def _fit_extension(self, name, **kwargs):
spatial_model = kwargs.get('spatial_model', 'RadialGaussian')
optimizer = kwargs.get('optimizer', {})
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)
width = np.logspace(-2.0, 0.5, 16)
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)
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']
err = max(10**-2.0, ul_data['err'])
lolim = max(ul_data['x0'] - 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(11, 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_data = utils.get_parameter_limits(width2, loglike2)
o = {}
o['ext'] = max(ul_data['x0'], 10**-2.5)
o['ext_ul95'] = ul_data['ul']
o['ext_err_lo'] = ul_data['err_lo']
o['ext_err_hi'] = ul_data['err_hi']
o['ext_err'] = ul_data['err']
o['loglike_ext'] = ul_data['lnlmax']
o['ra'] = skydir.ra.deg
o['dec'] = skydir.dec.deg
o['glon'] = skydir.galactic.l.deg
o['glat'] = skydir.galactic.b.deg
o['pos_offset'] = 0.0
return o
def _fit_extension_ebin(self, name, o, **kwargs):
optimizer = kwargs.get('optimizer', {})
spatial_model = kwargs.get('spatial_model')
psf_scale_fn = kwargs.pop('psf_scale_fn', None)
reoptimize = kwargs.pop('reoptimize', True)
src = self.roi.copy_source(name)
self.set_source_morphology(name,
spatial_model='PointSource',
use_pylike=False,
psf_scale_fn=psf_scale_fn)
for i, (logemin, logemax) in enumerate(
zip(self.log_energies[:-1], self.log_energies[1:])):
self.set_energy_range(logemin, logemax)
o.ebin_loglike_ptsrc[i] = -self.like()
self.set_energy_range(self.log_energies[0], self.log_energies[-1])
self.set_source_morphology(name,
spatial_model=src['SpatialModel'],
spatial_pars=src.spatial_pars,
psf_scale_fn=psf_scale_fn,
use_pylike=False)
o.ebin_loglike = self._scan_extension_fast_ebin(
name,
spatial_model=spatial_model,
width=o.width,
optimizer=kwargs['optimizer'],
psf_scale_fn=psf_scale_fn,
reoptimize=False)
for i, (logemin, logemax) in enumerate(
zip(self.log_energies[:-1], self.log_energies[1:])):
ul_data = utils.get_parameter_limits(o.width, o.ebin_loglike[i])
o.ebin_ext[i] = max(ul_data['x0'], 10**-2.5)
o.ebin_ext_err[i] = ul_data['err']
o.ebin_ext_err_lo[i] = ul_data['err_lo']
o.ebin_ext_err_hi[i] = ul_data['err_hi']
o.ebin_ext_ul95[i] = ul_data['ul']
o.ebin_loglike_ext[i] = ul_data['lnlmax']
o.ebin_ts_ext[i] = 2.0 * \
(o.ebin_loglike_ext[i] - o.ebin_loglike_ptsrc[i])
o.ebin_dloglike = o.ebin_loglike - o.ebin_loglike_ptsrc[:, None]
self.set_source_morphology(name,
spatial_model=src['SpatialModel'],
spatial_pars=src.spatial_pars,
use_pylike=False)
def _fit_extension_ebin(self, name, o, **kwargs):
optimizer = kwargs.get('optimizer', {})
spatial_model = kwargs.get('spatial_model')
psf_scale_fn = kwargs.pop('psf_scale_fn', None)
reoptimize = kwargs.pop('reoptimize', True)
src = self.roi.copy_source(name)
self.set_source_morphology(name, spatial_model='PointSource',
use_pylike=False,
psf_scale_fn=psf_scale_fn)
for i, (logemin, logemax) in enumerate(zip(self.log_energies[:-1],
self.log_energies[1:])):
self.set_energy_range(logemin, logemax)
o.ebin_loglike_ptsrc[i] = -self.like()
self.set_energy_range(self.log_energies[0], self.log_energies[-1])
self.set_source_morphology(name, spatial_model=src['SpatialModel'],
spatial_pars=src.spatial_pars,
psf_scale_fn=psf_scale_fn,
use_pylike=False)
o.ebin_loglike = self._scan_extension_fast_ebin(name,
spatial_model=spatial_model,
width=o.width,
optimizer=kwargs[
'optimizer'],
psf_scale_fn=psf_scale_fn,
reoptimize=False)
for i, (logemin, logemax) in enumerate(zip(self.log_energies[:-1],
self.log_energies[1:])):
ul_data = utils.get_parameter_limits(o.width, o.ebin_loglike[i])
o.ebin_ext[i] = max(ul_data['x0'], 10**-2.5)
o.ebin_ext_err[i] = ul_data['err']
o.ebin_ext_err_lo[i] = ul_data['err_lo']
o.ebin_ext_err_hi[i] = ul_data['err_hi']
o.ebin_ext_ul95[i] = ul_data['ul']
o.ebin_loglike_ext[i] = ul_data['lnlmax']
o.ebin_ts_ext[i] = 2.0 * \
(o.ebin_loglike_ext[i] - o.ebin_loglike_ptsrc[i])
o.ebin_dloglike = o.ebin_loglike - o.ebin_loglike_ptsrc[:, None]
self.set_source_morphology(name, spatial_model=src['SpatialModel'],
spatial_pars=src.spatial_pars,
use_pylike=False)
object_fit_ext_dlnl[name] = tab['fit_ext_scan_dlnl']
object_fit_halo_dlnl[name] = fit_halo_scan_dlnl
object_fit_ext_ts[name] = tab['fit_ext_ts']
object_fit_halo_ts[name] = tab['fit_halo_ts']
object_name[name] = tab['name']
# Sum over all sources
fit_halo_dlnl = np.sum(fit_halo_scan_dlnl,axis=0)
fit_ext_dlnl = np.sum(tab['fit_ext_scan_dlnl'],axis=0)
fit_ext_dlnl -= fit_ext_dlnl[0]
for i in range(9):
for j in range(7):
fit_halo_dlnl_sum = fit_halo_dlnl[i,j]
lims = utils.get_parameter_limits(eflux,fit_halo_dlnl_sum)
halo_ts[i,j] = 2.0*lims['lnlmax']
halo_eflux_ul95[i,j] = lims['ul']
#halo_ts = 2.0*np.max(fit_halo_dlnl,axis=2)
row_dict['name'] = name
row_dict['fit_halo_scan_dlnl'] = fit_halo_dlnl
row_dict['fit_halo_scan_ts'] = halo_ts
row_dict['fit_halo_scan_eflux_ul95'] = halo_eflux_ul95
row_dict['fit_ext_dlnl'] = fit_ext_dlnl
ext_lims = utils.get_parameter_limits(ext_width,fit_ext_dlnl)
row_dict['fit_ext_ts'] = 2.0*(ext_lims['lnlmax'])
row_dict['fit_ext_ul95'] = ext_lims['ul']
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 _make_sed(self, name, **config):
bin_index = config['bin_index']
use_local_index = config['use_local_index']
free_background = config['free_background']
free_radius = config['free_radius']
ul_confidence = config['ul_confidence']
cov_scale = config['cov_scale']
loge_bins = config['loge_bins']
if not loge_bins or loge_bins is None:
loge_bins = self.log_energies
else:
loge_bins = np.array(loge_bins)
nbins = len(loge_bins) - 1
max_index = 5.0
min_flux = 1E-30
npts = self.config['gtlike']['llscan_npts']
loge_bounds = self.loge_bounds
# Output Dictionary
o = {'name': name,
'loge_min': loge_bins[:-1],
'loge_max': loge_bins[1:],
'loge_ctr': 0.5 * (loge_bins[:-1] + loge_bins[1:]),
'loge_ref': 0.5 * (loge_bins[:-1] + loge_bins[1:]),
'e_min': 10 ** loge_bins[:-1],
'e_max': 10 ** loge_bins[1:],
'e_ctr': 10 ** (0.5 * (loge_bins[:-1] + loge_bins[1:])),
'e_ref': 10 ** (0.5 * (loge_bins[:-1] + loge_bins[1:])),
'ref_flux': np.zeros(nbins),
'ref_eflux': np.zeros(nbins),
'ref_dnde': np.zeros(nbins),
'ref_dnde_e_min': np.zeros(nbins),
'ref_dnde_e_max': np.zeros(nbins),
'ref_e2dnde': np.zeros(nbins),
'ref_npred': np.zeros(nbins),
'norm': np.zeros(nbins),
'flux': np.zeros(nbins),
'eflux': np.zeros(nbins),
'dnde': np.zeros(nbins),
'e2dnde': np.zeros(nbins),
'index': np.zeros(nbins),
'npred': np.zeros(nbins),
'ts': np.zeros(nbins),
'loglike': np.zeros(nbins),
'norm_scan': np.zeros((nbins, npts)),
'dloglike_scan': np.zeros((nbins, npts)),
'loglike_scan': np.zeros((nbins, npts)),
'fit_quality': np.zeros(nbins),
'fit_status': np.zeros(nbins),
'correlation': {},
'model_flux': {},
'config': config
}
for t in ['norm', 'flux', 'eflux', 'dnde', 'e2dnde']:
o['%s_err' % t] = np.zeros(nbins) * np.nan
o['%s_err_hi' % t] = np.zeros(nbins) * np.nan
o['%s_err_lo' % t] = np.zeros(nbins) * np.nan
o['%s_ul95' % t] = np.zeros(nbins) * np.nan
o['%s_ul' % t] = np.zeros(nbins) * np.nan
saved_state = LikelihoodState(self.like)
source = self.components[0].like.logLike.getSource(str(name))
# Perform global spectral fit
self._latch_free_params()
self.free_sources(False, pars='shape', loglevel=logging.DEBUG)
self.free_source(name, pars=config.get('free_pars', None),
loglevel=logging.DEBUG)
fit_output = self.fit(loglevel=logging.DEBUG, update=False,
min_fit_quality=2)
o['model_flux'] = self.bowtie(name)
spectral_pars = gtutils.get_function_pars_dict(source.spectrum())
o['SpectrumType'] = self.roi[name]['SpectrumType']
o.update(model_utils.pars_dict_to_vectors(o['SpectrumType'],
spectral_pars))
param_names = gtutils.get_function_par_names(o['SpectrumType'])
npar = len(param_names)
o['param_covariance'] = np.empty((npar, npar), dtype=float) * np.nan
pmask0 = np.empty(len(fit_output['par_names']), dtype=bool)
pmask0.fill(False)
pmask1 = np.empty(npar, dtype=bool)
pmask1.fill(False)
for i, pname in enumerate(param_names):
for j, pname2 in enumerate(fit_output['par_names']):
if name != fit_output['src_names'][j]:
continue
if pname != pname2:
continue
pmask0[j] = True
pmask1[i] = True
src_cov = fit_output['covariance'][pmask0, :][:, pmask0]
o['param_covariance'][np.ix_(pmask1, pmask1)] = src_cov
o['param_correlation'] = utils.cov_to_correlation(
o['param_covariance'])
for i, pname in enumerate(param_names):
o['param_covariance'][i, :] *= spectral_pars[pname]['scale']
o['param_covariance'][:, i] *= spectral_pars[pname]['scale']
self._restore_free_params()
self.logger.info('Fitting SED')
# Setup background parameters for SED
self.free_sources(False, pars='shape')
self.free_norm(name)
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)
if cov_scale is not None:
self._latch_free_params()
self.zero_source(name)
self.fit(loglevel=logging.DEBUG, update=False)
srcNames = list(self.like.sourceNames())
srcNames.remove(name)
self.constrain_norms(srcNames, cov_scale)
self.unzero_source(name)
self._restore_free_params()
# Precompute fluxes in each bin from global fit
gf_bin_flux = []
gf_bin_index = []
for i, (logemin, logemax) in enumerate(zip(loge_bins[:-1],
loge_bins[1:])):
emin = 10 ** logemin
emax = 10 ** logemax
delta = 1E-5
f = self.like[name].flux(emin, emax)
f0 = self.like[name].flux(emin * (1 - delta), emin * (1 + delta))
f1 = self.like[name].flux(emax * (1 - delta), emax * (1 + delta))
if f0 > min_flux and f1 > min_flux:
g = 1 - np.log10(f0 / f1) / np.log10(emin / emax)
gf_bin_index += [g]
gf_bin_flux += [f]
else:
gf_bin_index += [max_index]
gf_bin_flux += [min_flux]
old_spectrum = source.spectrum()
old_pars = copy.deepcopy(self.roi[name].spectral_pars)
old_type = self.roi[name]['SpectrumType']
spectrum_pars = {
'Prefactor':
{'value': 1.0, 'scale': 1E-13, 'min': 1E-10,
'max': 1E10, 'free': True},
'Index':
{'value': 2.0, 'scale': -1.0, 'min': 0.0, 'max': 5.0, 'free': False},
'Scale':
{'value': 1E3, 'scale': 1.0, 'min': 1., 'max': 1E6, 'free': False},
}
self.set_source_spectrum(str(name), 'PowerLaw',
spectrum_pars=spectrum_pars,
update_source=False)
src_norm_idx = -1
free_params = self.get_params(True)
for j, p in enumerate(free_params):
if not p['is_norm']:
continue
if p['is_norm'] and p['src_name'] == name:
src_norm_idx = j
o['correlation'][p['src_name']] = np.zeros(nbins) * np.nan
self._fitcache = None
for i, (logemin, logemax) in enumerate(zip(loge_bins[:-1],
loge_bins[1:])):
logectr = 0.5 * (logemin + logemax)
emin = 10 ** logemin
emax = 10 ** logemax
ectr = 10 ** logectr
ectr2 = ectr**2
saved_state_bin = LikelihoodState(self.like)
if use_local_index:
o['index'][i] = -min(gf_bin_index[i], max_index)
else:
o['index'][i] = -bin_index
self.set_norm(name, 1.0, update_source=False)
self.set_parameter(name, 'Index', o['index'][i], scale=1.0,
update_source=False)
self.like.syncSrcParams(str(name))
ref_flux = self.like[name].flux(emin, emax)
o['ref_flux'][i] = self.like[name].flux(emin, emax)
o['ref_eflux'][i] = self.like[name].energyFlux(emin, emax)
o['ref_dnde'][i] = self.like[name].spectrum()(pyLike.dArg(ectr))
o['ref_dnde_e_min'][i] = self.like[
name].spectrum()(pyLike.dArg(emin))
o['ref_dnde_e_max'][i] = self.like[
name].spectrum()(pyLike.dArg(emax))
o['ref_e2dnde'][i] = o['ref_dnde'][i] * ectr2
cs = self.model_counts_spectrum(
name, logemin, logemax, summed=True)
o['ref_npred'][i] = np.sum(cs)
normVal = self.like.normPar(name).getValue()
flux_ratio = gf_bin_flux[i] / ref_flux
newVal = max(normVal * flux_ratio, 1E-10)
self.set_norm(name, newVal, update_source=False)
self.set_norm_bounds(name, [newVal * 1E-6, newVal * 1E4])
self.like.syncSrcParams(str(name))
self.free_norm(name)
self.logger.debug('Fitting %s SED from %.0f MeV to %.0f MeV' %
(name, emin, emax))
self.set_energy_range(logemin, logemax)
fit_output = self._fit(**config['optimizer'])
free_params = self.get_params(True)
for j, p in enumerate(free_params):
if not p['is_norm']:
continue
o['correlation'][p['src_name']][i] = \
fit_output['correlation'][src_norm_idx, j]
o['fit_quality'][i] = fit_output['fit_quality']
o['fit_status'][i] = fit_output['fit_status']
flux = self.like[name].flux(emin, emax)
eflux = self.like[name].energyFlux(emin, emax)
dnde = self.like[name].spectrum()(pyLike.dArg(ectr))
o['norm'][i] = flux / o['ref_flux'][i]
o['flux'][i] = flux
o['eflux'][i] = eflux
o['dnde'][i] = dnde
o['e2dnde'][i] = dnde * ectr2
cs = self.model_counts_spectrum(name, logemin,
logemax, summed=True)
o['npred'][i] = np.sum(cs)
o['loglike'][i] = fit_output['loglike']
lnlp = self.profile_norm(name, logemin=logemin, logemax=logemax,
savestate=True, reoptimize=True,
npts=npts, optimizer=config['optimizer'])
o['ts'][i] = max(
2.0 * (fit_output['loglike'] - lnlp['loglike'][0]), 0.0)
o['loglike_scan'][i] = lnlp['loglike']
o['dloglike_scan'][i] = lnlp['dloglike']
o['norm_scan'][i] = lnlp['flux'] / ref_flux
ul_data = utils.get_parameter_limits(
lnlp['flux'], lnlp['dloglike'])
o['norm_err_hi'][i] = ul_data['err_hi'] / ref_flux
o['norm_err_lo'][i] = ul_data['err_lo'] / ref_flux
if np.isfinite(ul_data['err_lo']):
o['norm_err'][i] = 0.5 * (ul_data['err_lo'] +
ul_data['err_hi']) / ref_flux
else:
o['norm_err'][i] = ul_data['err_hi'] / ref_flux
o['norm_ul95'][i] = ul_data['ul'] / ref_flux
ul_data = utils.get_parameter_limits(lnlp['flux'],
lnlp['dloglike'],
cl_limit=ul_confidence)
o['norm_ul'][i] = ul_data['ul'] / ref_flux
saved_state_bin.restore()
for t in ['flux', 'eflux', 'dnde', 'e2dnde']:
o['%s_err' % t] = o['norm_err'] * o['ref_%s' % t]
o['%s_err_hi' % t] = o['norm_err_hi'] * o['ref_%s' % t]
o['%s_err_lo' % t] = o['norm_err_lo'] * o['ref_%s' % t]
o['%s_ul95' % t] = o['norm_ul95'] * o['ref_%s' % t]
o['%s_ul' % t] = o['norm_ul'] * o['ref_%s' % t]
self.set_energy_range(loge_bounds[0], loge_bounds[1])
self.set_source_spectrum(str(name), old_type,
spectrum_pars=old_pars,
update_source=False)
saved_state.restore()
self._sync_params(name)
if cov_scale is not None:
self.remove_priors()
return o
def _fit_extension(self, name, **kwargs):
spatial_model = kwargs.get('spatial_model', 'RadialGaussian')
optimizer = kwargs.get('optimizer', {})
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'] > 0.1):
width_lo = np.logspace(-2.0, np.log10(src['SpatialWidth']), 11)
width_hi = np.logspace(np.log10(src['SpatialWidth']), 0.5, 11)
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(-2.0, 0.5, 21)
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, 10**0.5])
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(11, 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, 10**0.5])
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_halo_scan(gta,
modelname,
src_name,
halo_width,
halo_index,
spatial_model='RadialGaussian',
loge_bounds=None,
optimizer='NEWTON'):
gta.logger.info('Starting Halo Scan %s' % (modelname))
halo_source_name = 'halo_' + spatial_model
halo_source_dict = {
'SpectrumType': 'PowerLaw',
'Index': {
'value': 2.0,
'scale': -1.0,
'min': 0.5,
'max': 4.5
},
'Scale': 1000,
'Prefactor': {
'value': 1E-5,
'scale': 1e-13,
'min': 1E-5,
'max': 1E4
},
'SpatialModel': spatial_model,
'SpatialWidth': 1.0
}
outprefix = '%s_%s' % (modelname, halo_source_name)
halo_source_dict['ra'] = gta.roi[src_name]['ra']
halo_source_dict['dec'] = gta.roi[src_name]['dec']
#gta.load_roi(modelname)
#if loge_bounds is not None:
# gta.set_energy_range(loge_bounds[0],loge_bounds[1])
skydir = gta.roi[src_name].skydir
diff_sources = [s.name for s in gta.roi.sources if s.diffuse]
gta.free_sources(False)
gta.free_sources(skydir=skydir,
distance=1.0,
pars='norm',
exclude=diff_sources)
gta.write_xml(modelname + '_base')
halo_tab = gta.roi.create_table([])
halo_tab_idx_free = gta.roi.create_table([])
halo_data = []
halo_data_idx_free = []
for i, w in enumerate(halo_width):
gta.logger.info('Fitting Halo Width %.3f', w)
halo_source_dict['SpatialWidth'] = w
gta.load_xml(modelname + '_base')
gta.add_source(halo_source_name, halo_source_dict, free=True)
# Free Index
gta.free_norm(halo_source_name)
gta.fit(optimizer=optimizer)
gta.sed(halo_source_name,
prefix='%s_cov05_%02i' % (modelname, i),
outfile='%s_cov05_%02i_sed' % (outprefix, i),
free_radius=1.0,
cov_scale=5.0,
optimizer={'optimizer': 'MINUIT'},
make_plots=False)
gta.sed(halo_source_name,
prefix='%s_cov10_%02i' % (modelname, i),
outfile='%s_cov10_%02i_sed' % (outprefix, i),
free_radius=1.0,
cov_scale=10.0,
optimizer={'optimizer': 'MINUIT'},
make_plots=False)
gta.free_parameter(halo_source_name, 'Index')
gta.fit(optimizer=optimizer)
gta.free_parameter(halo_source_name, 'Index', False)
gta.update_source(halo_source_name,
reoptimize=True,
optimizer={'optimizer': optimizer})
halo_data_idx_free += [copy.deepcopy(gta.roi[halo_source_name].data)]
gta.roi[halo_source_name].add_to_table(halo_tab_idx_free)
gta.write_roi('%s_%02i' % (outprefix, i), make_plots=False)
gta.print_params(loglevel=logging.DEBUG)
# Scan over fixed index
for j, idx in enumerate(halo_index):
gta.logger.info('Fitting Halo Index %.3f', idx)
model_idx = i * len(halo_index) + j
gta.set_norm(halo_source_name, 0.1, update_source=False)
gta.set_parameter(halo_source_name,
'Index',
-1.0 * idx,
update_source=False)
gta.fit(update=False, optimizer=optimizer)
gta.print_params(loglevel=logging.DEBUG)
gta.update_source(halo_source_name,
reoptimize=True,
optimizer={'optimizer': optimizer})
ul_flux = get_parameter_limits(
gta.roi[halo_source_name]['flux_scan'],
gta.roi[halo_source_name]['loglike_scan'])
ul_eflux = get_parameter_limits(
gta.roi[halo_source_name]['eflux_scan'],
gta.roi[halo_source_name]['loglike_scan'])
gta.roi[halo_source_name]['flux_err'] = ul_flux['err']
gta.roi[halo_source_name]['eflux_err'] = ul_eflux['err']
gta.logger.info(
'%s Halo Width: %6.3f Index: %6.2f TS: %6.2f Flux: %8.4g',
modelname, w, idx, gta.roi[halo_source_name]['ts'],
gta.roi[halo_source_name]['flux'])
#gta.write_roi('%s_%02i_%02i'%(outprefix,i,j),make_plots=False)
halo_data += [copy.deepcopy(gta.roi[halo_source_name].data)]
gta.roi[halo_source_name].add_to_table(halo_tab)
gta.delete_source(halo_source_name, save_template=False)
np.save(os.path.join(gta.workdir, '%s_data.npy' % outprefix), halo_data)
np.save(os.path.join(gta.workdir, '%s_data_idx_free.npy' % outprefix),
halo_data_idx_free)
tab_halo_width, tab_halo_index = np.meshgrid(halo_width,
halo_index,
indexing='ij')
halo_tab['halo_width'] = np.ravel(tab_halo_width)
halo_tab['halo_index'] = np.ravel(tab_halo_index)
halo_tab_idx_free['halo_width'] = halo_width
stack_files(
sorted(
glob.glob(os.path.join(gta.workdir, '%s*cov05*fits' % outprefix))),
os.path.join(gta.workdir, '%s_cov05_sed.fits' % outprefix),
new_cols=[Column(name='halo_width', data=halo_width, unit='deg')])
stack_files(
sorted(
glob.glob(os.path.join(gta.workdir, '%s*cov10*fits' % outprefix))),
os.path.join(gta.workdir, '%s_cov10_sed.fits' % outprefix),
new_cols=[Column(name='halo_width', data=halo_width, unit='deg')])
halo_tab.write(os.path.join(gta.workdir, '%s_data.fits' % outprefix),
overwrite=True)
halo_tab_idx_free.write(os.path.join(gta.workdir,
'%s_data_idx_free.fits' % outprefix),
overwrite=True)
gta.logger.info('Finished Halo Scan %s' % (modelname))
def run_composite2(lst_inputs, path_outdir, names_params_tied_universal=['Index'], names_params_tied_category=['Prefactor'], ncategory=0, str_suffix=''):
# Open table
tb = ReadLTFCatalogueInfo.open_table()
# Definition of GBM fluence categories
FLUENCE_CUT = [1.09e-04, 3.06e-05] #[1.45E-4, 3.70E-5] # Top 10%, 35%
NCATEGORIES_FLUENCE = len(FLUENCE_CUT)+1
dct_category_fluence = {}
# rh_fluence_weightNobs = ROOT.TH1D('roohtg', 'GBM Fluence', 100, -7, -2)
path_base = os.getcwd()
os.chdir(path_outdir)
lst_name_subdir = ['plots', 'xml', 'fits']
for name_subdir in lst_name_subdir:
path_subdir = '{0}/{1}'.format(path_outdir, name_subdir)
if not os.path.exists(path_subdir):
os.makedirs(path_subdir)
if str_suffix != '':
str_suffix = '_' + str_suffix
irfs = 'P8R2_SOURCE_V6' # To be used with P301 data
optimizer='Minuit'
level = 2.71
like={}
targets = []
lst_fluence_gbm = []
lst_fluence_gbm_err = []
lst_nobs_lat = []
for (itarget, path_target) in enumerate(lst_inputs):
path_base, name_base = os.path.split(path_target)
target = name_base[3:12]
targets.append(target)
print '##### No.{0} {1} #####'.format(itarget, target)
dct_category_fluence[target] = judge_category_fluence(tb, target, FLUENCE_CUT)
if ncategory-1 not in (dct_category_fluence[target], -1):
print 'skipped.'
continue
ltcube = '/'.join((path_base, name_base+'_ft1_ltCube.fits'))
expMap = '/'.join((path_base, name_base+'_ft1_expMap.fits'))
srcModel = '/'.join((path_base, name_base+'_ft1_model.xml'))
evt = '/'.join((path_base, name_base+'_ft1_filtered.fits'))
sc = '/'.join((path_base, '../../../../..', name_base.replace('_P8_P302_BASE_T00-999-101000_r030', '_T00-999-101000_ft2-30s.fits')))
if itarget==0:
print 'Files of the first target.'
print ' Event:', evt
print ' Spacecraft:', sc
print ' Livetime cube:', ltcube
print ' Exposure map:', expMap
print ' Source model:', srcModel
# Diffuse responses
my_apps.diffResps['evfile'] = evt
my_apps.diffResps['scfile'] = sc
my_apps.diffResps['srcmdl'] = srcModel
my_apps.diffResps['irfs'] = irfs
my_apps.diffResps.run()
like[target] = unbinnedAnalysis(evfile=evt,
scfile=sc,
expmap=expMap,
expcube=ltcube,
irfs=irfs,
srcmdl=srcModel,
optimizer=optimizer)
for source in like[target].sourceNames():
if source not in (target):
like[target].normPar(source).setFree(False)
sys.stdout.flush()
CompositeLike = []
# Scan ecutoff
xvals = 10 ** np.linspace(2.0, 5.125, 26)
ecutoff_lim95 = {}
ebreak_fixed = 10.
for icat in range(NCATEGORIES_FLUENCE):
print '======================'
print '===== Category', icat, '====='
print '======================'
if ncategory-1 not in (icat, -1):
print 'skipped.'
continue
fit_results = []
for i, x in enumerate(xvals):
print '---------------'
print 'Cutoff energy:', x, 'MeV'
tiedParams_category = {}
tiedParams_universal = {}
for par in names_params_tied_category:
tiedParams_category[par] = []
for par in names_params_tied_universal:
tiedParams_universal[par] = []
CompositeLike = Composite2(optimizer=optimizer)
for target in targets:
if dct_category_fluence[target]==icat:
print target
# Fixing cutoff energy
ecutoff_index = like[target].par_index(target, 'P1')
print like[target][ecutoff_index]
like[target][ecutoff_index] = x
like[target].freeze(ecutoff_index)
print " {0}'s P1 is freezed as {1}".format(target, like[target][ecutoff_index])
# Fixing break energy
ebreak_index = like[target].par_index(target, 'Ebreak')
print like[target][ebreak_index]
like[target][ebreak_index] = ebreak_fixed
like[target].freeze(ebreak_index)
print " {0}'s Ebreak is freezed as {1}".format(target, like[target][ebreak_index])
CompositeLike.addComponent(like[target])
# Tying parameters for each fluence category separately
for par in names_params_tied_category:
tiedParams_category[par].append(tuple([like[target], target, par]))
# Tying parameters universaly
for par in names_params_tied_universal:
tiedParams_universal[par].append(tuple([like[target], target, par]))
sys.stdout.flush()
print '* Parameters tied by each category:'
print tiedParams_category
print '* Parameters tied universaly:'
print tiedParams_universal
for par in names_params_tied_category:
CompositeLike.tieParameters(tuple(tiedParams_category[par]))
for par in names_params_tied_universal:
CompositeLike.tieParameters(tuple(tiedParams_universal[par]))
fit_results.append(CompositeLike.fit(covar=False,tol=1.e-2,optimizer=optimizer))
# Limit of ecutoff
loglike_inversed_scanned = np.array(fit_results)
loglike_inversed_min = min(loglike_inversed_scanned)
print '* Negative log-likelihood :'
for (i,s) in enumerate(loglike_inversed_scanned):
print '{0} ({1}) at {2} MeV'.format(s, s-loglike_inversed_min, xvals[i])
ecutoff_lim95[icat] = get_parameter_limits(xvals, -1*loglike_inversed_scanned, cl_limit=0.99540, cl_err=0.9398)
print '* 95% limit of cutoff energy:'
print ecutoff_lim95
