def _process_lc_bin(itime, name, config, basedir, workdir, diff_sources, const_spectrum, roi, lck_params,
**kwargs):
i, time = itime
roi = copy.deepcopy(roi)
config = copy.deepcopy(config)
config['selection']['tmin'] = time[0]
config['selection']['tmax'] = time[1]
# create output directories labeled in MET vals
outdir = basedir + 'lightcurve_%.0f_%.0f' % (time[0], time[1])
config['fileio']['outdir'] = os.path.join(workdir, outdir)
config['logging']['prefix'] = 'lightcurve_%.0f_%.0f ' % (time[0], time[1])
config['fileio']['logfile'] = os.path.join(config['fileio']['outdir'],
'fermipy.log')
utils.mkdir(config['fileio']['outdir'])
yaml.dump(utils.tolist(config),
open(os.path.join(config['fileio']['outdir'],
'config.yaml'), 'w'))
xmlfile = os.path.join(config['fileio']['outdir'], 'base.xml')
try:
from fermipy.gtanalysis import GTAnalysis
gta = GTAnalysis(config, roi, loglevel=logging.DEBUG)
gta.logger.info('Fitting time range %i %i' % (time[0], time[1]))
gta.setup()
except:
print('Analysis failed in time range %i %i' %
(time[0], time[1]))
print(sys.exc_info()[0])
raise
return {}
gta._lck_params = lck_params
# Recompute source map for source of interest and sources within 3 deg
if gta.config['gtlike']['use_scaled_srcmap']:
names = [s.name for s in
gta.roi.get_sources(distance=3.0, skydir=gta.roi[name].skydir)
if not s.diffuse]
gta.reload_sources(names)
# Write the current model
gta.write_xml(xmlfile)
# Optimize the model
gta.optimize(skip=diff_sources,
shape_ts_threshold=kwargs.get('shape_ts_threshold'))
fit_results = _fit_lc(gta, name, **kwargs)
gta.write_xml('fit_model_final.xml')
srcmodel = copy.deepcopy(gta.get_src_model(name))
numfree = gta.get_free_param_vector().count(True)
max_ts_thresholds = [None, 4, 9]
for max_ts in max_ts_thresholds:
if max_ts is not None:
gta.free_sources(minmax_ts=[None, max_ts], free=False, exclude=[name])
# rerun fit using params from full time (constant) fit using same
# param vector as the successful fit to get loglike
specname, spectrum = const_spectrum
gta.set_source_spectrum(name, spectrum_type=specname,
spectrum_pars=spectrum,
update_source=False)
gta.free_source(name, free=False)
const_fit_results = gta.fit()
if not const_fit_results['fit_success']:
continue
const_srcmodel = gta.get_src_model(name)
# rerun using shape fixed to full time fit
# for the fixed-shape lightcurve
gta.free_source(name, pars='norm')
fixed_fit_results = gta.fit()
if not fixed_fit_results['fit_success']:
continue
fixed_srcmodel = gta.get_src_model(name)
break
# special lc output
o = {'flux_const': const_srcmodel['flux'],
'loglike_const': const_fit_results['loglike'],
'fit_success': fit_results['fit_success'],
'fit_success_fixed': fixed_fit_results['fit_success'],
'fit_quality': fit_results['fit_quality'],
'fit_status': fit_results['fit_status'],
'num_free_params': numfree,
'config': config}
# full flux output
if fit_results['fit_success'] == 1:
for k in defaults.source_flux_output.keys():
if not k in srcmodel:
continue
o[k] = srcmodel[k]
o[k+'_fixed'] = fixed_srcmodel[k]
gta.logger.info('Finished time range %i %i' % (time[0], time[1]))
return o
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'])
def _process_lc_bin(itime, name, config, basedir, workdir, diff_sources, const_spectrum, roi, lck_params,
**kwargs):
i, time = itime
roi = copy.deepcopy(roi)
config = copy.deepcopy(config)
config['selection']['tmin'] = time[0]
config['selection']['tmax'] = time[1]
# create output directories labeled in MET vals
outdir = basedir + 'lightcurve_%.0f_%.0f' % (time[0], time[1])
config['fileio']['outdir'] = os.path.join(workdir, outdir)
config['logging']['prefix'] = 'lightcurve_%.0f_%.0f ' % (time[0], time[1])
config['fileio']['logfile'] = os.path.join(config['fileio']['outdir'],
'fermipy.log')
utils.mkdir(config['fileio']['outdir'])
yaml.dump(utils.tolist(config),
open(os.path.join(config['fileio']['outdir'],
'config.yaml'), 'w'))
xmlfile = os.path.join(config['fileio']['outdir'], 'base.xml')
try:
from fermipy.gtanalysis import GTAnalysis
gta = GTAnalysis(config, roi, loglevel=logging.DEBUG)
gta.logger.info('Fitting time range %i %i' % (time[0], time[1]))
gta.setup()
except:
print('Analysis failed in time range %i %i' %
(time[0], time[1]))
print(sys.exc_info()[0])
raise
return {}
gta._lck_params = lck_params
# Recompute source map for source of interest and sources within 3 deg
if gta.config['gtlike']['use_scaled_srcmap']:
names = [s.name for s in
gta.roi.get_sources(distance=3.0, skydir=gta.roi[name].skydir)
if not s.diffuse]
gta.reload_sources(names)
# Write the current model
gta.write_xml(xmlfile)
# Optimize the model
gta.optimize(skip=diff_sources,
shape_ts_threshold=kwargs.get('shape_ts_threshold'))
fit_results = _fit_lc(gta, name, **kwargs)
gta.write_xml('fit_model_final.xml')
srcmodel = copy.deepcopy(gta.get_src_model(name))
numfree = gta.get_free_param_vector().count(True)
const_srcmodel = gta.get_src_model(name).copy()
fixed_fit_results = fit_results.copy()
fixed_srcmodel = gta.get_src_model(name).copy()
fixed_fit_results['fit_success'],fixed_srcmodel['fit_success'] = [False,False]
fixed_fit_results['fit_quality'],fixed_srcmodel['fit_quality'] = [0,0]
max_ts_thresholds = [None, 4, 9, 16, 25]
for max_ts in max_ts_thresholds:
if max_ts is not None:
gta.free_sources(minmax_ts=[None, max_ts], free=False, exclude=[name])
# rerun fit using params from full time (constant) fit using same
# param vector as the successful fit to get loglike
specname, spectrum = const_spectrum
gta.set_source_spectrum(name, spectrum_type=specname,
spectrum_pars=spectrum,
update_source=False)
gta.free_source(name, free=False)
const_fit_results = gta.fit()
if not const_fit_results['fit_success']:
continue
const_srcmodel = gta.get_src_model(name)
# rerun using shape fixed to full time fit
# for the fixed-shape lightcurve
gta.free_source(name, pars='norm')
fixed_fit_results = gta.fit()
if not fixed_fit_results['fit_success']:
continue
fixed_srcmodel = gta.get_src_model(name)
break
# special lc output
o = {'flux_const': const_srcmodel['flux'],
'loglike_const': const_fit_results['loglike'],
'fit_success': fit_results['fit_success'],
'fit_success_fixed': fixed_fit_results['fit_success'],
'fit_quality': fit_results['fit_quality'],
'fit_status': fit_results['fit_status'],
'num_free_params': numfree,
'config': config}
# full flux output
if fit_results['fit_success'] == 1:
for k in defaults.source_flux_output.keys():
if not k in srcmodel:
continue
o[k] = srcmodel[k]
o[k+'_fixed'] = fixed_srcmodel[k]
gta.logger.info('Finished time range %i %i' % (time[0], time[1]))
return o