set_norm("galdiff05", 0.8)
gta.set_norm("galdiff06", 1.2)
gta.set_norm("galdiff07", 1.2)
gta.set_norm("MapSource", 1e-4)
gta.free_sources(free=True)
#gta.free_source("galdiff07",free=False)
Fit2 = gta.fit()
alternative = Fit2["loglike"]
gta.write_roi('after_alternative_fit')
gta.write_model_map("alternative_model")
#calculate source spectrum:
ltcube = '/zfs/astrohe/ckarwin/Stacking_Analysis/UFOs/NGC_4151_Analysis/MakeLTCube/zmax_105/UFOs_binned_ltcube.fits'
obs = BinnedObs(srcMaps='srcmap_00.fits',
expCube=ltcube,
binnedExpMap='bexpmap_00.fits',
irfs='P8R3_SOURCE_V2')
like = BinnedAnalysis(obs,
'after_alternative_fit_00.xml',
optimizer='MINUIT')
Elist, Flist = CalcFlux(like, 'MapSource')
data = {"energ[MeV]": Elist, "flux[MeV/cm^2/s]": Flist}
df = pd.DataFrame(data=data)
df.to_csv("excess_flux.dat", sep="\t", index=False)
#calculte TS:
TS = -2 * (null - alternative)
#write results:
savefile = "TS_sim_%s.txt" % sim
f = open(savefile, "w")
f.write(str(TS))
f.close()
#rm ft file to reduce storage:
os.system('rm ft1_00.fits')
return
