def _fit_extension(self, name, **kwargs):
spatial_model = kwargs.get('spatial_model', 'RadialGaussian')
optimizer = kwargs.get('optimizer', {})
width_min = kwargs.get('width_min', 10**-2.0)
width_max = kwargs.get('width_max', 10**0.5)
width_nstep = kwargs.get('width_nstep', 21)
fit_position = kwargs.get('fit_position', False)
skydir = kwargs.get('skydir', self.roi[name].skydir)
psf_scale_fn = kwargs.get('psf_scale_fn', None)
reoptimize = kwargs.get('reoptimize', True)
src = self.roi.copy_source(name)
# If the source is extended split the likelihood scan into two
# parts centered on the best-fit value -- this ensures better
# fit stability
if (src['SpatialModel'] in ['RadialGaussian', 'RadialDisk']
and src['SpatialWidth'] > width_min):
width_lo = np.logspace(np.log10(width_min),
np.log10(src['SpatialWidth']),
width_nstep // 2 + (width_nstep % 2 > 0))
width_hi = np.logspace(np.log10(src['SpatialWidth']),
np.log10(width_max), width_nstep // 2 + 1)
loglike_lo = self._scan_extension(name,
spatial_model=spatial_model,
width=width_lo[::-1],
optimizer=optimizer,
skydir=skydir,
psf_scale_fn=psf_scale_fn,
reoptimize=reoptimize)[::-1]
loglike_hi = self._scan_extension(name,
spatial_model=spatial_model,
width=width_hi,
optimizer=optimizer,
skydir=skydir,
psf_scale_fn=psf_scale_fn,
reoptimize=reoptimize)
width = np.concatenate((width_lo, width_hi[1:]))
loglike = np.concatenate((loglike_lo, loglike_hi[1:]))
else:
width = np.logspace(np.log10(width_min), np.log10(width_max),
width_nstep)
width = np.concatenate(([0.0], width))
loglike = self._scan_extension(name,
spatial_model=spatial_model,
width=width,
optimizer=optimizer,
skydir=skydir,
psf_scale_fn=psf_scale_fn,
reoptimize=reoptimize)
ul_data = utils.get_parameter_limits(width,
loglike,
bounds=[10**-3.0, width_max])
if not np.isfinite(ul_data['err']):
ul_data['x0'] = width[np.argmax(loglike)]
ul_data['err'] = ul_data['x0']
ul_data['err_lo'] = ul_data['x0']
ul_data['err_hi'] = ul_data['x0']
imax = np.argmax(loglike)
err = max(10**-2.0, ul_data['err'])
lolim = max(min(ul_data['x0'], width[imax]) - 2.0 * err, 0)
if np.isfinite(ul_data['ul']):
hilim = 1.5 * ul_data['ul']
else:
hilim = ul_data['x0'] + 2.0 * err
nstep = max(width_nstep, int((hilim - lolim) / err))
width2 = np.linspace(lolim, hilim, nstep)
loglike2 = self._scan_extension(name,
spatial_model=spatial_model,
width=width2,
optimizer=optimizer,
skydir=skydir,
psf_scale_fn=psf_scale_fn,
reoptimize=reoptimize)
ul_data2 = utils.get_parameter_limits(width2,
loglike2,
bounds=[10**-3.0, width_max])
self.logger.debug('width: %s', width)
self.logger.debug('loglike: %s', loglike - np.max(loglike))
self.logger.debug('ul_data:\n %s', pprint.pformat(ul_data))
self.logger.debug('width2: %s', width2)
self.logger.debug('loglike2: %s', loglike2 - np.max(loglike2))
self.logger.debug('ul_data2:\n %s', pprint.pformat(ul_data2))
return MutableNamedTuple(ext=max(ul_data2['x0'], 10**-2.5),
ext_ul95=ul_data2['ul'],
ext_err_lo=ul_data2['err_lo'],
ext_err_hi=ul_data2['err_hi'],
ext_err=ul_data2['err'],
loglike_ext=ul_data2['lnlmax'],
ra=skydir.ra.deg,
dec=skydir.dec.deg,
glon=skydir.galactic.l.deg,
glat=skydir.galactic.b.deg,
pos_offset=0.0)
def _fit_extension_full(self, name, **kwargs):
skydir = self.roi[name].skydir.copy()
src = self.roi.copy_source(name)
spatial_model = kwargs.get('spatial_model', 'RadialGaussian')
loglike = -self.like()
o = MutableNamedTuple(ext=np.nan,
ext_ul95=np.nan,
ext_err_lo=np.nan,
ext_err_hi=np.nan,
ext_err=np.nan,
loglike_ext=np.nan,
ra=np.nan,
dec=np.nan,
glon=np.nan,
glat=np.nan,
ra_err=np.nan,
dec_err=np.nan,
glon_err=np.nan,
glat_err=np.nan,
pos_err=np.nan,
pos_r68=np.nan,
pos_r95=np.nan,
pos_r99=np.nan,
pos_err_semimajor=np.nan,
pos_err_semiminor=np.nan,
pos_angle=np.nan,
pos_offset=np.nan)
t0 = Timer()
t1 = Timer()
nstep = 7
for i in range(4):
t0.start()
fit_ext = self._fit_extension(name, skydir=skydir, **kwargs)
o.update(fit_ext)
self.set_source_morphology(
name,
spatial_model=spatial_model,
spatial_pars={'SpatialWidth': max(o.ext, 0.00316)},
use_pylike=False)
t0.stop()
t1.start()
if i == 0 or not np.isfinite(o.pos_r99):
dtheta_max = max(0.5, 1.5 * o.ext)
else:
dtheta_max = max(0.5, o.pos_r99)
fit_pos0, fit_pos1 = self._fit_position(name,
nstep=nstep,
dtheta_max=dtheta_max,
zmin=-3.0,
use_pylike=False)
o.update(fit_pos0)
t1.stop()
self.set_source_morphology(name,
spatial_model=spatial_model,
spatial_pars={
'RA': o['ra'],
'DEC': o['dec']
},
use_pylike=False)
self.logger.debug('Elapsed Time: %.2f %.2f', t0.elapsed_time,
t1.elapsed_time)
fit_output = self._fit(loglevel=logging.DEBUG,
**kwargs['optimizer'])
skydir = fit_pos0['skydir']
o.pos_offset = skydir.separation(src.skydir).deg
o.loglike_ext = -self.like()
dloglike = o.loglike_ext - loglike
self.logger.info(
'Iter %i R68 = %8.3f Offset = %8.3f '
'LogLikelihood = %10.2f Delta-LogLikelihood = %8.2f', i, o.ext,
o.pos_offset, o.loglike_ext, dloglike)
if i > 0 and dloglike < 0.1:
break
loglike = o.loglike_ext
self.set_source_morphology(name,
spatial_pars=src.spatial_pars,
use_pylike=False)
return o
def _fit_extension_full(self, name, **kwargs):
skydir = self.roi[name].skydir.copy()
src = self.roi.copy_source(name)
spatial_model = kwargs.get('spatial_model', 'RadialGaussian')
loglike = -self.like()
o = MutableNamedTuple(
ext=np.nan, ext_ul95=np.nan,
ext_err_lo=np.nan, ext_err_hi=np.nan,
ext_err=np.nan,
loglike_ext=np.nan,
ra=np.nan, dec=np.nan, glon=np.nan, glat=np.nan,
ra_err=np.nan, dec_err=np.nan,
glon_err=np.nan, glat_err=np.nan,
pos_err=np.nan, pos_r68=np.nan,
pos_r95=np.nan, pos_r99=np.nan,
pos_err_semimajor=np.nan, pos_err_semiminor=np.nan,
pos_angle=np.nan, pos_offset=np.nan)
t0 = Timer()
t1 = Timer()
nstep = 7
for i in range(4):
t0.start()
fit_ext = self._fit_extension(name, skydir=skydir, **kwargs)
o.update(fit_ext)
self.set_source_morphology(name,
spatial_model=spatial_model,
spatial_pars={
'SpatialWidth': max(o.ext, 0.00316)},
use_pylike=False)
t0.stop()
t1.start()
if i == 0 or not np.isfinite(o.pos_r99):
dtheta_max = max(0.5, 1.5 * o.ext)
else:
dtheta_max = max(0.5, o.pos_r99)
fit_pos0, fit_pos1 = self._fit_position(name, nstep=nstep,
dtheta_max=dtheta_max,
zmin=-3.0, use_pylike=False)
o.update(fit_pos0)
t1.stop()
self.set_source_morphology(name,
spatial_model=spatial_model,
spatial_pars={'RA': o['ra'],
'DEC': o['dec']},
use_pylike=False)
self.logger.debug('Elapsed Time: %.2f %.2f',
t0.elapsed_time, t1.elapsed_time)
fit_output = self._fit(
loglevel=logging.DEBUG, **kwargs['optimizer'])
skydir = fit_pos0['skydir']
o.pos_offset = skydir.separation(src.skydir).deg
o.loglike_ext = -self.like()
dloglike = o.loglike_ext - loglike
self.logger.info('Iter %i R68 = %8.3f Offset = %8.3f '
'LogLikelihood = %10.2f Delta-LogLikelihood = %8.2f',
i, o.ext, o.pos_offset, o.loglike_ext,
dloglike)
if i > 0 and dloglike < 0.1:
break
loglike = o.loglike_ext
self.set_source_morphology(name, spatial_pars=src.spatial_pars,
use_pylike=False)
return o