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
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
