def main():
usage = "usage: %(prog)s -c config.yaml"
description = "Run the analysis"
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('-c', '--conf', required=True)
parser.add_argument('--halo-template-dir',
required=True,
help='Directory with halo template fits files')
parser.add_argument('--halo-template-suffix',
required=True,
help='suffix for halo template fits fits')
parser.add_argument('--file-suffix',
help='additional suffix for output files',
default='')
parser.add_argument('--state',
default=['avgspec'],
choices=['avgspec', 'avgspec_ebl'],
help='Analysis state')
parser.add_argument('-i',
required=False,
default=0,
help='Set local or scratch calculation',
type=int)
parser.add_argument('--overwrite',
action="store_true",
help='overwrite existing single files')
parser.add_argument('--generate-seds',
action="store_true",
help='Generate SEDs during analysis')
parser.add_argument('--generate-maps',
action="store_true",
help='Generate TS and residual maps during analysis')
args = parser.parse_args()
gta, config, fit_config, job_id = setup.init_gta(args.conf,
i=args.i,
logging_level="INFO")
gta.logger.info('Running fermipy setup')
init_matplotlib_backend()
gta.logger.info('reloading {0:s}'.format(args.state))
gta.load_roi(args.state) # reload the average spectral fit
modelname = "{0:s}_{1:s}{2:s}".format(
args.state, '_'.join([
k for k in args.halo_template_dir.split('/')[-4:]
if not 'spec' in k
]), args.file_suffix)
gta.logger.info("Using modelname: {0:s}".format(modelname))
# change the outdir
# not the greatest that I'm not using the API here,
# but no other possibility
gta._outdir = os.path.join(gta.outdir, 'igmf_' + modelname + '/')
if not os.path.exists(gta.outdir):
os.makedirs(gta.outdir)
gta.logger.info("Set new outdir: {0:s}".format(gta.outdir))
gta.logger.info(
"reloaded ROI had log likelihood value: {0:.2f}".format(-gta.like()))
halo_profile_tied = fit_igmf_halo_scan(
gta,
modelname,
config['selection']['target'],
args.halo_template_dir,
model_idx=job_id,
halo_template_suffix=args.halo_template_suffix,
injection_spectrum='PLSuperExpCutoff',
injection_par2_name='Cutoff',
injection_norm_name='Prefactor',
injection_scale_name='Scale',
index_par_name='Index',
free_bkgs=True,
generate_maps=args.generate_maps,
generate_seds=args.generate_seds,
distance_free_norm=
3., # at 1e-14 G, above 2. deg about 10% of cascade photons are beyond 2 deg at 1GeV
z=fit_config['z'],
ebl_model_name='dominguez',
optimizer='MINUIT')
return gta, halo_profile_tied
def main():
usage = "usage: %(prog)s -c config.yaml"
description = "Run the analysis"
parser = argparse.ArgumentParser(usage=usage,description=description)
parser.add_argument('-c', '--conf', required = True)
parser.add_argument('--state', default = ['avgspec'],
choices = ['avgspec','avgspec_ebl'],
help='Analysis state')
parser.add_argument('-i', required=False, default = 0,
help='Set local or scratch calculation', type=int)
parser.add_argument('--create_ts_maps', required=False, default = 1,
help='Generate TS maps', type=int)
args = parser.parse_args()
gta, config, fit_config, job_id = setup.init_gta(args.conf, i = args.i, logging_level = "INFO")
gta.logger.info('Running fermipy setup')
init_matplotlib_backend()
#files = [fn for fn in glob(fit_config['avgspec']) if fn.find('.xml') > 0 or fn.find('.npy') > 0]
#if len(files):
# utils.copy2scratch(files, gta.workdir)
#else:
# gta.logger.error("No files found in {0:s}".format(fit_config['avgspec']))
gta.setup()
gta.logger.info('reloading {0:s}'.format(args.state))
gta.load_roi(args.state) # reload the average spectral fit
modelname = "{0:s}".format(args.state)
# change the outdir
# not the greatest that I'm not using the API here,
# but no other possibility
gta._outdir = os.path.join(gta.outdir, 'extension_' + modelname + '/')
if not os.path.exists(gta.outdir):
os.makedirs(gta.outdir)
gta.logger.info("Set new outdir: {0:s}".format(gta.outdir))
free_radius_sed = fit_config.get('extension', dict(free_radius_sed=1.)).pop('free_radius_sed', 1.)
force_ps = fit_config.get('extension', dict(force_ps=False)).pop('force_ps', False)
free_shape_target = fit_config.get('extension', dict(free_shape_target=False)).pop('free_shape_target', False)
distance_free_norm = fit_config.get('extension', dict(distance_free_norm=1.5)).pop('distance_free_norm', 1.5)
distance_free_shape = fit_config.get('extension', dict(distance_free_shape=1.)).pop('distance_free_shape', 1.)
halo_fit = fit_config.get('extension', dict(halo_fit=False)).pop('halo_fit', False)
halo_scan = fit_config.get('extension', dict(halo_scan=False)).pop('halo_scan', False)
fit_halo_kwargs = fit_config.get('extension', dict(fit_halo_kwargs={})).pop('fit_halo_kwargs', {})
scan_halo_kwargs = fit_config.get('extension', dict(scan_halo_kwargs={})).pop('scan_halo_kwargs', {})
fit_region(gta, modelname, gta.config['selection']['target'],
loge_bounds=None,
skip_opt=list(fit_config.get('fix_sources', {}).keys()),
shape_ts_threshold=9.0,
force_ps=force_ps,
create_maps=args.create_ts_maps,
create_sed=False,
free_radius_sed=free_radius_sed,
distance_free_norm=distance_free_norm,
distance_free_shape=distance_free_shape,
free_shape_target=free_shape_target,
**fit_config.get('extension', {})
)
if halo_fit:
fit_halo(gta, modelname, gta.config['selection']['target'],
**fit_halo_kwargs
)
if halo_scan:
fit_halo_scan(gta, modelname, gta.config['selection']['target'],
**fit_halo_kwargs
)
return gta, fit_config
def main():
usage = "usage: %(prog)s -c config.yaml"
description = "Run the analysis"
parser = argparse.ArgumentParser(usage=usage,description=description)
parser.add_argument('-c', '--conf', required = True)
parser.add_argument('--overwrite', required=False, default = 0,
help='Overwrite existing files', type=int)
parser.add_argument('--state', default = ['setup'],
choices = ['setup','avgspec','avgspec_ebl','lcbin', 'lcmonthly'],
help='Analysis state')
parser.add_argument('-i', required=False, default = 0,
help='Set local or scratch calculation', type=int)
parser.add_argument('--specin', required=False, default = -2.1,
help='Spectral index used for gtexposure in lieu of target in xml file',
type=float)
parser.add_argument('--addnewsrcs', default = 0,
help='Search for and add new sources and create residual map',
type=int)
parser.add_argument('--reloadfit', default = 0,
help='Reload ROI from avgspec xml file',
type=int)
parser.add_argument('--relocalize', default = 0,
help='Relocalize central source',
type=int)
parser.add_argument('--createsed', default = 0,
help='Create SED from best fit model',
type=int)
parser.add_argument('--forcespec', default = 0,
help='Recompute model parameters',
type=int)
parser.add_argument('--freezesupexp', default = 0,
help='freeze super exponential index parameters',
type=int)
parser.add_argument('--restorecatspec', default = 0,
help='Restore intitial catalog spectrum',
type=int)
parser.add_argument('--sethardexpcutoff', default = 0,
help='Manually change parameters of PL with SuperExpCutoff',
type=int)
parser.add_argument('--pivotE_free', default = 0,
help='let the pivot energy free during fit if spectrum is changed',
type=int)
parser.add_argument('--forcepl', default = 0,
help='Force the target source to have power-law shape',
type=int)
parser.add_argument('--profile2d', default = 0,
help='Compute 2D likelihood surface for PL index and normalization',
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('--psf', default = 0,
help='Calculate the psf',
type=int)
parser.add_argument('--make_plots', default = 0, type=int,
help='Create plots',
)
parser.add_argument('--drm', default = 0,
help='Calculate the detector response matrix',
type=int)
args = parser.parse_args()
gta, config, fit_config, job_id = setup.init_gta(args.conf, i = args.i, logging_level = "INFO")
logging.info('Running fermipy setup')
if args.make_plots:
init_matplotlib_backend()
if args.reloadfit:
files = [fn for fn in glob(fit_config['avgspec']) if fn.find('.xml') > 0 or fn.find('.npy') > 0]
if len(files):
utils.copy2scratch(files, gta.workdir)
else:
logging.error("No files found in {0:s}".format(fit_config['avgspec']))
args.reloadfit = False
if args.state == 'lcbin':
pps = PreparePointSource(config,logging={'verbosity' : 3})
pps.setup()
pps._bin_data_lc(overwrite = args.overwrite)
pps._compute_lc_exp(overwrite = args.overwrite,
specin = args.specin)
return pps
elif args.state == 'setup':
try:
gta.setup()
except RuntimeError as e:
logging.error("setup ended with runtime error:\n{0}.".format(e))
if args.psf:
logging.info("Running psf")
gta.compute_psf(overwrite = True)
if args.drm:
logging.info("Running drm")
gta.compute_drm(overwrite = True)
return None, gta, fit_config
elif args.state.find('avgspec') >= 0:
gta.setup()
if args.psf:
logging.info("Running psf")
gta.compute_psf(overwrite = True)
if args.drm:
logging.info("Running drm")
gta.compute_drm(overwrite = True)
if not type(config['selection']['target']) == str:
# target name not given
# take closest source to ROI center if separation
# is less then 0.1 degree
logging.warning("Target name is {0}".format(config['selection']['target']))
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']
logging.info("Set target to {0:s}".format(config['selection']['target']))
else: # add source at center of ROI
csrc = SkyCoord(ra = config['selection']['ra'],
dec = config['selection']['dec'], frame = 'fk5', unit = 'degree')
if csrc.dec.value < 0.:
sign = '-'
else:
sign = '+'
newname = 'j{0:02.0f}{1:02.0f}{2:s}{3:02.0f}{4:02.0f}'.format(
csrc.ra.hms.h, csrc.ra.hms.m, sign,
np.abs(csrc.dec.dms.d), np.abs(csrc.dec.dms.m))
gta.add_source(newname,{
'ra' : config['selection']['ra'], 'dec' : config['selection']['dec'],
'SpectrumType' : 'PowerLaw', 'Index' : fit_config['new_src_pl_index'],
'Scale' : fit_config['pivotE'] if 'pivotE' in fit_config.keys() else 1000.,
'Prefactor' : 1e-11,
'SpatialModel' : 'PointSource' })
config['selection']['target'] = newname
logging.info("Set target to {0:s}".format(config['selection']['target']))
if args.reloadfit:
# save old spectrum
spec_cat = gta.roi.get_source_by_name(config['selection']['target'])
try:
gta.load_roi(args.state) # reload the average spectral fit
except:
logging.error("Could not reload fit. Continuing anyway.")
logging.info('Running fermipy optimize and fit')
# gives "failed to create spline" in get_parameter_limits function
if 'source_spec' in fit_config.keys():
m = gta.roi.get_source_by_name(config['selection']['target'])
if not m['SpectrumType'] == fit_config['source_spec'] or args.forcespec:
if fit_config['source_spec'] == 'PowerLaw':
gta, _ = set_src_spec_pl(gta, gta.get_source_name(config['selection']['target']),
fit_config['pivotE'] if 'pivotE' in fit_config.keys() else None,
e0_free = args.pivotE_free)
elif fit_config['source_spec'] == 'PLSuperExpCutoff':
gta, _ = set_src_spec_plexpcut(gta, gta.get_source_name(config['selection']['target']),
fit_config['pivotE'] if 'pivotE' in fit_config.keys() else None,
e0_free = args.pivotE_free)
#elif fit_config['source_spec'] == 'LogParabola':
#gta = set_src_spec_lp(gta, gta.get_source_name(config['selection']['target']))
else:
logging.warning("Spectrum {0:s} not supported, spectrum not changed".format(fit_config['source_spec']))
# restore spectrum from catalog
if args.restorecatspec:
#for k in ['alpha','Index','Index1']:
#if k in spec_cat.spectral_pars.keys():
#spec_cat.spectral_pars[k]['value'] -= 0.5
#spec_cat.spectral_pars['Eb']['free'] = True
#spec_cat.spectral_pars['Eb']['value'] = 2000.
#spec_cat.spectral_pars['alpha']['value'] = 1.8
#print spec_cat.spectral_pars
gta.set_source_spectrum(config['selection']['target'],
spectrum_type = spec_cat['SpectrumType'],
spectrum_pars= spec_cat.spectral_pars)
logging.info("restored catalog spectrum")
# for some sources modeled with PL with super exponential cutoff I
# have to do this to get a nice SED, but not for 3C454.3!
if gta.roi.get_source_by_name(config['selection']['target'])['SpectrumType'] ==\
'PLSuperExpCutoff' and args.sethardexpcutoff :
pars = {}
old_spec_pars = copy.deepcopy(gta.roi.get_source_by_name(config['selection']['target']))
for k in ['Prefactor','Scale','Index1','Index2','Cutoff']:
pars[k] = old_spec_pars.spectral_pars[k]
if config['selection']['target'] == '3C454.3':
# values from Romoli et al 2017
pars['Prefactor']['value'] = 4.7
pars['Index1']['value'] = 1.87
pars['Index2']['value'] = 0.4
pars['Cutoff']['value'] = 1100.
pars['Cutoff']['scale'] = 1.
pars['Cutoff']['min'] = 100.
pars['Cutoff']['max'] = 10000.
else:
pars['Index1']['value'] = 1.8
pars['Index2']['value'] = 1.
pars['Cutoff']['value'] = 5e4
gta.set_source_spectrum(config['selection']['target'],
# spectrum_type = 'PLSuperExpCutoff2',
spectrum_type = 'PLSuperExpCutoff',
spectrum_pars= pars)
logging.info("changed spectral parameters to {0}".format(
gta.roi.get_source_by_name(config['selection']['target']).spectral_pars))
else:
old_spec_pars = None
if args.forcepl and not \
gta.roi.get_source_by_name(config['selection']['target'])['SpectrumType'] == 'PowerLaw':
gta, _ = set_src_spec_pl(gta, gta.get_source_name(config['selection']['target']))
args.state += "_pl"
if args.state.find('ebl') >= 0:
gta = fa.utils.add_ebl_atten(gta,config['selection']['target'],fit_config['z'])
gta = set_free_pars_avg(gta, fit_config, freezesupexp = args.freezesupexp)
f,gta = fit_with_retries(gta, fit_config, config['selection']['target'],
alt_spec_pars = old_spec_pars)
logging.debug(f)
try:
get_best_fit_covar(gta, config['selection']['target'], prefix = args.state)
except IndexError:
logging.error("Covariance matrix calculation failed")
#relocalize central source and refit
if args.relocalize and type(config['selection']['target']) == str:
loc = gta.localize(config['selection']['target'], make_plots=args.make_plots,
free_background = fit_config['reloc_bkg'],
free_radius = fit_config['reloc_rad'],
update=True)
logging.info('new position is {0[pos_offset]:.3f} degrees from old position'.format(loc))
logging.info('Pos uncertainty is {0[pos_r68]:.3f} (68%); {0[pos_r95]:.3f} (95%) degrees'.format(loc))
logging.info('Refitting with new source position ...')
logging.info('free source parameters:')
for s in gta.get_sources() :
for k in s.spectral_pars.keys():
if s.spectral_pars[k]['free']:
logging.info('{0:s}: {1:s}'.format(s.name, k))
f = gta.fit()
#f,gta = fit_with_retries(gta, fit_config, config['selection']['target'])
gta.print_roi()
gta.write_roi(args.state)
elif args.state == 'lcmonthly':
gta.setup()
gta.load_roi('avgspec') # reload the average spectral fit
logging.info('Running the 30-day bin' + \
'light curve for {0[target]:s}'.format(config['selection']))
lc = gta.lightcurve(config['selection']['target'],
binsz = 30. * 24. * 60. * 60.)
model = {'Scale': 1000., 'Index' : fit_config['new_src_pl_index'], 'SpatialModel' : 'PointSource'}
if args.addnewsrcs:
gta.load_roi(args.state) # reload the average spectral fit
max_sqrt_ts = 1000.
irun = 0
# define the test source
#model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
# run ts map and add new sources with sqrt(ts) > 5
# reoptimize iteratively for each new source
# this is only done for outer RoI
while max_sqrt_ts >= fit_config['max_sqrt_ts']:
# run ts and residual maps
ts_maps = gta.tsmap(args.state,model=model,
write_fits = True, write_npy = True, make_plots = args.make_plots)
# get the skydirs
#coords = ts_maps['sqrt_ts'].get_pixel_skydirs()
coords = ts_maps['sqrt_ts'].geom.get_coord()
if ts_maps['sqrt_ts'].geom.coordsys == 'CEL':
frame = 'fk5'
elif ts_maps['sqrt_ts'].geom.coordsys == 'GAL':
frame = 'galactic'
c = SkyCoord(coords[0], coords[1], unit = 'deg',
frame = frame)
#sqrt_ts = ts_maps['sqrt_ts'].get_map_values(coords.ra, coords.dec) # these are all nans. workaround: load fits file
#sqrt_ts_map = Map.create_from_fits(path.join(gta.workdir,ts_maps['file']),hdu = 'SQRT_TS_MAP')
#n_map = Map.create_from_fits(path.join(gta.workdir,ts_maps['file']),hdu = 'N_MAP')
sqrt_ts_map = WcsNDMap.read(path.join(gta.workdir,ts_maps['file']),hdu = 'SQRT_TS_MAP')
n_map = WcsNDMap.read(path.join(gta.workdir,ts_maps['file']),hdu = 'N_MAP')
sqrt_ts = sqrt_ts_map.data
amplitudes = n_map.data
# get the angular separation from RoI center
sep = gta.roi.skydir.separation(c)
# mask nan values and pixels close to central source
m = np.isfinite(sqrt_ts) & (sep.value > fit_config['new_src_search_rad'])
if not np.sum(m):
logging.warning('No pixels that are finite at distance > {0[new_src_search_rad]:.2f}'.format(fit_config))
raise RuntimeError
# get max ts value
max_sqrt_ts = np.max(sqrt_ts[m])
if max_sqrt_ts < fit_config['max_sqrt_ts']: break
# get the coords of max ts
idx = np.argmax(sqrt_ts[m])
logging.info('Found new source with sqrt(ts) = {0:.2f} at ra,dec = {1:.2f}, {2:.2f}'.format(
sqrt_ts[m][idx], c.ra[m][idx].value, c.dec[m][idx].value))
# add a new source
csrc = SkyCoord(ra = c.ra[m][idx], dec = c.dec[m][idx], frame = 'fk5')
if csrc.dec.value < 0.:
sign = '-'
else:
sign = '+'
newname = 'j{0:02.0f}{1:02.0f}{2:s}{3:02.0f}{4:02.0f}'.format(
csrc.ra.hms.h, csrc.ra.hms.m, sign,
np.abs(csrc.dec.dms.d), np.abs(csrc.dec.dms.m))
gta.add_source(newname,{
'ra' : c.ra[m][idx].value, 'dec' : c.dec[m][idx].value,
'SpectrumType' : 'PowerLaw', 'Index' : fit_config['new_src_pl_index'],
'Scale' : 1000, 'Prefactor' : amplitudes[m][idx],
'SpatialModel' : 'PointSource' })
logging.debug('Amplitude of source: {0}'.format(amplitudes[m][idx]))
gta.free_source(newname, pars=['norm','index'], free = True)
f = gta.fit()
gta.print_roi()
irun += 1
# if new sources where added, save output
if irun > 0:
gta.print_roi()
# gta = reset_diff_filenames(gta)
# refit the model with new sources present
gta = set_free_pars_avg(gta, fit_config)
f,gta = fit_with_retries(gta, fit_config, config['selection']['target'])
gta.write_roi(args.state)
else:
ts_maps = gta.tsmap(args.state,model=model,
write_fits = True, write_npy = True, make_plots = args.make_plots)
try:
resid_maps = gta.residmap(args.state,model=model, make_plots=args.make_plots, write_fits = True, write_npy = True)
except:
logging.error("Residual map computation and plotting failed")
if args.profile2d:
compute_profile2d(gta, config['selection']['target'], prefix = args.state,
sigma = 5., xsteps = 30, ysteps = 31)
if args.createsed:
if fit_config.get('force_free_index', False):
gta.free_index(config['selection']['target'], free = False)
gta.free_index(config['selection']['target'], free = True)
gta.load_roi(args.state) # reload the average spectral fit
logging.info('Running sed for {0[target]:s}'.format(config['selection']))
sed = gta.sed(config['selection']['target'],
prefix = args.state,
#outfile = 'sed.fits',
#free_radius = #sed_config['free_radius'],
#free_background= #sed_config['free_background'],
#free_pars = fa.allnorm,
make_plots = args.make_plots,
#cov_scale = sed_config['cov_scale'],
#use_local_index = sed_config['use_local_index'],
#use_local_index = True, # sed_config['use_local_index'],
#bin_index = sed_config['bin_index']
)
logging.info("SED covariance: {0}".format(sed['param_covariance']))
# generate additional SEDs
for src in fit_config.get('additional_seds', []):
sed = gta.sed(src,
prefix = args.state,
#outfile = 'sed.fits',
#free_radius = #sed_config['free_radius'],
#free_background= #sed_config['free_background'],
#free_pars = fa.allnorm,
make_plots = args.make_plots,
#cov_scale = sed_config['cov_scale'],
#use_local_index = sed_config['use_local_index'],
#use_local_index = True, # sed_config['use_local_index'],
#bin_index = sed_config['bin_index']
)
if args.srcprob:
logging.info("Running srcprob with srcmdl {0:s}".format(args.state))
gta.compute_srcprob(xmlfile=args.state, overwrite = True)
return f, gta, fit_config
from fermiAnalysis import setup
from fermiAnalysis import xrootd
from fermiAnalysis.utils import set_free_pars_avg, fit_with_retries
from fermiAnalysis.prepare import PreparePointSource
from fermiAnalysis.batchfarm import utils, lsf
if __name__ == '__main__':
usage = "usage: %(prog)s -c config.yaml"
description = "Run the analysis"
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('-c', '--conf', required=True)
parser.add_argument('-i', required=False, default=0, type=int)
args = parser.parse_args()
gta, config, fit_config, job_id = setup.init_gta(args.conf,
i=args.i,
logging_level="INFO")
# start analysis
xrd = xrootd.XrootdSelect()
gta.config['selection']['infile'] = '@' + xrd.create_shortlist(
gta.config['selection']['tmin'], gta.config['selection']['tmax'],
gta.workdir)
gta.config['selection']['rad'] = gta.config['binning']['roiwidth']
gta.config['selection']['outfile'] = gta.config['data']['evfile']
command = xrd.make_cmd_str(**gta.config['selection'])
command = """/afs/slac/g/glast/applications/xrootd/PROD/bin/xrdprel -g gtselect {0:s}""".format(
command)
logging.info('Executing command: {0:s}'.format(command))
def main():
usage = "usage: %(prog)s -c config.yaml"
description = "Run the analysis"
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('-c', '--conf', required=True)
parser.add_argument('--overwrite',
required=False,
default=0,
help='Overwrite existing files',
type=int)
parser.add_argument('--state',
default=['setup'],
choices=[
'setup', 'lcbinexp', 'avgspec', 'tsresid',
'avgsed', 'lcbin', 'srcprob'
],
help='Analysis state')
parser.add_argument('-i',
required=False,
default=0,
help='Set local or scratch calculation',
type=int)
parser.add_argument(
'--dt',
required=False,
default=0.,
help=
'time interval for light curve binning, if none, use binning of config file',
type=float)
parser.add_argument(
'--specin',
required=False,
default=-2.1,
help='Spectral index used for gtexposure in lieu of target in xml file',
type=float)
parser.add_argument(
'--targetname',
required=False,
default="",
help='Source name used for binned light curve exposure calculation')
parser.add_argument(
'--srcmdl',
required=False,
default="srcmdl",
help='srcmdl name for gtsrcprob / gtexposure calculation')
args = parser.parse_args()
gta, config, fit_config, job_id = setup.init_gta(args.conf,
i=args.i,
logging_level="INFO")
logging.info('Running fermipy setup')
pps = PreparePointSource(config, logging={'verbosity': 3})
if args.state == 'setup':
pps.setup()
return pps
if args.state == 'lcbinexp':
pps._bin_data_lc(overwrite=args.overwrite, dtime=args.dt)
if 'target' in config['selection'].keys():
target = pps.roi.sources[0].name # assume central source
files = [
fn for fn in glob(fit_config['avgspec']) if fn.find('.xml') > 0
]
utils.copy2scratch(files, gta.workdir)
else:
target = args.targetname
logging.info(
"Running lc bin with target {0:s} and specin {1:.2f}".format(
target, args.specin))
pps._compute_lc_exp(overwrite=args.overwrite,
specin=args.specin,
target=target,
srcmdl=args.srcmdl)
return pps
if args.state == 'srcprob':
# copy srcmodels of average fit
files = [
fn for fn in glob(fit_config['avgspec']) if fn.find('.xml') > 0
]
utils.copy2scratch(files, gta.workdir)
logging.info("Running diffrsp with srcmdl {0:s}".format(args.srcmdl))
pps._compute_diffrsp(args.srcmdl, overwrite=args.overwrite)
logging.info("Running srcprob with srcmdl {0:s}".format(args.srcmdl))
pps._compute_srcprob(args.srcmdl, overwrite=args.overwrite)
return pps
elif args.state == 'avgspec':
gta.setup()
logging.info('Running fermipy optimize and fit')
# run the fitting of the entire time and energy range
o = gta.optimize() # perform an initial fit
logging.debug(o)
gta.print_roi()
# Free all parameters of all Sources within X deg of ROI center
#gta.free_sources(distance=fit_config['ps_dist_all'])
# Free Normalization of all Sources within X deg of ROI center
gta.free_sources(distance=fit_config['ps_dist_norm'], pars=fa.allnorm)
# Free spectra parameters of all Sources within X deg of ROI center
gta.free_sources(distance=fit_config['ps_dist_idx'], pars=fa.allidx)
# Free all parameters of isotropic and galactic diffuse components
gta.free_source('galdiff',
pars='norm',
free=fit_config['gal_norm_free'])
gta.free_source('galdiff',
pars=['index'],
free=fit_config['gal_idx_free'])
gta.free_source('isodiff',
pars='norm',
free=fit_config['iso_norm_free'])
# Free sources with TS > X
gta.free_sources(minmax_ts=[fit_config['ts_norm'], None],
pars=fa.allnorm)
# Fix sources with TS < Y
gta.free_sources(minmax_ts=[None, fit_config['ts_fixed']],
free=False,
pars=fa.allnorm + fa.allidx)
# Fix sources Npred < Z
gta.free_sources(minmax_npred=[None, fit_config['npred_fixed']],
free=False,
pars=fa.allnorm + fa.allidx)
# gives "failed to create spline" in get_parameter_limits function
#gta = fa.utils.add_ebl_atten(gta,config['selection']['target'],fit_config['z'])
f = gta.fit()
#logging.debug(f)
#relocalize central source and refit
loc = gta.localize(config['selection']['target'],
make_plots=True,
free_background=fit_config['reloc_bkg'],
free_radius=fit_config['reloc_rad'],
update=True)
logging.info(
'new position is {0[pos_offset]:.3f} degrees from old position'.
format(loc))
logging.info(
'Pos uncertainty is {0[pos_r68]:.3f} (68%); {0[pos_r95]:.3f} (95%) degrees'
.format(loc))
logging.info('Refitting with new source position ...')
logging.info('free source parameters:')
for s in gta.get_sources():
for k in s.spectral_pars.keys():
if s.spectral_pars[k]['free']:
logging.info('{0:s}: {1:s}'.format(s.name, k))
f = gta.fit()
gta.print_roi()
gta.write_roi(args.state)
if args.state == 'tsresid':
gta.setup()
gta.load_roi('avgspec') # reload the average spectral fit
max_sqrt_ts = 1000.
irun = 0
# define the test source
model = {
'Scale': 1000.,
'Index': fit_config['new_src_pl_index'],
'SpatialModel': 'PointSource'
}
#model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
# run ts map and add new sources with sqrt(ts) > 5
# reoptimize iteratively for each new source
# this is only done for outer RoI
while max_sqrt_ts >= fit_config['max_sqrt_ts']:
# run ts and residual maps
ts_maps = gta.tsmap('avgspec',
model=model,
write_fits=True,
write_npy=True,
make_plots=True)
# get the skydirs
coords = ts_maps['sqrt_ts'].get_pixel_skydirs()
#sqrt_ts = ts_maps['sqrt_ts'].get_map_values(coords.ra, coords.dec) # these are all nans. workaround: load fits file
sqrt_ts_map = Map.create_from_fits(path.join(
gta.workdir, ts_maps['file']),
hdu='SQRT_TS_MAP')
n_map = Map.create_from_fits(path.join(gta.workdir,
ts_maps['file']),
hdu='N_MAP')
sqrt_ts = sqrt_ts_map.get_map_values(coords.ra, coords.dec)
amplitudes = n_map.get_map_values(coords.ra, coords.dec)
# get the angular separation from RoI center
sep = gta.roi.skydir.separation(coords)
# mask nan values and pixels close to central source
m = np.isfinite(sqrt_ts) & (sep.value >
fit_config['new_src_search_rad'])
if not np.sum(m):
logging.warning(
'No pixels that are finite at distance > {0[new_src_search_rad]:.2f}'
.format(fit_config))
raise RuntimeError
# get max ts value
max_sqrt_ts = np.max(sqrt_ts[m])
if max_sqrt_ts < fit_config['max_sqrt_ts']: break
# get the coords of max ts
idx = np.argmax(sqrt_ts[m])
logging.info(
'Found new source with sqrt(ts) = {0:.2f} at ra,dec = {1:.2f}, {2:.2f}'
.format(sqrt_ts[m][idx], coords.ra[m][idx].value,
coords.dec[m][idx].value))
# add a new source
c = SkyCoord(ra=coords.ra[m][idx],
dec=coords.dec[m][idx],
frame='icrs')
if c.dec.value < 0.:
sign = '-'
else:
sign = '+'
newname = 'j{0:02.0f}{1:02.0f}{2:s}{3:02.0f}{4:02.0f}'.format(
c.ra.hms.h, c.ra.hms.m, sign, np.abs(c.dec.dms.d),
np.abs(c.dec.dms.m))
gta.add_source(
newname, {
'ra': coords.ra[m][idx].value,
'dec': coords.dec[m][idx].value,
'SpectrumType': 'PowerLaw',
'Index': fit_config['new_src_pl_index'],
'Scale': 1000,
'Prefactor': amplitudes[m][idx],
'SpatialModel': 'PointSource'
})
logging.debug('Amplitude of source: {0}'.format(
amplitudes[m][idx]))
gta.free_source(newname, pars=['norm', 'index'], free=True)
f = gta.fit()
gta.print_roi()
irun += 1
resid_maps = gta.residmap('avgspec',
model=model,
make_plots=True,
write_fits=True,
write_npy=True)
# if new sources where added, save output
if irun > 0:
gta.print_roi()
# gta = reset_diff_filenames(gta)
gta.write_roi('avgspec')
if args.state == 'avgsed':
gta.setup()
gta.load_roi('avgspec') # reload the average spectral fit
logging.info('Running sed for {0[target]:s}'.format(
config['selection']))
sed = gta.sed(
config['selection']['target'],
#outfile = 'sed.fits',
#free_radius = sed_config['free_radius'],
#free_background= sed_config['free_background'],
#make_plots = sed_config['make_plots'],
#cov_scale = sed_config['cov_scale'],
#use_local_index = sed_config['use_local_index'],
#bin_index = sed_config['bin_index']
)
if args.state == 'lcmonthly':
gta.setup()
gta.load_roi('avgspec') # reload the average spectral fit
logging.info(
'Running the 30-day bin light curve for {0[target]:s}'.format(
config['selection']))
lc = gta.lightcurve(config['selection']['target'],
binsz=30. * 24. * 60. * 60.)
# run the analysis for the full flare durations
#if args.state == 'fullflare-avg':
if args.state.find('-avg') >= 0:
# 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
]
utils.copy2scratch(files, gta.workdir)
if args.state == 'fullflare-avg':
gta.load_roi('avgspec') # reload the average spectral fit
elif args.state == 'gtiflare-avg':
gta.load_roi('fullflare-avg') # reload the average spectral fit
o = gta.optimize() # perform an initial fit
logging.debug(o)
gta.print_roi()
# Free all parameters of all Sources within X deg of ROI center
#gta.free_sources(distance=fit_config['ps_dist_all'])
# Free Normalization of all Sources within X deg of ROI center
gta.free_sources(distance=fit_config['ps_dist_norm_fullflare'],
pars=fa.allnorm)
# Free spectra parameters of all Sources within X deg of ROI center
gta.free_sources(distance=fit_config['ps_dist_idx_fullflare'],
pars=fa.allidx)
# Free all parameters of isotropic and galactic diffuse components
gta.free_source('galdiff',
pars=fa.allnorm,
free=fit_config['gal_norm_free_fullflare'])
gta.free_source('galdiff',
pars=fa.allidx,
free=fit_config['gal_idx_free_fullflare'])
gta.free_source('isodiff',
pars=fa.allnorm,
free=fit_config['iso_norm_free_fullflare'])
# Free sources with TS > X
gta.free_sources(minmax_ts=[fit_config['ts_norm'], None],
pars=fa.allnorm)
# Fix sources with TS < Y
gta.free_sources(minmax_ts=[None, fit_config['ts_fixed']],
free=False,
pars=fa.allidx + fa.allnorm)
# Fix indeces for sources with TS < Z
gta.free_sources(minmax_ts=[None, fit_config['ts_fixed_idx']],
free=False,
pars=fa.allidx)
# Fix sources Npred < Z
gta.free_sources(minmax_npred=[None, fit_config['npred_fixed']],
free=False,
pars=fa.allidx + fa.allnorm)
# gives "failed to create spline" in get_parameter_limits function
#gta = fa.utils.add_ebl_atten(gta,config['selection']['target'],fit_config['z'])
logging.info('free source parameters:')
for s in gta.get_sources():
for k in s.spectral_pars.keys():
if s.spectral_pars[k]['free']:
logging.info('{0:s}: {1:s}'.format(s.name, k))
f = gta.fit()
retries = f['config']['retries']
tsfix = fit_config['ts_fixed']
while not f['fit_success'] and retries > 0:
gta.free_source(config['selection']['target'],
pars=['beta', 'Index2'],
free=False)
# Fix more sources
tsfix *= 3
gta.free_sources(minmax_ts=[None, tsfix],
free=False,
pars=fa.allidx + fa.allnorm)
logging.info("retrying fit")
for s in gta.get_sources():
for k in s.spectral_pars.keys():
if s.spectral_pars[k]['free']:
logging.info('{0:s}: {1:s}'.format(s.name, k))
o = gta.optimize()
gta.print_roi()
f = gta.fit()
retries -= 1
gta.write_roi(args.state)
logging.info('Running sed for {0[target]:s}'.format(
config['selection']))
sed = gta.sed(config['selection']['target'], prefix=args.state)
model = {
'Scale': 1000.,
'Index': fit_config['new_src_pl_index'],
'SpatialModel': 'PointSource'
}
resid_maps = gta.residmap(args.state,
model=model,
make_plots=True,
write_fits=True,
write_npy=True)
return f, gta