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)
baseline_roi_fit(gta, make_plots=args.make_plots,
minmax_npred=[1e3, np.inf])
localize_sources(gta, nstep=5, dtheta_max=0.5, update=True,
prefix='base', make_plots=args.make_plots)
gta.find_sources(sqrt_ts_threshold=5.0, search_skydir=gta.roi.skydir,
search_minmax_radius=[1.0, np.nan])
gta.optimize()
gta.print_roi()
gta.print_params()
gta.free_sources(skydir=gta.roi.skydir, distance=1.0, pars='norm')
gta.fit(covar=True)
gta.print_roi()
gta.print_params()
gta.write_roi(args.roi_baseline, make_plots=args.make_plots)
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")
gta = GTAnalysis(args.config, logging={'verbosity': 3},
fileio={'workdir_regex': '\.xml$|\.npy$'})
gta.setup(overwrite=False)
gta.free_sources(False)
gta.print_roi()
gta.optimize()
gta.print_roi()
exclude = ['3FGL J1707.8+5626']
# Localize all point sources
for src in sorted(gta.roi.sources, key=lambda t: t['ts'], reverse=True):
# for s in gta.roi.sources:
if not src['SpatialModel'] == 'PointSource':
continue
if src['offset_roi_edge'] > -0.1:
continue
if src.name in exclude:
continue
if not '3FGL' in src.name:
continue
gta.localize(src.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)
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('fit_baseline', make_plots=True)
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_not_null(args.roi_baseline):
gta = GTAnalysis.create(args.roi_baseline, args.config)
else:
gta = GTAnalysis(args.config,
logging={'verbosity': 3},
fileio={'workdir_regex': '\.xml$|\.npy$'})
gta.print_roi()
test_source = args.target
gta.sed(test_source, outfile='sed_%s.fits' % 'FL8Y', make_plots=True)
gta.extension(test_source, make_plots=True)
return gta
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 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)
baseline_roi_fit(gta,
make_plots=args.make_plots,
minmax_npred=[1e3, np.inf])
localize_sources(gta,
nstep=5,
dtheta_max=0.5,
update=True,
prefix='base',
make_plots=args.make_plots)
gta.find_sources(sqrt_ts_threshold=5.0,
search_skydir=gta.roi.skydir,
search_minmax_radius=[1.0, np.nan])
gta.optimize()
gta.print_roi()
gta.print_params()
gta.free_sources(skydir=gta.roi.skydir, distance=1.0, pars='norm')
gta.fit(covar=True)
gta.print_roi()
gta.print_params()
gta.write_roi(args.roi_baseline, make_plots=args.make_plots)
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
def main():
usage = "usage: %(prog)s -c config.yaml"
description = "Run the lc analysis"
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('-c', '--conf', required=True)
parser.add_argument('-i',
required=False,
default=0,
help='Set local or scratch calculation',
type=int)
parser.add_argument('--state',
help='analysis state',
choices=['avgspec', 'setup'],
default='avgspec')
parser.add_argument('--forcepl',
default=0,
help='Force the target source to have power-law shape',
type=int)
parser.add_argument('--createsed',
default=0,
help='Create SED from best fit model',
type=int)
parser.add_argument(
'--adaptive',
default=0,
help='Use adaptive binning for minute scale light curves',
type=int)
parser.add_argument('--srcprob', default = 0,
help='Calculate the source probability for the photons,' \
' only works when no sub orbit time scales are used',
type=int)
parser.add_argument(
'--mincounts',
default=2,
help='Minimum number of counts within LC bin to run analysis',
type=int)
parser.add_argument('--simulate', default = None,
help='None or full path to yaml file which contains src name' \
'and spec to be simulated',
)
parser.add_argument(
'--make_plots',
default=0,
type=int,
help='Create sed plot',
)
parser.add_argument(
'--randomize',
default=1,
help=
'If you simulate, use Poisson realization. If false, use Asimov data set',
type=int)
args = parser.parse_args()
utils.init_logging('DEBUG')
config = yaml.load(open(args.conf))
tmpdir, job_id = lsf.init_lsf()
if not job_id:
job_id = args.i
logging.info('tmpdir: {0:s}, job_id: {1:n}'.format(tmpdir, job_id))
os.chdir(tmpdir) # go to tmp directory
logging.info('Entering directory {0:s}'.format(tmpdir))
logging.info('PWD is {0:s}'.format(os.environ["PWD"]))
# copy the ft1,ft2 and ltcube files
#for k in ['evfile','scfile','ltcube']:
# don't stage them, done automatically by fermipy if needed
# config[k] = utils.copy2scratch(config[k], tmpdir)
# set the scratch directories
logging.debug(config['data'])
config['fileio']['scratchdir'] = tmpdir
# set the log file
logdir = copy.deepcopy(config['fileio']['logfile'])
config['fileio']['logfile'] = path.join(tmpdir, 'fermipy.log')
# debugging: all files will be saved (default is False)
#config['fileio']['savefits'] = True
# if simulating an orbit, save fits files
if args.simulate is not None:
config['fileio']['savefits'] = True
# copy all fits files already present in outdir
# run the analysis
lc_config = copy.deepcopy(config['lightcurve'])
fit_config = copy.deepcopy(config['fit_pars'])
# remove parameters from config file not accepted by fermipy
for k in ['configname', 'tmp', 'log', 'fit_pars']:
config.pop(k, None)
if 'adaptive' in config['lightcurve'].keys():
config['lightcurve'].pop('adaptive', None)
# set the correct time bin
config['selection']['tmin'], config['selection']['tmax'], nj = set_lc_bin(
config['selection']['tmin'],
config['selection']['tmax'],
config['lightcurve']['binsz'],
job_id - 1 if job_id > 0 else 0,
ft1=config['data']['evfile'])
logging.debug('setting light curve bin' + \
'{0:n}, between {1[tmin]:.0f} and {1[tmax]:.0f}'.format(job_id, config['selection']))
if args.adaptive:
config['fileio']['outdir'] = utils.mkdir(
path.join(config['fileio']['outdir'],
'adaptive{0:.0f}/'.format(lc_config['adaptive'])))
if args.state == 'setup':
config['fileio']['outdir'] = utils.mkdir(
path.join(config['fileio']['outdir'],
'setup{0:05n}/'.format(job_id if job_id > 0 else 1)))
else:
config['fileio']['outdir'] = utils.mkdir(
path.join(config['fileio']['outdir'],
'{0:05n}/'.format(job_id if job_id > 0 else 1)))
logging.info('Starting with fermipy analysis')
logging.info('using fermipy version {0:s}'.format(fermipy.__version__))
logging.info('located at {0:s}'.format(fermipy.__file__))
if config['data']['ltcube'] == '':
config['data'].pop('ltcube', None)
compute_sub_gti_lc = False
if type(config['lightcurve']['binsz']) == str:
if len(config['lightcurve']['binsz'].strip('gti')):
compute_sub_gti_lc = True
if config['lightcurve']['binsz'].find('min') > 0:
config['lightcurve']['binsz'] = float(
config['lightcurve']['binsz'].strip('gti').strip(
'min')) * 60.
logging.info("set time bin length to {0:.2f}s".format(
config['lightcurve']['binsz']))
else:
config['lightcurve']['binsz'] = 3. * 3600.
try:
gta = GTAnalysis(config, logging={'verbosity': 3})
except Exception as e:
logging.error("{0}".format(e))
config['selection']['target'] = None
gta = GTAnalysis(config, logging={'verbosity': 3})
sep = gta.roi.sources[0]['offset']
logging.warning(
"Source closets to ROI center is {0:.3f} degree away".format(sep))
if sep < 0.1:
config['selection']['target'] = gta.roi.sources[0]['name']
gta.config['selection']['target'] = config['selection']['target']
logging.info("Set target to {0:s}".format(
config['selection']['target']))
# stage the full time array analysis results to the tmp dir
# do not copy png images
files = [
fn for fn in glob(fit_config['avgspec'])
if fn.find('.xml') > 0 or fn.find('.npy') > 0
]
files += [config['data']['evfile']]
utils.copy2scratch(files, gta.workdir)
# we're using actual data
if args.simulate is None:
# check before the analysis start if there are any events in the master file
# in the specified time range
logging.info('Checking for events in initial ft1 file')
t = Table.read(path.join(gta.workdir,
path.basename(config['data']['evfile'])),
hdu='EVENTS')
logging.info("times in base ft1: {0} {1} {2}".format(
t["TIME"].max(), t["TIME"].min(),
t["TIME"].max() - t["TIME"].min()))
m = (t["TIME"] >= config['selection']['tmin']) & (
t["TIME"] <= config['selection']['tmax'])
if np.sum(m) < args.mincounts + 1:
logging.error(
"*** Only {0:n} events between tmin and tmax! Exiting".format(
np.sum(m)))
assert np.sum(m) > args.mincounts
else:
logging.info("{0:n} events between tmin and tmax".format(
np.sum(m)))
# check how many bins are in each potential light curve bin
if compute_sub_gti_lc:
# select time of first and last
# photon instead of GTI time
m = (t["TIME"] >= config['selection']['tmin']) & \
(t["TIME"] <= config['selection']['tmax'])
tmin = t["TIME"][m].min() - 1.
tmax = t["TIME"][m].max() + 1.
logging.info("There will be up to {0:n} time bins".format(np.ceil(
(tmax - tmin) / \
config['lightcurve']['binsz'])))
bins = np.arange(tmin, tmax, config['lightcurve']['binsz'])
bins = np.concatenate([bins, [config['selection']['tmax']]])
counts = calc_counts(t, bins)
# remove the starting times of the bins with zero counts
# and rebin the data
logging.info("Counts before rebinning: {0}".format(counts))
mincounts = 10.
mc = counts < mincounts
if np.sum(mc):
# remove trailing zeros
if np.any(counts == 0.):
mcounts_post, mcounts_pre = rm_trailing_zeros(counts)
counts = counts[mcounts_post & mcounts_pre]
bins = np.concatenate([
bins[:-1][mcounts_post & mcounts_pre],
[bins[1:][mcounts_post & mcounts_pre].max()]
])
bins = rebin(counts, bins)
logging.info("Bin lengths after rebinning: {0}".format(
np.diff(bins)))
logging.info("Bin times after rebinning: {0}".format(bins))
counts = calc_counts(t, bins)
logging.info("Counts after rebinning: {0}".format(counts))
else:
logging.info("Regular time binning will be used")
bins = list(bins)
logging.info('Running fermipy setup')
try:
gta.setup()
except (RuntimeError, IndexError) as e:
logging.error(
'Caught Runtime/Index Error while initializing analysis object')
logging.error('Printing error:')
logging.error(e)
if e.message.find("File not found") >= 0 and e.message.find(
'srcmap') >= 0:
logging.error("*** Srcmap calculation failed ***")
if e.message.find("NDSKEYS") >= 0 and e.message.find('srcmap') >= 0:
logging.error(
"*** Srcmap calculation failed with NDSKEYS keyword not found in header ***"
)
logging.info("Checking if there are events in ft1 file")
ft1 = path.join(gta.workdir, 'ft1_00.fits')
f = glob(ft1)
if not len(f):
logging.error(
"*** no ft1 file found at location {0:s}".format(ft1))
raise
t = Table.read(f[0], hdu='EVENTS')
if not len(t):
logging.error("*** The ft1 file contains no events!! ***".format(
len(t)))
else:
logging.info("The ft1 file contains {0:n} event(s)".format(len(t)))
return
# end here if you only want to calulate
# intermediate fits files
if args.state == 'setup':
return gta
logging.info('Loading the fit for the average spectrum')
gta.load_roi('avgspec') # reload the average spectral fit
logging.info('Running fermipy optimize and fit')
# we're using actual data
if args.simulate is None:
if args.forcepl:
gta = set_src_spec_pl(
gta, gta.get_source_name(config['selection']['target']))
# to do add EBL absorption at some stage ...
# gta = add_ebl_atten(gta, gta.get_source_name(config['selection']['target']), fit_config['z'])
# make sure you are fitting data
gta.simulate_roi(restore=True)
if compute_sub_gti_lc:
if args.adaptive:
# do import only here since root must be compiled
from fermiAnalysis import adaptivebinning as ab
# compute the exposure
energy = 1000.
texp, front, back = ab.comp_exposure_phi(gta, energy=1000.)
# compute the bins
result = ab.time_bins(
gta,
texp,
0.5 * (front + back),
#critval = 20., # bins with ~20% unc
critval=lc_config['adaptive'],
Epivot=None, # compute on the fly
# tstart = config['selection']['tmin'],
# tstop = config['selection']['tmax']
)
# cut the bins to this GTI
mask = result['tstop'] > config['selection']['tmin']
mask = mask & (result['tstart'] < config['selection']['tmax'])
# try again with catalog values
if not np.sum(mask):
logging.error(
"Adaptive bins outside time window, trying catalog values for flux"
)
result = ab.time_bins(
gta,
texp,
0.5 * (front + back),
critval=lc_config['adaptive'], # bins with ~20% unc
Epivot=None, # compute on the fly
forcecatalog=True,
# tstart = config['selection']['tmin'],
# tstop = config['selection']['tmax']
)
# cut the bins to this GTI
mask = result['tstop'] > config['selection']['tmin']
mask = mask & (result['tstart'] <
config['selection']['tmax'])
if not np.sum(mask):
logging.error(
"Adaptive bins do not cover selected time interval!"
)
logging.error("Using original bins")
else:
bins = np.concatenate((result['tstart'][mask],
[result['tstop'][mask][-1]]))
bins[0] = np.max(
[config['selection']['tmin'], bins[0]])
bins[-1] = np.min(
[config['selection']['tmax'], bins[-1]])
bins = list(bins)
# removing trailing zeros
counts = calc_counts(t, bins)
mcounts_post, mcounts_pre = rm_trailing_zeros(counts)
logging.info(
"count masks: {0} {1}, bins: {2}, counts: {3}".
format(mcounts_post, mcounts_pre, bins, counts))
counts = counts[mcounts_post & mcounts_pre]
bins = np.concatenate([
np.array(bins)[:-1][mcounts_post & mcounts_pre],
[
np.array(bins)[1:][mcounts_post
& mcounts_pre].max()
]
])
logging.info(
"Using bins {0}, total n={1:n} bins".format(
bins,
len(bins) - 1))
logging.info("bins widths : {0}".format(np.diff(bins)))
logging.info("counts per bin: {0} ".format(
calc_counts(t, bins)))
bins = list(bins)
# TODO: test that this is working also with GTIs that have little or no counts
lc = gta.lightcurve(
config['selection']['target'],
binsz=config['lightcurve']['binsz'],
free_background=config['lightcurve']['free_background'],
free_params=config['lightcurve']['free_params'],
free_radius=config['lightcurve']['free_radius'],
make_plots=False,
multithread=True,
nthread=4,
#multithread = False,
#nthread = 1,
save_bin_data=True,
shape_ts_threshold=16.,
use_scaled_srcmap=True,
use_local_ltcube=True,
write_fits=True,
write_npy=True,
time_bins=bins,
outdir='{0:.0f}s'.format(config['lightcurve']['binsz']))
else:
# run the fitting of the entire time and energy range
try:
o = gta.optimize() # perform an initial fit
logging.debug(o)
except RuntimeError as e:
logging.error("Error in optimize: {0}".format(e))
logging.info("Trying to continue ...")
gta = set_free_pars_lc(gta, config, fit_config)
f = gta.fit()
if 'fix_sources' in fit_config.keys():
skip = fit_config['fix_sources'].keys()
else:
skip = []
gta, f = refit(gta,
config['selection']['target'],
f,
fit_config['ts_fixed'],
skip=skip)
gta.print_roi()
gta.write_roi('lc')
if args.createsed:
if args.make_plots:
init_matplotlib_backend()
gta.load_roi('lc') # reload the average spectral fit
logging.info('Running sed for {0[target]:s}'.format(
config['selection']))
sed = gta.sed(config['selection']['target'],
prefix = 'lc_sed',
free_radius = None if config['sed']['free_radius'] == 0. \
else config['sed']['free_radius'],
free_background= config['sed']['free_background'],
free_pars = fa.allnorm,
make_plots = args.make_plots,
cov_scale = config['sed']['cov_scale'],
use_local_index = config['sed']['use_local_index'],
bin_index = config['sed']['bin_index']
)
# debugging: calculate sed and resid maps for each light curve bin
#logging.info('Running sed for {0[target]:s}'.format(config['selection']))
#sed = gta.sed(config['selection']['target'], prefix = 'lc')
#model = {'Scale': 1000., 'Index' : fit_config['new_src_pl_index'], 'SpatialModel' : 'PointSource'}
#resid_maps = gta.residmap('lc',model=model, make_plots=True, write_fits = True, write_npy = True)
if args.srcprob:
logging.info("Running srcprob with srcmdl {0:s}".format('lc'))
gta.compute_srcprob(xmlfile='lc', overwrite=True)
# we are simulating a source
else:
# TODO: I probably have to run the setup here. Do on weekly files, i.e., no time cut? Only do that later?
with open(args.simulate) as f:
simsource = np.load(f, allow_pickle=True).flat[0]
# set the source to the simulation value
gta.set_source_spectrum(
simsource['target'],
spectrum_type=simsource['spectrum_type'],
spectrum_pars=simsource['spectrum_pars'][job_id - 1])
logging.info("changed spectral parameters to {0}".format(
gta.roi.get_source_by_name(simsource['target']).spectral_pars))
# simulate the ROI
gta.simulate_roi(randomize=bool(args.randomize))
gta = set_free_pars_lc(gta, config, fit_config)
# fit the simulation
f = gta.fit()
gta, f = refit(gta, config['selection']['target'], f,
fit_config['ts_fixed'])
gta.print_roi()
gta.write_roi('lc_simulate_{0:s}'.format(simsource['suffix']))
return gta
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)
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')
# -----------------------------------
# Pass 0 - Source at Nominal Position
# -----------------------------------
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
# -------------------------------------
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
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)
f.write(os.path.join(od, 'ft1_00.fits') + '\n')
# modify base config to include merged files
with open(BASE_CONFIG) as infile, \
open(CONFIG_FINAL_FILE, 'w') as outfile:
config = yaml.load(infile)
config['data']['evfile'] = os.path.join(os.getcwd(), FT1_FILES_LIST)
config['data']['ltcube'] = os.path.join(os.getcwd(), LTCUBE_FINAL_FILE)
config['fileio'] = {'outdir': 'out_merged/'}
outfile.write('# Automatically merged from directories:\n')
for outdir in outdirs:
outfile.write('# {}\n'.format(outdir))
outfile.write('\n')
yaml.dump(config, outfile, indent=4)
# some generic processing just for sanity check
gta = GTAnalysis(CONFIG_FINAL_FILE, logging={'verbosity': 3})
gta.setup()
gta.free_source('4FGL J1512.8-0906', free=True, pars=['Index'])
gta.free_source('4FGL J1512.8-0906', free=True, pars='norm')
# Free Normalization of all Sources within 3 deg of ROI center
gta.free_sources(distance=3.0, pars='norm')
# Free all parameters of isotropic and galactic diffuse components
gta.free_source('galdiff')
gta.free_source('isodiff')
gta.optimize()
gta.print_roi()
fit_res = gta.fit()
print('Fit Quality: ', fit_res['fit_quality'])
print(gta.roi['4FGL J1512.8-0906'])
