def run_analysis(self, argv):
"""Run this analysis"""
args = self._parser.parse_args(argv)
if not HAVE_ST:
raise RuntimeError("Trying to run fermipy analysis, but don't have ST")
gta = GTAnalysis(args.config,
logging={'verbosity': 3},
fileio={'workdir_regex': '\.xml$|\.npy$'})
gta.setup(overwrite=False)
gta.load_roi('fit_baseline')
gta.print_roi()
basedir = os.path.dirname(args.config)
# This should be a no-op, b/c it was done in the baseline analysis
gta.free_sources(skydir=gta.roi.skydir, distance=1.0, pars='norm')
for profile in args.profiles:
pkey, pdict = SEDAnalysis._build_profile_dict(basedir, profile)
# test_case need to be a dict with spectrum and morphology
gta.add_source(pkey, pdict)
# refit the ROI
gta.fit()
# build the SED
gta.sed(pkey, outfile="sed_%s.fits" % pkey)
# remove the source
gta.delete_source(pkey)
# put the ROI back to how it was
gta.load_xml('fit_baseline')
return gta
def run_analysis(self, argv):
"""Run this analysis"""
args = self._parser.parse_args(argv)
if not HAVE_ST:
raise RuntimeError(
"Trying to run fermipy analysis, but don't have ST")
if is_null(args.skydirs):
skydir_dict = None
else:
skydir_dict = load_yaml(args.skydirs)
gta = GTAnalysis(args.config,
logging={'verbosity': 3},
fileio={'workdir_regex': '\.xml$|\.npy$'})
#gta.setup(overwrite=False)
gta.load_roi(args.roi_baseline)
gta.print_roi()
basedir = os.path.dirname(args.config)
# This should be a no-op, b/c it was done in the baseline analysis
for profile in args.profiles:
if skydir_dict is None:
skydir_keys = [None]
else:
skydir_keys = sorted(skydir_dict.keys())
for skydir_key in skydir_keys:
if skydir_key is None:
pkey, pdict = AnalyzeSED._build_profile_dict(
basedir, profile)
else:
skydir_val = skydir_dict[skydir_key]
pkey, pdict = AnalyzeSED._build_profile_dict(
basedir, profile)
pdict['ra'] = skydir_val['ra']
pdict['dec'] = skydir_val['dec']
pkey += "_%06i" % skydir_key
outfile = "sed_%s.fits" % pkey
# Add the source and get the list of correlated soruces
correl_dict = add_source_get_correlated(gta,
pkey,
pdict,
correl_thresh=0.25)
# Write the list of correlated sources
correl_yaml = os.path.join(basedir, "correl_%s.yaml" % pkey)
write_yaml(correl_dict, correl_yaml)
gta.free_sources(False)
for src_name in correl_dict.keys():
gta.free_source(src_name, pars='norm')
# build the SED
gta.sed(pkey, outfile=outfile, make_plots=args.make_plots)
# remove the source
gta.delete_source(pkey)
# put the ROI back to how it was
gta.load_xml(args.roi_baseline)
return gta
# 'SpatialWidth' : 1.0
# }
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)
# 'SpatialWidth' : 1.0
# }
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(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'])]
def run_analysis(self, argv):
"""Run this analysis"""
args = self._parser.parse_args(argv)
if not HAVE_ST:
raise RuntimeError(
"Trying to run fermipy analysis, but don't have ST")
if is_null(args.skydirs):
skydir_dict = None
else:
skydir_dict = load_yaml(args.skydirs)
gta = GTAnalysis(args.config,
logging={'verbosity': 3},
fileio={'workdir_regex': '\.xml$|\.npy$'})
#gta.setup(overwrite=False)
gta.load_roi(args.roi_baseline)
gta.print_roi()
basedir = os.path.dirname(args.config)
# This should be a no-op, b/c it was done in the baseline analysis
for profile in args.profiles:
if skydir_dict is None:
skydir_keys = [None]
else:
skydir_keys = sorted(skydir_dict.keys())
for skydir_key in skydir_keys:
if skydir_key is None:
pkey, psrc_name, pdict = build_profile_dict(basedir, profile)
else:
skydir_val = skydir_dict[skydir_key]
pkey, psrc_name, pdict = build_profile_dict(basedir, profile)
pdict['ra'] = skydir_val['ra']
pdict['dec'] = skydir_val['dec']
pkey += "_%06i" % skydir_key
outfile = "sed_%s.fits" % pkey
# Add the source and get the list of correlated soruces
correl_dict, test_src_name = add_source_get_correlated(gta, psrc_name,
pdict, correl_thresh=0.25,
non_null_src=args.non_null_src)
# Write the list of correlated sources
correl_yaml = os.path.join(basedir, "correl_%s.yaml" % pkey)
write_yaml(correl_dict, correl_yaml)
gta.free_sources(False)
for src_name in correl_dict.keys():
gta.free_source(src_name, pars='norm')
# build the SED
if args.non_null_src:
gta.update_source(test_src_name, reoptimize=True)
gta.write_roi("base_%s"% pkey, make_plots=False)
gta.sed(test_src_name, prefix=pkey, outfile=outfile, make_plots=args.make_plots)
# remove the source
gta.delete_source(test_src_name)
# put the ROI back to how it was
gta.load_xml(args.roi_baseline)
return gta
