gta = GTAnalysis(args.config, logging={"verbosity": 3})
gta.setup()
gta.simulate_roi(restore=True)
ext_fit_data = []
halo_fit_data = []
gta.write_roi("base_model", save_model_map=False, make_plots=False)
for i in range(1):
gta.load_xml("base_model")
gta.simulate_roi(randomize=False)
gta.free_source("testsource")
gta.update_source("testsource")
# gta.fit()
gta.free_sources(free=False)
gta.extension("testsource", width=np.logspace(-2.5, -0.5, 9))
ext_fit_data += [copy.deepcopy(gta.roi["testsource"])]
gta.write_roi("fit%04i" % i, save_model_map=False, make_plots=False, format="npy")
fit_halo(gta, "fit%04i" % i, "testsource", halo_width, halo_index)
np.save(os.path.join(gta._savedir, "ext_fit_data.npy"), ext_fit_data)
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)
ext_fit_data = []
halo_fit_data = []
gta.write_roi('base_model',save_model_map=False,make_plots=False)
for i in range(10):
gta.load_xml('base_model')
gta.simulate_roi()
gta.free_source('testsource')
gta.free_source('galdiff',pars='norm')
gta.free_source('isodiff',pars='norm')
gta.fit()
gta.update_source('testsource',reoptimize=True,npts=9)
gta.free_sources(free=False)
gta.extension('testsource',width=np.logspace(-2.5,-0.5,9))
ext_fit_data += [copy.deepcopy(gta.roi['testsource'])]
gta.write_roi('fit%04i'%i,save_model_map=False,make_plots=False,
format='npy')
fit_halo(gta,'fit%04i'%i,'testsource',halo_width,halo_index)
np.save(os.path.join(gta._savedir,'ext_fit_data.npy'),ext_fit_data)
# -----------------------------------
fit_region(gta,'fit0',src_name)
#fit_region(gta,'fit0_emin40',src_name,erange=[4.0,5.5])
gta.load_roi('fit0')
# -------------------------------------
# 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,halo_width,halo_index)
#fit_region(gta,'fit1_emin40',src_name,erange=[4.0,5.5])
#fit_halo(gta,'fit1_emin40',src_name,halo_width,halo_index,erange=[4.0,5.5])
gta.load_roi('fit1')
# -------------------------------------
# Pass 2 - 2+ Point Sources
# -------------------------------------
srcs = []
best_fit_idx = 1
# Fit up to 4 sources
for i in range(2,5):
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
# -------------------------------------
gta.load_roi("fit1", reload_sources=True)
# fit_region(gta,'fit1',src_name)
# fit_halo(gta,'fit1',src_name,halo_width,halo_index)
fit_region(gta, "fit1_emin40", src_name, erange=[4.0, 5.5])
fit_halo(gta, "fit1_emin40", src_name, halo_width, halo_index, erange=[4.0, 5.5])
# -------------------------------------
# Pass 2 - 2+ Point Sources
# -------------------------------------
best_fit_idx = 1
# Fit up to 4 sources
for i in range(2, 5):
roi_file = "fit%i.npy" % i
if not os.path.isfile(os.path.join(gta.workdir, roi_file)):
continue
