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)
if args.source is None:
src_name = gta.roi.sources[0].name
gta.setup()
gta.optimize()
loc = gta.localize(src_name, free_radius=1.0, update=True, make_plots=True)
model = {'Index': 2.0, 'SpatialModel': 'PointSource'}
srcs = gta.find_sources(model=model,
sqrt_ts_threshold=5.0,
min_separation=0.5)
sed = gta.sed(src_name, free_radius=1.0, make_plots=True)
gta.tsmap(make_plots=True)
gta.write_roi('fit0')
lc = gta.lightcurve(src_name,
binsz=86400. * 7.0,
free_radius=3.0,
use_scaled_srcmap=True,
multithread=False)
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)
if args.source is None:
src_name = gta.roi.sources[0].name
gta.setup()
gta.optimize()
if (gta.roi[src_name]['ts'] > 1000. and
gta.roi[src_name]['SpectrumType'] == 'PowerLaw'):
gta.set_source_spectrum(src_name, spectrum_type='LogParabola',
spectrum_pars={'beta' : {'value' : 0.0, 'scale' : 1.0,
'min' : 0.0, 'max' : 2.0}})
gta.free_source(src_name)
gta.fit()
gta.free_source(src_name, False)
loc = gta.localize(src_name, free_radius=1.0, update=True, make_plots=True)
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
srcs = gta.find_sources(model=model, sqrt_ts_threshold=5.0,
min_separation=0.5)
sed = gta.sed(src_name, free_radius=1.0, make_plots=True)
gta.tsmap(make_plots=True)
gta.tsmap(prefix='excludeSource', exclude=[src_name], make_plots=True)
gta.write_roi('fit0')
lc = gta.lightcurve(src_name, binsz=86400.*28.0, free_radius=3.0, use_scaled_srcmap=True,
multithread=False)
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)
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)
if args.source is None:
src_name = gta.roi.sources[0].name
gta.setup()
gta.optimize()
loc = gta.localize(src_name, free_radius=1.0, update=True, make_plots=True)
model = {'Index': 2.0, 'SpatialModel': 'PointSource'}
srcs = gta.find_sources(model=model,
sqrt_ts_threshold=5.0,
min_separation=0.5)
sed = gta.sed(src_name, free_radius=1.0, make_plots=True)
gta.tsmap(make_plots=True)
gta.write_roi('fit0')
# make sure bins are shifted to line up with the end of the time window (where the neutrino arrived)
tmax = 528835414
LCbins = tmax - 119 * 28 * 24 * 3600 + numpy.linspace(0, 119,
120) * 28 * 24 * 3600
lc = gta.lightcurve(src_name,
time_bins=list(LCbins),
free_radius=3.0,
use_scaled_srcmap=True,
multithread=False,
shape_ts_threshold=100)
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$|\.npy$'})
gta.setup()
names = [s.name for s in gta.roi.sources if not s.diffuse]
gta.reload_sources(names)
sqrt_ts_threshold=3
model0 = { 'SpatialModel' : 'PointSource', 'Index' : 1.5 }
model1 = { 'SpatialModel' : 'PointSource', 'Index' : 2.0 }
model2 = { 'SpatialModel' : 'PointSource', 'Index' : 2.5 }
#src_name = gta.roi.sources[0].name
if args.source is None:
src_name = gta.config['selection']['target']
else:
src_name = args.source
# -----------------------------------
# Fit the Baseline Model
# -----------------------------------
# Get a reasonable starting point for the spectral model
gta.free_source(src_name)
gta.fit()
gta.free_source(src_name,False)
gta.optimize()
# Localize 3FGL sources
for s in gta.roi.sources:
if not s['SpatialModel'] == 'PointSource':
continue
if s['offset'] < 0.5 or s['ts'] < 25.:
continue
if s['offset_roi_edge'] > -0.1:
continue
gta.localize(s.name,nstep=5,dtheta_max=0.5,update=True,
prefix='base')
gta.free_source(s.name,False)
gta.tsmap('base',model=model1)
# Look for new point sources outside the inner 1.0 deg
gta.find_sources('base',model=model1,
search_skydir=gta.roi.skydir,
max_iter=5,min_separation=0.5,
sqrt_ts_threshold=sqrt_ts_threshold,
search_minmax_radius=[1.0,None])
gta.optimize()
gta.print_roi()
gta.write_roi('base')
# -----------------------------------
# Pass 0 - Source at Nominal Position
# -----------------------------------
fit_region(gta,'fit0',src_name)
# -------------------------------------
# Pass 1 - Source at Localized Position
# -------------------------------------
gta.localize(src_name,nstep=5,dtheta_max=0.5,update=True,
prefix='fit1')
fit_region(gta,'fit1',src_name)
fit_halo(gta,'fit1',src_name)
gta.load_roi('fit1')
# -------------------------------------
# Pass 2 - 2+ Point Sources
# -------------------------------------
srcs = []
# Fit up to 4 sources
for i in range(2,6):
srcs_fit = gta.find_sources('fit%i'%i,
search_skydir=gta.roi.skydir,
max_iter=1,
sources_per_iter=1,
sqrt_ts_threshold=3,
min_separation=0.5,
search_minmax_radius=[None,1.0])
if len(srcs_fit['sources']) == 0:
break
srcs += srcs_fit['sources']
best_fit_idx = i
gta.localize(src_name,nstep=5,dtheta_max=0.4,
update=True,prefix='fit%i'%i)
# Relocalize new sources
for s in sorted(srcs, key=lambda t: t['ts'],reverse=True):
gta.localize(s.name,nstep=5,dtheta_max=0.4,
update=True,prefix='fit%i'%i)
fit_region(gta,'fit%i'%i,src_name)
fit_halo(gta,'fit%i'%i,src_name)
gta.load_roi('fit%i'%i)
new_source_data = []
for s in srcs:
src_data = gta.roi[s.name].data
new_source_data.append(copy.deepcopy(src_data))
np.save(os.path.join(gta.workdir,'new_source_data.npy'),
new_source_data)
if s['offset'] < 0.5 or s['ts'] < 25.:
continue
if np.abs(s['offset_glon']) > 0.5*roiwidth-0.2:
continue
if np.abs(s['offset_glat']) > 0.5*roiwidth-0.2:
continue
gta.localize(s.name,nstep=5,dtheta_max=0.5,update=True,
prefix='base')
gta.free_source(s.name,False)
gta.tsmap('base',model=model1)
#gta.tsmap('base_emin40',model=model1,erange=[4.0,5.5])
# Look for new point sources outside the inner 1.0 deg
gta.find_sources('base',model=model1,
search_skydir=gta.roi.skydir,
max_iter=4,min_separation=0.5,
sqrt_ts_threshold=sqrt_ts_threshold,
search_minmax_radius=[1.0,None])
gta.optimize()
gta.print_roi()
gta.write_roi('base')
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)
gta.setup()
sqrt_ts_threshold = 3
halo_width = np.logspace(-1, 0, 9)
halo_index = np.array([1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0])
model0 = {"SpatialModel": "PointSource", "Index": 1.5}
model1 = {"SpatialModel": "PointSource", "Index": 2.0}
model2 = {"SpatialModel": "PointSource", "Index": 2.5}
src_name = gta.roi.sources[0].name
gta.load_roi("base", reload_sources=True)
# gta.tsmap('base',model=model1)
gta.tsmap("base_emin40", model=model1, erange=[4.0, 5.5])
gta.print_roi()
# -----------------------------------
# Pass 0 - Source at Nominal Position
# -----------------------------------
gta.load_roi("fit0", reload_sources=True)
# fit_region(gta,'fit0',src_name)
fit_region(gta, "fit0_emin40", src_name, erange=[4.0, 5.5])
# -------------------------------------
# Pass 1 - Source at Localized Position
# -------------------------------------
