def _localize(self, name, **kwargs):
nstep = kwargs.get('nstep')
dtheta_max = kwargs.get('dtheta_max')
update = kwargs.get('update', True)
prefix = kwargs.get('prefix', '')
use_cache = kwargs.get('use_cache', False)
free_background = kwargs.get('free_background', False)
free_radius = kwargs.get('free_radius', None)
fix_shape = kwargs.get('fix_shape', False)
saved_state = LikelihoodState(self.like)
loglike_init = -self.like()
self.logger.debug('Initial Model Log-Likelihood: %f', loglike_init)
if not free_background:
self.free_sources(free=False, loglevel=logging.DEBUG)
if free_radius is not None:
diff_sources = [s.name for s in self.roi.sources if s.diffuse]
skydir = self.roi[name].skydir
free_srcs = [s.name for s in
self.roi.get_sources(skydir=skydir,
distance=free_radius,
exclude=diff_sources)]
self.free_sources_by_name(free_srcs, pars='norm',
loglevel=logging.DEBUG)
src = self.roi.copy_source(name)
skydir = src.skydir
skywcs = self.geom.wcs
src_pix = skydir.to_pixel(skywcs)
fit0 = self._fit_position_tsmap(name, prefix=prefix,
dtheta_max=dtheta_max,
zmin=-3.0,
use_pylike=False)
self.logger.debug('Completed localization with TS Map.\n'
'(ra,dec) = (%10.4f,%10.4f) '
'(glon,glat) = (%10.4f,%10.4f)',
fit0['ra'], fit0['dec'],
fit0['glon'], fit0['glat'])
# Fit baseline (point-source) model
self.free_source(name, loglevel=logging.DEBUG)
if fix_shape:
self.free_source(name, free=False, pars='shape',
loglevel=logging.DEBUG)
fit_output = self._fit(loglevel=logging.DEBUG, **
kwargs.get('optimizer', {}))
# Save likelihood value for baseline fit
loglike_base = fit_output['loglike']
self.logger.debug('Baseline Model Log-Likelihood: %f', loglike_base)
o = defaults.make_default_tuple(defaults.localize_output)
o.name = name
o.config = kwargs
o.fit_success = True
o.loglike_init = loglike_init
o.loglike_base = loglike_base
o.loglike_loc = np.nan
o.dloglike_loc = np.nan
if fit0['fit_success']:
scan_cdelt = 2.0 * fit0['pos_r95'] / (nstep - 1.0)
else:
scan_cdelt = np.abs(skywcs.wcs.cdelt[0])
self.logger.debug('Refining localization search to '
'region of width: %.4f deg',
scan_cdelt * nstep)
fit1 = self._fit_position_scan(name,
skydir=fit0['skydir'],
scan_cdelt=scan_cdelt,
**kwargs)
o.loglike_loc = fit1['loglike']
o.dloglike_loc = o.loglike_loc - o.loglike_base
o.tsmap = fit0.pop('tsmap')
o.tsmap_peak = fit1.pop('tsmap')
# o.update(fit1)
# Best fit position and uncertainty from fit to TS map
o.fit_init = fit0
# Best fit position and uncertainty from pylike scan
o.fit_scan = fit1
o.update(fit1)
cdelt0 = np.abs(skywcs.wcs.cdelt[0])
cdelt1 = np.abs(skywcs.wcs.cdelt[1])
pix = fit1['skydir'].to_pixel(skywcs)
o.pos_offset = skydir.separation(fit1['skydir']).deg
o.xpix = float(pix[0])
o.ypix = float(pix[1])
o.deltax = (o.xpix - src_pix[0]) * cdelt0
o.deltay = (o.ypix - src_pix[1]) * cdelt1
o.ra_preloc = skydir.ra.deg
o.dec_preloc = skydir.dec.deg
o.glon_preloc = skydir.galactic.l.deg
o.glat_preloc = skydir.galactic.b.deg
if o.pos_offset > dtheta_max:
o.fit_success = False
if not o.fit_success:
self.logger.warning('Fit to localization contour failed.')
elif not o.fit_inbounds:
self.logger.warning('Best-fit position outside of search region.')
else:
self.logger.info('Localization succeeded.')
if update and ((not o.fit_success) or (not o.fit_inbounds)):
self.logger.warning(
'Localization failed. Keeping existing position.')
if update and o.fit_success and o.fit_inbounds:
self.logger.info('Updating source %s '
'to localized position.', name)
src = self.delete_source(name)
src.set_position(fit1['skydir'])
self.add_source(name, src, free=True)
self.free_source(name, loglevel=logging.DEBUG)
if fix_shape:
self.free_source(name, free=False, pars='shape',
loglevel=logging.DEBUG)
fit_output = self.fit(loglevel=logging.DEBUG)
o.loglike_loc = fit_output['loglike']
o.dloglike_loc = o.loglike_loc - o.loglike_base
src = self.roi.get_source_by_name(name)
src['glon_err'] = o.glon_err
src['glat_err'] = o.glat_err
src['ra_err'] = o.glon_err
src['dec_err'] = o.glat_err
src['pos_err'] = o.pos_err
src['pos_err_semimajor'] = o.pos_err_semimajor
src['pos_err_semiminor'] = o.pos_err_semiminor
src['pos_r68'] = o.pos_r68
src['pos_r95'] = o.pos_r95
src['pos_r99'] = o.pos_r99
src['pos_angle'] = o.pos_angle
src['pos_gal_cov'] = o.pos_gal_cov
src['pos_gal_corr'] = o.pos_gal_corr
src['pos_cel_cov'] = o.pos_cel_cov
src['pos_cel_corr'] = o.pos_cel_corr
else:
saved_state.restore()
self._sync_params(name)
self._update_roi()
self.logger.info('Localization completed with new position:\n'
'( ra, dec) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'(glon,glat) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'offset = %8.4f r68 = %8.4f r95 = %8.4f r99 = %8.4f',
o.ra, o.ra_err, o.dec, o.dec_err,
o.glon, o.glon_err, o.glat, o.glat_err,
o.pos_offset, o.pos_r68, o.pos_r95, o.pos_r99)
self.logger.info('LogLike: %12.3f DeltaLogLike: %12.3f',
o.loglike_loc, o.loglike_loc - o.loglike_init)
return o
def _extension(self, name, **kwargs):
spatial_model = kwargs['spatial_model']
width_min = kwargs['width_min']
width_max = kwargs['width_max']
width_nstep = kwargs['width_nstep']
width = kwargs['width']
free_background = kwargs['free_background']
free_radius = kwargs.get('free_radius', None)
fix_shape = kwargs.get('fix_shape', False)
make_tsmap = kwargs.get('make_tsmap', False)
tsmap_fitter = kwargs.get('tsmap_fitter', 'tsmap')
update = kwargs['update']
sqrt_ts_threshold = kwargs['sqrt_ts_threshold']
if kwargs['psf_scale_fn']:
def psf_scale_fn(t):
return 1.0 + np.interp(np.log10(t), kwargs['psf_scale_fn'][0],
kwargs['psf_scale_fn'][1])
else:
psf_scale_fn = None
saved_state = LikelihoodState(self.like)
loglike_init = -self.like()
self.logger.debug('Initial Model Log-Likelihood: %f', loglike_init)
if not free_background:
self.free_sources(free=False, loglevel=logging.DEBUG)
if free_radius is not None:
diff_sources = [s.name for s in self.roi.sources if s.diffuse]
skydir = self.roi[name].skydir
free_srcs = [
s.name for s in self.roi.get_sources(
skydir=skydir, distance=free_radius, exclude=diff_sources)
]
self.free_sources_by_name(free_srcs,
pars='norm',
loglevel=logging.DEBUG)
# Fit baseline model
self.free_source(name, loglevel=logging.DEBUG)
if fix_shape:
self.free_source(name,
free=False,
pars='shape',
loglevel=logging.DEBUG)
fit_output = self._fit(loglevel=logging.DEBUG, **kwargs['optimizer'])
src = self.roi.copy_source(name)
# Save likelihood value for baseline fit
saved_state_base = LikelihoodState(self.like)
loglike_base = fit_output['loglike']
self.logger.debug('Baseline Model Log-Likelihood: %f', loglike_base)
if not width:
width = np.logspace(np.log10(width_min), np.log10(width_max),
width_nstep)
width = np.array(width)
width = width[width > 0]
width = np.concatenate(([0.0], np.array(width)))
o = defaults.make_default_tuple(defaults.extension_output)
o.name = name
o.width = width
o.dloglike = np.zeros(len(width) + 1)
o.loglike = np.zeros(len(width) + 1)
o.loglike_base = loglike_base
o.loglike_init = loglike_init
o.config = kwargs
o.ebin_ext = np.ones(self.enumbins) * np.nan
o.ebin_ext_err = np.ones(self.enumbins) * np.nan
o.ebin_ext_err_lo = np.ones(self.enumbins) * np.nan
o.ebin_ext_err_hi = np.ones(self.enumbins) * np.nan
o.ebin_ext_ul95 = np.ones(self.enumbins) * np.nan
o.ebin_ts_ext = np.ones(self.enumbins) * np.nan
o.ebin_loglike = np.ones((self.enumbins, len(width))) * np.nan
o.ebin_dloglike = np.ones((self.enumbins, len(width))) * np.nan
o.ebin_loglike_ptsrc = np.ones(self.enumbins) * np.nan
o.ebin_loglike_ext = np.ones(self.enumbins) * np.nan
o.ebin_e_min = self.energies[:-1]
o.ebin_e_max = self.energies[1:]
o.ebin_e_ctr = np.sqrt(o.ebin_e_min * o.ebin_e_max)
self.logger.debug('Width scan vector:\n %s', width)
if kwargs['fit_position']:
ext_fit = self._fit_extension_full(name,
spatial_model=spatial_model,
optimizer=kwargs['optimizer'],
width_max=width_max,
width_min=width_min,
width_nstep=width_nstep)
else:
ext_fit = self._fit_extension(name,
spatial_model=spatial_model,
optimizer=kwargs['optimizer'],
psf_scale_fn=psf_scale_fn,
width_max=width_max,
width_min=width_min,
width_nstep=width_nstep)
o.update(ext_fit)
# Fit with the best-fit extension model
self.logger.info('Fitting extended-source model.')
self.set_source_morphology(name,
spatial_model=spatial_model,
spatial_pars={
'ra': o['ra'],
'dec': o['dec'],
'SpatialWidth': o['ext']
},
use_pylike=False,
psf_scale_fn=psf_scale_fn)
# Perform scan over width parameter
o.loglike = self._scan_extension(name,
spatial_model=spatial_model,
width=width,
optimizer=kwargs['optimizer'],
psf_scale_fn=psf_scale_fn)
self.set_source_morphology(name,
spatial_model=spatial_model,
spatial_pars={
'ra': o['ra'],
'dec': o['dec'],
'SpatialWidth': o['ext']
},
use_pylike=False,
psf_scale_fn=psf_scale_fn)
fit_output = self._fit(loglevel=logging.DEBUG,
update=False,
**kwargs['optimizer'])
o.source_fit = self.get_src_model(name,
reoptimize=True,
optimizer=kwargs['optimizer'])
o.loglike_ext = fit_output['loglike']
if kwargs['fit_ebin']:
self._fit_extension_ebin(name,
o,
spatial_model=spatial_model,
optimizer=kwargs['optimizer'],
psf_scale_fn=psf_scale_fn,
reoptimize=kwargs.get('reoptimize', True))
if kwargs['save_model_map']:
o.ext_tot_map = self.model_counts_map()
o.ext_src_map = self.model_counts_map(name)
o.ext_bkg_map = self.model_counts_map(exclude=[name])
if make_tsmap:
tsmap_model = {
'SpatialModel': 'RadialDisk',
'SpatialWidth': 0.1 * 0.8246211251235321
}
tsmap_model.update(src.spectral_pars)
self.logger.info('Generating TS map.')
if tsmap_fitter == 'tsmap':
tsmap = self.tsmap(model=tsmap_model,
map_skydir=SkyCoord(o['ra'],
o['dec'],
unit='deg'),
map_size=max(1.0, 4.0 * o['ext']),
exclude=[name],
write_fits=False,
write_npy=False,
use_pylike=False,
make_plots=False,
loglevel=logging.DEBUG)
o.tsmap = tsmap['ts']
elif tsmap_fitter == 'tscube':
tscube = self.tscube(model=tsmap_model,
map_skydir=SkyCoord(o['ra'],
o['dec'],
unit='deg'),
map_size=max(1.0, 4.0 * o['ext']),
do_sed=False,
write_fits=False,
write_npy=False,
make_plots=False,
loglevel=logging.DEBUG)
o.tsmap = tscube['ts']
self.logger.info('Testing point-source model.')
# Test point-source hypothesis
self.set_source_morphology(name,
spatial_model='PointSource',
use_pylike=False,
psf_scale_fn=psf_scale_fn)
# Fit a point-source
saved_state_base.restore()
self.logger.debug('Fitting point-source model.')
fit_output = self._fit(loglevel=logging.DEBUG, **kwargs['optimizer'])
if src['SpatialModel'] == 'PointSource' and kwargs['fit_position']:
loc = self.localize(name, update=False)
o.loglike_ptsrc = loc['loglike_loc']
else:
o.loglike_ptsrc = fit_output['loglike']
o.dloglike = o.loglike - o.loglike_ptsrc
o.ts_ext = 2 * (o.loglike_ext - o.loglike_ptsrc)
self.logger.debug('Point-Source Model Likelihood: %f', o.loglike_ptsrc)
if kwargs['save_model_map']:
o.ptsrc_tot_map = self.model_counts_map()
o.ptsrc_src_map = self.model_counts_map(name)
o.ptsrc_bkg_map = self.model_counts_map(exclude=[name])
if update and (sqrt_ts_threshold is None
or np.sqrt(o['ts_ext']) > sqrt_ts_threshold):
src = self.delete_source(name, loglevel=logging.DEBUG)
src.set_spatial_model(spatial_model, {
'ra': o.ra,
'dec': o.dec,
'SpatialWidth': o.ext
})
# FIXME: Issue with source map cache with source is
# initialized as fixed.
self.add_source(name, src, free=True, loglevel=logging.DEBUG)
self.free_source(name, loglevel=logging.DEBUG)
if fix_shape:
self.free_source(name,
free=False,
pars='shape',
loglevel=logging.DEBUG)
fit_output = self.fit(loglevel=logging.DEBUG,
**kwargs['optimizer'])
o.loglike_ext = fit_output['loglike']
src = self.roi.get_source_by_name(name)
if kwargs['fit_position']:
for k in [
'ra_err', 'dec_err', 'glon_err', 'glat_err', 'pos_err',
'pos_err_semimajor', 'pos_err_semiminor', 'pos_r68',
'pos_r95', 'pos_r99', 'pos_angle'
]:
src[k] = o[k]
else:
self.set_source_morphology(name,
spatial_model=src['SpatialModel'],
spatial_pars=src.spatial_pars,
update_source=False)
# Restore ROI to previous state
saved_state.restore()
self._sync_params(name)
self._update_roi()
self.logger.info('Best-fit extension: %6.4f + %6.4f - %6.4f' %
(o['ext'], o['ext_err_hi'], o['ext_err_lo']))
self.logger.info('TS_ext: %.3f' % o['ts_ext'])
self.logger.info('Extension UL: %6.4f' % o['ext_ul95'])
self.logger.info('LogLike: %12.3f DeltaLogLike: %12.3f', o.loglike_ext,
o.loglike_ext - o.loglike_init)
if kwargs['fit_position']:
self.logger.info(
'Position:\n'
'( ra, dec) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'(glon,glat) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'offset = %8.4f r68 = %8.4f r95 = %8.4f r99 = %8.4f', o.ra,
o.ra_err, o.dec, o.dec_err, o.glon, o.glon_err, o.glat,
o.glat_err, o.pos_offset, o.pos_r68, o.pos_r95, o.pos_r99)
return o
def _localize(self, name, **kwargs):
nstep = kwargs.get('nstep')
dtheta_max = kwargs.get('dtheta_max')
update = kwargs.get('update', True)
prefix = kwargs.get('prefix', '')
use_cache = kwargs.get('use_cache', False)
free_background = kwargs.get('free_background', False)
free_radius = kwargs.get('free_radius', None)
saved_state = LikelihoodState(self.like)
if not free_background:
self.free_sources(free=False, loglevel=logging.DEBUG)
if free_radius is not None:
diff_sources = [s.name for s in self.roi.sources if s.diffuse]
skydir = self.roi[name].skydir
free_srcs = [s.name for s in
self.roi.get_sources(skydir=skydir,
distance=free_radius,
exclude=diff_sources)]
self.free_sources_by_name(free_srcs, pars='norm',
loglevel=logging.DEBUG)
src = self.roi.copy_source(name)
skydir = src.skydir
skywcs = self._skywcs
src_pix = skydir.to_pixel(skywcs)
fit0 = self._fit_position_tsmap(name, prefix=prefix,
dtheta_max=dtheta_max,
zmin=-3.0,
use_pylike=False)
self.logger.debug('Completed localization with TS Map.\n'
'(ra,dec) = (%10.4f,%10.4f) '
'(glon,glat) = (%10.4f,%10.4f)',
fit0['ra'], fit0['dec'],
fit0['glon'], fit0['glat'])
# Fit baseline (point-source) model
self.free_norm(name)
fit_output = self._fit(loglevel=logging.DEBUG, **
kwargs.get('optimizer', {}))
# Save likelihood value for baseline fit
loglike0 = fit_output['loglike']
self.logger.debug('Baseline Model Likelihood: %f', loglike0)
o = defaults.make_default_tuple(defaults.localize_output)
o.name = name
o.config = kwargs
o.fit_success = True
o.loglike_base = loglike0
o.loglike_loc = np.nan
o.dloglike_loc = np.nan
if fit0['fit_success']:
scan_cdelt = 2.0 * fit0['pos_r95'] / (nstep - 1.0)
else:
scan_cdelt = np.abs(skywcs.wcs.cdelt[0])
self.logger.debug('Refining localization search to '
'region of width: %.4f deg',
scan_cdelt * nstep)
fit1 = self._fit_position_scan(name,
skydir=fit0['skydir'],
scan_cdelt=scan_cdelt,
**kwargs)
o.loglike_loc = fit1['loglike']
o.dloglike_loc = o.loglike_loc - o.loglike_base
o.tsmap = fit0.pop('tsmap')
o.tsmap_peak = fit1.pop('tsmap')
# o.update(fit1)
# Best fit position and uncertainty from fit to TS map
o.fit_init = fit0
# Best fit position and uncertainty from pylike scan
o.fit_scan = fit1
o.update(fit1)
cdelt0 = np.abs(skywcs.wcs.cdelt[0])
cdelt1 = np.abs(skywcs.wcs.cdelt[1])
pix = fit1['skydir'].to_pixel(skywcs)
o.pos_offset = skydir.separation(fit1['skydir']).deg
o.xpix = float(pix[0])
o.ypix = float(pix[1])
o.deltax = (o.xpix - src_pix[0]) * cdelt0
o.deltay = (o.ypix - src_pix[1]) * cdelt1
o.ra_preloc = skydir.ra.deg
o.dec_preloc = skydir.dec.deg
o.glon_preloc = skydir.galactic.l.deg
o.glat_preloc = skydir.galactic.b.deg
if o.pos_offset > dtheta_max:
o.fit_success = False
self.logger.info('Localization completed with new position:\n'
'( ra, dec) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'(glon,glat) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'offset = %8.4f r68 = %8.4f r95 = %8.4f r99 = %8.4f',
o.ra, o.ra_err, o.dec, o.dec_err,
o.glon, o.glon_err, o.glat, o.glat_err,
o.pos_offset, o.pos_r68, o.pos_r95, o.pos_r99)
if not o.fit_success:
self.logger.warning('Fit to localization contour failed.')
elif not o.fit_inbounds:
self.logger.warning('Best-fit position outside of search region.')
else:
self.logger.info('Localization succeeded.')
if update and ((not o.fit_success) or (not o.fit_inbounds)):
self.logger.warning(
'Localization failed. Keeping existing position.')
if update and o.fit_success and o.fit_inbounds:
self.logger.info('Updating source %s '
'to localized position.', name)
src = self.delete_source(name)
src.set_position(fit1['skydir'])
self.add_source(name, src, free=True)
fit_output = self.fit(loglevel=logging.DEBUG)
o.loglike_loc = fit_output['loglike']
o.dloglike_loc = o.loglike_loc - o.loglike_base
src = self.roi.get_source_by_name(name)
self.logger.info('LogLike: %12.3f DeltaLogLike: %12.3f',
o.loglike_loc, o.dloglike_loc)
src['glon_err'] = o.glon_err
src['glat_err'] = o.glat_err
src['ra_err'] = o.glon_err
src['dec_err'] = o.glat_err
src['pos_err'] = o.pos_err
src['pos_err_semimajor'] = o.pos_err_semimajor
src['pos_err_semiminor'] = o.pos_err_semiminor
src['pos_r68'] = o.pos_r68
src['pos_r95'] = o.pos_r95
src['pos_r99'] = o.pos_r99
src['pos_angle'] = o.pos_angle
src['pos_gal_cov'] = o.pos_gal_cov
src['pos_gal_corr'] = o.pos_gal_corr
src['pos_cel_cov'] = o.pos_cel_cov
src['pos_cel_corr'] = o.pos_cel_corr
else:
saved_state.restore()
self._sync_params(name)
self._update_roi()
return o
def _extension(self, name, **kwargs):
spatial_model = kwargs['spatial_model']
width_min = kwargs['width_min']
width_max = kwargs['width_max']
width_nstep = kwargs['width_nstep']
width = kwargs['width']
free_background = kwargs['free_background']
free_radius = kwargs.get('free_radius', None)
fix_shape = kwargs.get('fix_shape', False)
make_tsmap = kwargs.get('make_tsmap', False)
update = kwargs['update']
sqrt_ts_threshold = kwargs['sqrt_ts_threshold']
if kwargs['psf_scale_fn']:
def psf_scale_fn(t): return 1.0 + np.interp(np.log10(t),
kwargs['psf_scale_fn'][0],
kwargs['psf_scale_fn'][1])
else:
psf_scale_fn = None
saved_state = LikelihoodState(self.like)
loglike_init = -self.like()
self.logger.debug('Initial Model Log-Likelihood: %f', loglike_init)
if not free_background:
self.free_sources(free=False, loglevel=logging.DEBUG)
if free_radius is not None:
diff_sources = [s.name for s in self.roi.sources if s.diffuse]
skydir = self.roi[name].skydir
free_srcs = [s.name for s in
self.roi.get_sources(skydir=skydir,
distance=free_radius,
exclude=diff_sources)]
self.free_sources_by_name(free_srcs, pars='norm',
loglevel=logging.DEBUG)
# Fit baseline model
self.free_source(name, loglevel=logging.DEBUG)
if fix_shape:
self.free_source(name, free=False, pars='shape',
loglevel=logging.DEBUG)
fit_output = self._fit(loglevel=logging.DEBUG, **kwargs['optimizer'])
src = self.roi.copy_source(name)
# Save likelihood value for baseline fit
saved_state_base = LikelihoodState(self.like)
loglike_base = fit_output['loglike']
self.logger.debug('Baseline Model Log-Likelihood: %f', loglike_base)
if not width:
width = np.logspace(np.log10(width_min), np.log10(width_max),
width_nstep)
width = np.array(width)
width = width[width > 0]
width = np.concatenate(([0.0], np.array(width)))
o = defaults.make_default_tuple(defaults.extension_output)
o.name = name
o.width = width
o.dloglike = np.zeros(len(width) + 1)
o.loglike = np.zeros(len(width) + 1)
o.loglike_base = loglike_base
o.loglike_init = loglike_init
o.config = kwargs
o.ebin_ext = np.ones(self.enumbins) * np.nan
o.ebin_ext_err = np.ones(self.enumbins) * np.nan
o.ebin_ext_err_lo = np.ones(self.enumbins) * np.nan
o.ebin_ext_err_hi = np.ones(self.enumbins) * np.nan
o.ebin_ext_ul95 = np.ones(self.enumbins) * np.nan
o.ebin_ts_ext = np.ones(self.enumbins) * np.nan
o.ebin_loglike = np.ones((self.enumbins, len(width))) * np.nan
o.ebin_dloglike = np.ones((self.enumbins, len(width))) * np.nan
o.ebin_loglike_ptsrc = np.ones(self.enumbins) * np.nan
o.ebin_loglike_ext = np.ones(self.enumbins) * np.nan
o.ebin_e_min = self.energies[:-1]
o.ebin_e_max = self.energies[1:]
o.ebin_e_ctr = np.sqrt(o.ebin_e_min * o.ebin_e_max)
self.logger.debug('Width scan vector:\n %s', width)
if kwargs['fit_position']:
ext_fit = self._fit_extension_full(name,
spatial_model=spatial_model,
optimizer=kwargs['optimizer'])
else:
ext_fit = self._fit_extension(name,
spatial_model=spatial_model,
optimizer=kwargs['optimizer'],
psf_scale_fn=psf_scale_fn)
o.update(ext_fit)
# Fit with the best-fit extension model
self.logger.info('Fitting extended-source model.')
self.set_source_morphology(name, spatial_model=spatial_model,
spatial_pars={'ra': o['ra'], 'dec': o['dec'],
'SpatialWidth': o['ext']},
use_pylike=False,
psf_scale_fn=psf_scale_fn)
# Perform scan over width parameter
o.loglike = self._scan_extension(name,
spatial_model=spatial_model,
width=width,
optimizer=kwargs['optimizer'],
psf_scale_fn=psf_scale_fn)
self.set_source_morphology(name, spatial_model=spatial_model,
spatial_pars={'ra': o['ra'], 'dec': o['dec'],
'SpatialWidth': o['ext']},
use_pylike=False,
psf_scale_fn=psf_scale_fn)
fit_output = self._fit(loglevel=logging.DEBUG, update=False,
**kwargs['optimizer'])
o.source_fit = self.get_src_model(name, reoptimize=True,
optimizer=kwargs['optimizer'])
o.loglike_ext = fit_output['loglike']
if kwargs['fit_ebin']:
self._fit_extension_ebin(name, o, **kwargs)
if kwargs['save_model_map']:
o.ext_tot_map = self.model_counts_map()
o.ext_src_map = self.model_counts_map(name)
o.ext_bkg_map = self.model_counts_map(exclude=[name])
if make_tsmap:
tsmap_model = {'SpatialModel': 'RadialDisk',
'SpatialWidth': 0.1 * 0.8246211251235321}
tsmap_model.update(src.spectral_pars)
self.logger.info('Generating TS map.')
tsmap = self.tsmap(model=tsmap_model,
map_skydir=SkyCoord(
o['ra'], o['dec'], unit='deg'),
map_size=max(1.0, 4.0 * o['ext']),
exclude=[name],
write_fits=False,
write_npy=False,
use_pylike=False,
make_plots=False,
loglevel=logging.DEBUG)
o.tsmap = tsmap['ts']
self.logger.info('Testing point-source model.')
# Test point-source hypothesis
self.set_source_morphology(name, spatial_model='PointSource',
use_pylike=False,
psf_scale_fn=psf_scale_fn)
# Fit a point-source
saved_state_base.restore()
self.logger.debug('Fitting point-source model.')
fit_output = self._fit(loglevel=logging.DEBUG, **kwargs['optimizer'])
if src['SpatialModel'] == 'PointSource' and kwargs['fit_position']:
loc = self.localize(name, update=False)
o.loglike_ptsrc = loc['loglike_loc']
else:
o.loglike_ptsrc = fit_output['loglike']
o.dloglike = o.loglike - o.loglike_ptsrc
o.ts_ext = 2 * (o.loglike_ext - o.loglike_ptsrc)
self.logger.debug('Point-Source Model Likelihood: %f', o.loglike_ptsrc)
if kwargs['save_model_map']:
o.ptsrc_tot_map = self.model_counts_map()
o.ptsrc_src_map = self.model_counts_map(name)
o.ptsrc_bkg_map = self.model_counts_map(exclude=[name])
if update and (sqrt_ts_threshold is None or
np.sqrt(o['ts_ext']) > sqrt_ts_threshold):
src = self.delete_source(name, loglevel=logging.DEBUG)
src.set_spatial_model(spatial_model,
{'ra': o.ra, 'dec': o.dec,
'SpatialWidth': o.ext})
# FIXME: Issue with source map cache with source is
# initialized as fixed.
self.add_source(name, src, free=True, loglevel=logging.DEBUG)
self.free_source(name, loglevel=logging.DEBUG)
if fix_shape:
self.free_source(name, free=False, pars='shape',
loglevel=logging.DEBUG)
fit_output = self.fit(loglevel=logging.DEBUG,
**kwargs['optimizer'])
o.loglike_ext = fit_output['loglike']
src = self.roi.get_source_by_name(name)
if kwargs['fit_position']:
for k in ['ra_err', 'dec_err', 'glon_err', 'glat_err',
'pos_err', 'pos_err_semimajor', 'pos_err_semiminor',
'pos_r68', 'pos_r95', 'pos_r99', 'pos_angle']:
src[k] = o[k]
else:
self.set_source_morphology(name, spatial_model=src['SpatialModel'],
spatial_pars=src.spatial_pars,
update_source=False)
# Restore ROI to previous state
saved_state.restore()
self._sync_params(name)
self._update_roi()
self.logger.info('Best-fit extension: %6.4f + %6.4f - %6.4f'
% (o['ext'], o['ext_err_hi'], o['ext_err_lo']))
self.logger.info('TS_ext: %.3f' % o['ts_ext'])
self.logger.info('Extension UL: %6.4f' % o['ext_ul95'])
self.logger.info('LogLike: %12.3f DeltaLogLike: %12.3f',
o.loglike_ext, o.loglike_ext - o.loglike_init)
if kwargs['fit_position']:
self.logger.info('Position:\n'
'( ra, dec) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'(glon,glat) = (%10.4f +/- %8.4f,%10.4f +/- %8.4f)\n'
'offset = %8.4f r68 = %8.4f r95 = %8.4f r99 = %8.4f',
o.ra, o.ra_err, o.dec, o.dec_err,
o.glon, o.glon_err, o.glat, o.glat_err,
o.pos_offset, o.pos_r68, o.pos_r95, o.pos_r99)
return o
