def run_analysis(config):
print('Running analysis...')
gta = GTAnalysis(config)
gta.setup()
gta.optimize()
gta.print_roi()
# Localize and generate SED for first source in ROI
srcname = gta.roi.sources[0].name
gta.free_source(srcname)
gta.fit()
gta.localize(srcname)
gta.sed(srcname)
gta.write_roi('roi', make_plots=True)
gta.tsmap(make_plots=True)
gta.residmap(make_plots=True)
def run_analysis(config):
print('Running analysis...')
gta = GTAnalysis(config)
gta.setup()
gta.optimize()
gta.print_roi()
# Localize and generate SED for first source in ROI
srcname = gta.roi.sources[0].name
gta.free_source(srcname)
gta.fit()
gta.localize(srcname)
gta.sed(srcname)
gta.write_roi('roi', make_plots=True)
gta.tsmap(make_plots=True)
gta.residmap(make_plots=True)
class ExtensionFit:
def __init__(self, configFile):
self.gta = GTAnalysis(configFile, logging={'verbosity': 3})
self.target = None
self.targetRadius = None
self.distance = None
self.catalog = fits.getdata('/users-data/mfalxa/code/gll_psch_v13.fit',
1)
def setSourceName(self, sourceObject, newName):
self.gta.delete_source(sourceObject['name'])
self.gta.add_source(newName, sourceObject)
''' INITIALIZE '''
def initialize(self, sizeROI, rInner, addToROI, TSMin, debug):
self.gta.setup()
if self.gta.config['selection']['emin'] >= 10000:
self.gta.set_parameter('galdiff', 'Scale', 30000)
if debug == True:
self.gta.make_plots('startAll')
self.gta.residmap(prefix='startAll', make_plots=True)
# Get model source names
sourceList = self.gta.get_sources(exclude=['isodiff', 'galdiff'])
# Delete sources unassociated with TS < 50
for i in range(len(sourceList)):
if sourceList[i]['catalog']['TS_value'] < TSMin and self.catalog[
'CLASS'][self.catalog['Source_Name'] == sourceList[i]
['name']][0] == '':
self.gta.delete_source(sourceList[i]['name'])
closests = self.gta.get_sources(distance=rInner,
exclude=['isodiff', 'galdiff'])
# Delete all unidentified sources
for i in range(len(closests)):
if self.catalog['CLASS'][self.catalog['Source_Name'] == closests[i]
['name']][0].isupper() == False:
self.gta.delete_source(closests[i]['name'])
if self.catalog['CLASS'][self.catalog['Source_Name'] == closests[i]
['name']][0] == 'SFR':
self.target = closests[i]
self.setSourceName(self.target, 'TESTSOURCE')
# If debug, save ROI and make plots
if debug == True:
self.gta.write_roi('startModel')
self.gta.residmap(prefix='start', make_plots=True)
self.gta.make_plots('start')
# Optmize spectral parameters for sources with npred > 1
self.gta.optimize(npred_threshold=1, skip=['isodiff'])
# Get model source names
sourceList = self.gta.get_sources(distance=sizeROI + addToROI,
square=True,
exclude=['isodiff', 'galdiff'])
# Iterate source localizing on source list
for i in range(len(sourceList)):
if sourceList[i].extended == False:
self.gta.localize(sourceList[i]['name'],
write_fits=False,
write_npy=False,
update=True)
# Free sources within ROI size + extra distance from center
self.gta.free_sources(distance=sizeROI + addToROI, square=True)
# Re-optimize ROI
self.gta.optimize(skip=['isodiff'])
# Save and make plots if debug
if debug == True:
self.gta.write_roi('modelInitialized')
self.gta.residmap(prefix='initialized', make_plots=True)
self.gta.make_plots('initialized')
# Lock sources
self.gta.free_sources(free=False)
''' OUTER REGION '''
def outerRegionAnalysis(self, sizeROI, rInner, sqrtTsThreshold,
minSeparation, debug):
self.gta.free_sources(distance=sizeROI,
pars='norm',
square=True,
free=True)
self.gta.free_sources(distance=rInner, free=False)
self.gta.free_source('galdiff', free=True)
self.gta.free_source('isodiff', free=False)
# Seek new sources until none are found
sourceModel = {
'SpectrumType': 'PowerLaw',
'Index': 2.0,
'Scale': 30000,
'Prefactor': 1.e-15,
'SpatialModel': 'PointSource'
}
newSources = self.gta.find_sources(sqrt_ts_threshold=sqrtTsThreshold,
min_separation=minSeparation,
model=sourceModel,
**{
'search_skydir':
self.gta.roi.skydir,
'search_minmax_radius':
[rInner, sizeROI]
})
if len(newSources) > 0:
for i in range(len(newSources)):
if newSources['sources'][i]['ts'] > 100.:
self.gta.set_source_spectrum(
newSources['sources'][i]['name'],
spectrum_type='LogParabola')
self.gta.free_source(newSources['sources'][i]['name'])
self.gta.fit()
self.gta.free_source(newSources['sources'][i]['name'],
free=False)
# Optimize all ROI
self.gta.optimize(skip=['isodiff'])
# Save sources found
if debug == True:
self.gta.residmap(prefix='outer', make_plots=True)
self.gta.write_roi('outerAnalysisROI')
self.gta.make_plots('outer')
''' INNER REGION '''
def innerRegionAnalysis(self, sizeROI, rInner, maxIter, sqrtTsThreshold,
minSeparation, dmMin, TSm1Min, TSextMin, debug):
self.gta.free_sources(distance=sizeROI, square=True, free=False)
self.gta.free_sources(distance=rInner, free=True, exclude=['isodiff'])
# Keep closest source if identified with star forming region in catalog or look for new source closest to center within Rinner
if self.target != None:
print('Closest source identified with star forming region : ',
self.target['name'])
self.gta.set_source_morphology('TESTSOURCE',
**{'spatial_model': 'PointSource'})
else:
closeSources = self.gta.find_sources(sqrt_ts_threshold=2.,
min_separation=minSeparation,
max_iter=1,
**{
'search_skydir':
self.gta.roi.skydir,
'search_minmax_radius':
[0., rInner]
})
dCenter = np.array([])
for i in range(len(closeSources['sources'])):
dCenter = np.append(
dCenter,
self.gta.roi.skydir.separation(
closeSources['sources'][i].skydir).value)
self.target = closeSources['sources'][np.argmin(dCenter)]
print('Target name : ', self.target['name'])
self.setSourceName(self.target, 'TESTSOURCE')
for i in [
x for x in range(len(closeSources['sources']))
if x != (np.argmin(dCenter))
]:
self.gta.delete_source(closeSources['sources'][i]['name'])
self.gta.optimize(skip=['isodiff'])
# Initialize n sources array
nSources = []
# Save ROI without extension fit
self.gta.write_roi('nSourcesFit')
if debug == True:
self.gta.make_plots('innerInit')
self.gta.residmap(prefix='innerInit', make_plots=True)
# Test for extension
extensionTest = self.gta.extension('TESTSOURCE',
make_plots=True,
write_npy=debug,
write_fits=debug,
spatial_model='RadialDisk',
update=True,
free_background=True,
fit_position=True)
extLike = extensionTest['loglike_ext']
TSext = extensionTest['ts_ext']
print('TSext : ', TSext)
extAIC = 2 * (len(self.gta.get_free_param_vector()) -
self.gta._roi_data['loglike'])
self.gta.write_roi('extFit')
if debug == True:
self.gta.residmap(prefix='ext0', make_plots=True)
self.gta.make_plots('ext0')
self.gta.load_roi('nSourcesFit', reload_sources=True)
for i in range(1, maxIter + 1):
# Test for n point sources
nSourcesTest = self.gta.find_sources(
sources_per_iter=1,
sqrt_ts_threshold=sqrtTsThreshold,
min_separation=minSeparation,
max_iter=1,
**{
'search_skydir': self.gta.roi.skydir,
'search_minmax_radius': [0., rInner]
})
if len(nSourcesTest['sources']) > 0:
if nSourcesTest['sources'][0]['ts'] > 100.:
self.gta.set_source_spectrum(
nSourcesTest['sources'][0]['name'],
spectrum_type='LogParabola')
self.gta.free_source(nSourcesTest['sources'][0]['name'])
self.gta.fit()
self.gta.free_source(nSourcesTest['sources'][0]['name'],
free=False)
if debug == True:
self.gta.make_plots('nSources' + str(i))
nSources.append(nSourcesTest['sources'])
self.gta.localize(nSourcesTest['sources'][0]['name'],
write_npy=debug,
write_fits=debug,
update=True)
nAIC = 2 * (len(self.gta.get_free_param_vector()) -
self.gta._roi_data['loglike'])
self.gta.free_source(nSourcesTest['sources'][0]['name'],
free=True)
self.gta.residmap(prefix='nSources' + str(i), make_plots=True)
self.gta.write_roi('n1SourcesFit')
# Estimate Akaike Information Criterion difference between both models
dm = extAIC - nAIC
print('AIC difference between both models = ', dm)
# Estimate TS_m+1
extensionTestPlus = self.gta.extension(
'TESTSOURCE',
make_plots=True,
write_npy=debug,
write_fits=debug,
spatial_model='RadialDisk',
update=True,
free_background=True,
fit_position=True)
TSm1 = 2 * (extensionTestPlus['loglike_ext'] - extLike)
print('TSm+1 = ', TSm1)
if debug == True:
self.gta.residmap(prefix='ext' + str(i), make_plots=True)
self.gta.make_plots('ext' + str(i))
if dm < dmMin and TSm1 < TSm1Min:
self.gta.load_roi('extFit', reload_sources=True)
break
else:
# Set extension test to current state and save current extension fit ROI and load previous nSources fit ROI
extensionTest = extensionTestPlus
extLike = extensionTestPlus['loglike_ext']
TSext = extensionTestPlus['ts_ext']
print('TSext : ', TSext)
extAIC = 2 * (len(self.gta.get_free_param_vector()) -
self.gta._roi_data['loglike'])
self.gta.write_roi('extFit')
self.gta.load_roi('n1SourcesFit', reload_sources=True)
self.gta.write_roi('nSourcesFit')
else:
if TSext > TSextMin:
self.gta.load_roi('extFit', reload_sources=True)
break
else:
self.gta.load_roi('nSourcesFit', reload_sources=True)
break
self.gta.fit()
# Get source radius depending on spatial model
endSources = self.gta.get_sources()
for i in range(len(endSources)):
if endSources[i]['name'] == 'TESTSOURCE':
self.target = endSources[i]
self.distance = self.gta.roi.skydir.separation(
endSources[i].skydir).value
if endSources[i].extended == True:
self.targetRadius = endSources[i]['SpatialWidth']
else:
self.targetRadius = endSources[i]['pos_r95']
''' CHECK OVERLAP '''
def overlapDisk(self, rInner, radiusCatalog):
print('Target radius : ', self.targetRadius)
# Check radius sizes
if radiusCatalog < self.targetRadius:
r = float(radiusCatalog)
R = float(self.targetRadius)
else:
r = float(self.targetRadius)
R = float(radiusCatalog)
# Estimating overlapping area
d = self.distance
print('Distance from center : ', d)
if d < (r + R):
if R < (r + d):
area = r**2 * np.arccos(
(d**2 + r**2 - R**2) / (2 * d * r)) + R**2 * np.arccos(
(d**2 + R**2 - r**2) / (2 * d * R)) - 0.5 * np.sqrt(
(-d + r + R) * (d + r - R) * (d - r + R) *
(d + r + R))
overlap = round((area / (np.pi * r**2)) * 100, 2)
else:
area = np.pi * r**2
overlap = 100.0
else:
area = 0.
overlap = 0.
print('Overlapping surface : ', area)
print('Overlap : ', overlap)
if overlap > 68. and self.distance < rInner:
associated = True
else:
associated = False
return associated
''' CHECK UPPER LIMIT '''
def upperLimit(self, name, radius):
sourceModel = {
'SpectrumType': 'PowerLaw',
'Index': 2.0,
'Scale': 30000,
'Prefactor': 1.e-15,
'SpatialModel': 'RadialDisk',
'SpatialWidth': radius,
'glon': self.gta.config['selection']['glon'],
'glat': self.gta.config['selection']['glat']
}
self.gta.add_source(name, sourceModel, free=True)
self.gta.fit()
self.gta.residmap(prefix='upperLimit', make_plots=True)
print('Upper limit : ', self.gta.get_sources()[0]['flux_ul95'])
config['fileio']['outdir'] = cwd+'/fits'
config['fileio']['logfile'] = cwd+'/fits/fermipy.log'
config['data']['ltcube'] = cwd+'/fits/ltcube_00.fits'
config['model']['galdiff'] = path_to_conda+'/share/fermitools/refdata/fermi/galdiffuse/gll_iem_v07.fits'
config['model']['isodiff'] = path_to_conda+'/share/fermitools/refdata/fermi/galdiffuse/iso_P8R3_SOURCE_V3_v1.txt'
config['logging']['verbosity'] = 4
source = config['selection']['target']
with open(cwd+'/config_modified.yaml', 'w') as o:
yaml.dump(config, o)
likelihoods = np.zeros((5))
gta = GTAnalysis(config='config_modified.yaml')
gta.setup()
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
for i in range(1,6):
gta.optimize()
gta.free_sources(free=False)
gta.free_source(source)
gta.free_source('galdiff')
gta.free_source('isodiff')
gta.free_sources(distance=3, pars='norm')
gta.free_sources(minmax_ts=[100, None], pars='norm')
gta.fit(optimizer='NEWMINUIT', reoptimize=True)
maps = gta.residmap(f'../maps/opt_alternating{i}', model=model, make_plots=True)
maps = gta.tsmap(f'../maps/opt_alternating_{i}', model=model, make_plots=True)
gta.write_roi(f'opt_{i}', make_plots=True)
likelihoods[i-1] = - gta.like()
np.savetxt('optimization_process_likes_alternating.dat', likelihoods)
def FGES_BinnedAnalysis(prefix, ANALYSISDIR, numsources, xmlsources, spectrum,
spectrumpoints, spectrumpointsUL, spectrum_mev_or_erg,
spectrum_mev_or_tev, configfile):
ANALYSISDIR = ANALYSISDIR + prefix + '/'
i = numsources #number of sources
sources_names = ''
for x in range(0, i):
sources_names += str(xmlsources[x])
#Run the likelihood analysis up to doing the fit
gta = GTAnalysis(ANALYSISDIR + configfile, logging={'verbosity': 3})
gta.setup()
#Print the pre likelihood fit parameters
gta.print_roi()
for x in range(0, i):
print(gta.roi[xmlsources[x]])
#Do an initial optimization of parameters
gta.optimize()
gta.print_roi()
#Prepare to get the likelihood
#Free the normalizations of sources within 7 degrees of the center of the field of view
gta.free_sources(distance=7.0, pars='norm')
gta.free_source('galdiff')
gta.free_source('isodiff')
for x in range(0, i):
gta.free_source(xmlsources[x])
#LIKELIHOOD ANALYSIS
fit_results = gta.fit()
#print out and return the results
print('Fit Quality: ', fit_results['fit_quality'])
for x in range(0, i):
print(gta.roi[xmlsources[x]])
gta.write_roi(sources_names + 'fit')
#RESIDUAL MAP
model = {'Index': 2.0, 'SpatialModel': 'PointSource'}
maps = gta.residmap('residual', model=model, make_plots=True)
# Generate residual map with source of interest removed from the model
model_nosource = {'Index': 2.0, 'SpatialModel': 'PointSource'}
maps_nosource = gta.residmap('residual_wsource',
model=model_nosource,
exclude=xmlsources,
make_plots=True)
#TS Map
tsmap = gta.tsmap('tsmap',
model={
'SpatialModel': 'PointSource',
'Index': 2.0
},
exclude=xmlsources,
make_plots=True)
tsmap_wSNR = gta.tsmap('tsmap_wSNR',
model={
'SpatialModel': 'PointSource',
'Index': 2.0
},
make_plots=True)
#PLOT SEDs
for x in range(0, i):
c = np.load('10to500gev/' + sources_names + 'fit.npy').flat[0]
sorted(c['sources'].keys())
c['sources'][xmlsources[x]]['flux']
print(c['sources'][xmlsources[x]]['param_names'][:4])
print(c['sources'][xmlsources[x]]['param_values'][:4])
c['sources'][xmlsources[x]]['ts']
E = np.array(c['sources'][xmlsources[x]]['model_flux']['energies'])
dnde = np.array(c['sources'][xmlsources[x]]['model_flux']['dnde'])
dnde_hi = np.array(
c['sources'][xmlsources[x]]['model_flux']['dnde_hi'])
dnde_lo = np.array(
c['sources'][xmlsources[x]]['model_flux']['dnde_lo'])
if spectrum_mev_or_erg == "erg":
suffix = 'erg'
mult = 0.00000160218
elif spectrum_mev_or_erg == "mev":
suffix = 'MeV'
mult = 1
if spectrum_mev_or_tev == "mev":
xaxis = 'MeV'
denominator = 1
elif spectrum_mev_or_tev == "tev":
xaxis = 'TeV'
denominator = 1000000
if spectrum:
plt.loglog(E, (E**2) * dnde, 'k--')
plt.loglog(E, (E**2) * dnde_hi, 'k')
plt.loglog(E, (E**2) * dnde_lo, 'k')
plt.xlabel('E [MeV]')
plt.ylabel(r'E$^2$ dN/dE [MeV cm$^{-2}$ s$^{-1}$]')
plt.savefig('spectrum_' + xmlsources[x] + '.png')
#GET SED POINTS
if spectrumpoints:
sed = gta.sed(xmlsources[x], make_plots=True)
#sed = gta.sed(xmlsource,prefix=xmlsource + 'spectrum',loge_bins=)
src = gta.roi[xmlsources[x]]
#Plot without upper limits
plt.loglog(E, (E**2) * dnde, 'k--')
plt.loglog(E, (E**2) * dnde_hi, 'k')
plt.loglog(E, (E**2) * dnde_lo, 'k')
plt.errorbar(np.array(sed['e_ctr']),
sed['e2dnde'],
yerr=sed['e2dnde_err'],
fmt='o')
plt.xlabel('E [MeV]')
plt.ylabel(r'E$^{2}$ dN/dE [MeV cm$^{-2}$ s$^{-1}$]')
#plt.show()
plt.savefig('spectrumpoints_' + xmlsources[x] + '.png')
#Plot with upper limits, last 5 points
plt.loglog(E, (E**2) * dnde, 'k--')
plt.loglog(E, (E**2) * dnde_hi, 'k')
plt.loglog(E, (E**2) * dnde_lo, 'k')
plt.errorbar(sed['e_ctr'][:-5],
sed['e2dnde'][:-5],
yerr=sed['e2dnde_err'][:-5],
fmt='o')
plt.errorbar(np.array(sed['e_ctr'][-5:]),
sed['e2dnde_ul95'][-5:],
yerr=0.2 * sed['e2dnde_ul95'][-5:],
fmt='o',
uplims=True)
plt.xlabel('E [MeV]')
plt.ylabel(r'E$^{2}$ dN/dE [MeV cm$^{-2}$ s$^{-1}$]')
plt.savefig('spectrumpointsUL_' + xmlsources[x] + '.png')
plt.clf()
def main():
usage = "usage: %(prog)s [config file]"
description = "Run fermipy analysis chain."
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('--config', default='sample_config.yaml')
parser.add_argument('--source', default=None)
args = parser.parse_args()
gta = GTAnalysis(args.config,
logging={'verbosity': 3},
fileio={'workdir_regex': '\.xml$|\.npy$'})
model0 = {'SpatialModel': 'PointSource', 'Index': 1.5}
model1 = {'SpatialModel': 'PointSource', 'Index': 2.0}
model2 = {'SpatialModel': 'PointSource', 'Index': 2.7}
src_name = gta.config['selection']['target']
gta.setup(overwrite=True)
gta.free_sources(False)
gta.print_roi()
gta.optimize()
gta.print_roi()
exclude = []
# Localize all point sources
for s in sorted(gta.roi.sources, key=lambda t: t['ts'], reverse=True):
# for s in gta.roi.sources:
if not s['SpatialModel'] == 'PointSource':
continue
if s['offset_roi_edge'] > -0.1:
continue
if s.name in exclude:
continue
if not '3FGL' in s.name:
continue
if s.name == src_name:
continue
gta.localize(s.name,
nstep=5,
dtheta_max=0.5,
update=True,
prefix='base',
make_plots=True)
gta.optimize()
gta.print_roi()
gta.write_roi('base_roi', make_plots=True)
exclude = [src_name]
if not 'carina_2' in exclude:
exclude += ['carina_2']
if not 'carina_3' in exclude:
exclude += ['carina_3']
gta.tsmap('base', model=model0, make_plots=True, exclude=exclude)
gta.residmap('base', model=model0, make_plots=True, exclude=exclude)
gta.tsmap('base', model=model1, make_plots=True, exclude=exclude)
gta.residmap('base', model=model1, make_plots=True, exclude=exclude)
gta.tsmap('base', model=model2, make_plots=True, exclude=exclude)
gta.residmap('base', model=model2, make_plots=True, exclude=exclude)
gta.find_sources(sqrt_ts_threshold=5.0)
gta.optimize()
gta.print_roi()
gta.print_params()
gta.free_sources(skydir=gta.roi.skydir, distance=1.0, pars='norm')
gta.fit()
gta.print_roi()
gta.print_params()
gta.write_roi('fit0_roi', make_plots=True)
m = gta.tsmap('fit0', model=model0, make_plots=True, exclude=exclude)
gta.plotter.make_tsmap_plots(m, gta.roi, zoom=2, suffix='tsmap_zoom')
gta.residmap('fit0', model=model0, make_plots=True, exclude=exclude)
gta.tsmap('fit0', model=model1, make_plots=True, exclude=exclude)
gta.plotter.make_tsmap_plots(m, gta.roi, zoom=2, suffix='tsmap_zoom')
gta.residmap('fit0', model=model1, make_plots=True, exclude=exclude)
gta.tsmap('fit0', model=model2, make_plots=True, exclude=exclude)
gta.plotter.make_tsmap_plots(m, gta.roi, zoom=2, suffix='tsmap_zoom')
gta.residmap('fit0', model=model2, make_plots=True, exclude=exclude)
gta.sed(src_name, prefix='fit0', make_plots=True, free_radius=1.0)
gta.free_source(src_name)
gta.fit(reoptimize=True)
gta.print_roi()
gta.print_params()
gta.write_roi('fit1_roi', make_plots=True)
