def localize(self, name, **kwargs):
"""Find the best-fit position of a source. Localization is
performed in two steps. First a TS map is computed centered
on the source with half-width set by ``dtheta_max``. A fit is
then performed to the maximum TS peak in this map. The source
position is then further refined by scanning the likelihood in
the vicinity of the peak found in the first step. The size of
the scan region is set to encompass the 99% positional
uncertainty contour as determined from the peak fit.
Parameters
----------
name : str
Source name.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
localize : dict
Dictionary containing results of the localization
analysis.
"""
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['localize'],
optimizer=self.defaults['optimizer'])
schema.add_option('use_cache', True)
schema.add_option('prefix', '')
config = utils.create_dict(self.config['localize'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Running localization for %s' % name)
free_state = FreeParameterState(self)
loc = self._localize(name, **config)
free_state.restore()
self.logger.info('Finished localization.')
if config['make_plots']:
self._plotter.make_localization_plots(loc, self.roi,
prefix=config['prefix'])
outfile = \
utils.format_filename(self.workdir, 'loc',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
if config['write_fits']:
loc['file'] = os.path.basename(outfile) + '.fits'
self._make_localize_fits(loc, outfile + '.fits',
**config)
if config['write_npy']:
np.save(outfile + '.npy', dict(loc))
return loc
def localize(self, name, **kwargs):
"""Find the best-fit position of a source. Localization is
performed in two steps. First a TS map is computed centered
on the source with half-width set by ``dtheta_max``. A fit is
then performed to the maximum TS peak in this map. The source
position is then further refined by scanning the likelihood in
the vicinity of the peak found in the first step. The size of
the scan region is set to encompass the 99% positional
uncertainty contour as determined from the peak fit.
Parameters
----------
name : str
Source name.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
localize : dict
Dictionary containing results of the localization
analysis.
"""
timer = Timer.create(start=True)
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['localize'],
optimizer=self.defaults['optimizer'])
schema.add_option('use_cache', True)
schema.add_option('prefix', '')
config = utils.create_dict(self.config['localize'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Running localization for %s' % name)
free_state = FreeParameterState(self)
loc = self._localize(name, **config)
free_state.restore()
self.logger.info('Finished localization.')
if config['make_plots']:
self._plotter.make_localization_plots(loc, self.roi,
prefix=config['prefix'])
outfile = \
utils.format_filename(self.workdir, 'loc',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
if config['write_fits']:
loc['file'] = os.path.basename(outfile) + '.fits'
self._make_localize_fits(loc, outfile + '.fits',
**config)
if config['write_npy']:
np.save(outfile + '.npy', dict(loc))
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return loc
def extension(self, name, **kwargs):
"""Test this source for spatial extension with the likelihood
ratio method (TS_ext). This method will substitute an
extended spatial model for the given source and perform a
one-dimensional scan of the spatial extension parameter over
the range specified with the width parameters. The 1-D
profile likelihood is then used to compute the best-fit value,
upper limit, and TS for extension. The nuisance parameters
that will be simultaneously fit when performing the spatial
scan can be controlled with the ``fix_shape``,
``free_background``, and ``free_radius`` options. By default
the position of the source will be fixed to its current
position. A simultaneous fit to position and extension can be
performed by setting ``fit_position`` to True.
Parameters
----------
name : str
Source name.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
extension : dict
Dictionary containing results of the extension analysis. The same
dictionary is also saved to the dictionary of this source under
'extension'.
"""
timer = Timer.create(start=True)
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['extension'],
optimizer=self.defaults['optimizer'])
schema.add_option('prefix', '')
schema.add_option('outfile', None, '', str)
config = utils.create_dict(self.config['extension'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Running extension fit for %s', name)
free_state = FreeParameterState(self)
ext = self._extension(name, **config)
free_state.restore()
self.logger.info('Finished extension fit.')
if config['make_plots']:
self._plotter.make_extension_plots(ext,
self.roi,
prefix=config['prefix'])
outfile = config.get('outfile', None)
if outfile is None:
outfile = utils.format_filename(
self.workdir,
'ext',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
else:
outfile = os.path.join(self.workdir, os.path.splitext(outfile)[0])
if config['write_fits']:
self._make_extension_fits(ext, outfile + '.fits')
if config['write_npy']:
o_copy = dict(ext)
self.logger.warning(
'Saving maps in .npy files is disabled b/c of incompatibilities in python3, remove the maps from the %s.npy'
% outfile)
print(o_copy)
for xrm in ['tsmap']:
o_copy.pop(xrm)
np.save(outfile + '.npy', o_copy)
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return ext
def extension(self, name, **kwargs):
"""Test this source for spatial extension with the likelihood
ratio method (TS_ext). This method will substitute an
extended spatial model for the given source and perform a
one-dimensional scan of the spatial extension parameter over
the range specified with the width parameters. The 1-D
profile likelihood is then used to compute the best-fit value,
upper limit, and TS for extension. The nuisance parameters
that will be simultaneously fit when performing the spatial
scan can be controlled with the ``fix_shape``,
``free_background``, and ``free_radius`` options. By default
the position of the source will be fixed to its current
position. A simultaneous fit to position and extension can be
performed by setting ``fit_position`` to True.
Parameters
----------
name : str
Source name.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
extension : dict
Dictionary containing results of the extension analysis. The same
dictionary is also saved to the dictionary of this source under
'extension'.
"""
timer = Timer.create(start=True)
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['extension'],
optimizer=self.defaults['optimizer'])
schema.add_option('prefix', '')
schema.add_option('outfile', None, '', str)
config = utils.create_dict(self.config['extension'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Running extension fit for %s', name)
free_state = FreeParameterState(self)
ext = self._extension(name, **config)
free_state.restore()
self.logger.info('Finished extension fit.')
if config['make_plots']:
self._plotter.make_extension_plots(ext, self.roi,
prefix=config['prefix'])
outfile = config.get('outfile', None)
if outfile is None:
outfile = utils.format_filename(self.workdir, 'ext',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
else:
outfile = os.path.join(self.workdir,
os.path.splitext(outfile)[0])
if config['write_fits']:
self._make_extension_fits(ext, outfile + '.fits')
if config['write_npy']:
np.save(outfile + '.npy', dict(ext))
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return ext
def _fit_lc(gta, name, **kwargs):
# lightcurve fitting routine-
# 1.) start by freeing target and provided list of
# sources, fix all else- if fit fails, fix all pars
# except norm and try again
# 2.) if that fails to converge then try fixing low TS
# (<4) sources and then refit
# 3.) if that fails to converge then try fixing low-moderate TS (<9) sources and then refit
# 4.) if that fails then fix sources out to 1dg away from center of ROI
# 5.) if that fails set values to 0 in output and print warning message
free_sources = kwargs.get('free_sources', [])
free_background = kwargs.get('free_background', False)
free_params = kwargs.get('free_params', None)
shape_ts_threshold = kwargs.get('shape_ts_threshold', 16)
max_free_sources = kwargs.get('max_free_sources', 5)
if name in free_sources:
free_sources.remove(name)
free_state = FreeParameterState(gta)
gta.free_sources(free=False)
gta.free_sources_by_name(free_sources + [name], pars='norm')
gta.fit()
free_sources = sorted(free_sources,
key=lambda t: gta.roi[t]['ts']
if np.isfinite(gta.roi[t]['ts']) else -100.,
reverse=True)
free_sources = free_sources[:max_free_sources]
free_sources_norm = free_sources + [name]
free_sources_shape = []
for t in free_sources_norm:
if gta.roi[t]['ts'] > shape_ts_threshold:
free_sources_shape += [t]
gta.free_sources(free=False)
gta.logger.debug('Free Sources Norm: %s', free_sources_norm)
gta.logger.debug('Free Sources Shape: %s', free_sources_shape)
for niter in range(5):
if free_background:
free_state.restore()
if free_params:
gta.free_source(name, pars=free_params)
if niter == 0:
gta.free_sources_by_name(free_sources_norm, pars='norm')
gta.free_sources_by_name(free_sources_shape, pars='shape')
elif niter == 1:
gta.logger.info('Fit Failed. Retrying with free '
'normalizations.')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources_norm, pars='norm')
elif niter == 2:
gta.logger.info('Fit Failed with User Supplied List of '
'Free/Fixed Sources.....Lets try '
'fixing TS<4 sources')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources_norm, pars='norm')
gta.free_sources(minmax_ts=[None, 4], free=False, exclude=[name])
elif niter == 3:
gta.logger.info('Fit Failed with User Supplied List of '
'Free/Fixed Sources.....Lets try '
'fixing TS<9 sources')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources_norm, pars='norm')
gta.free_sources(minmax_ts=[None, 9], free=False, exclude=[name])
elif niter == 4:
gta.logger.info('Fit still did not converge, lets try fixing the '
'sources up to 1dg out from ROI')
gta.free_sources_by_name(free_sources, False)
for s in free_sources:
src = gta.roi.get_source_by_name(s)
if src['offset'] < 1.0:
gta.free_source(s, pars='norm')
gta.free_sources(minmax_ts=[None, 9], free=False, exclude=[name])
else:
gta.logger.error('Fit still didnt converge.....please examine this data '
'point, setting output to 0')
break
fit_results = gta.fit()
if fit_results['fit_success'] is True:
break
return fit_results
def _fit_lc(self, gta, name, **kwargs):
# lightcurve fitting routine-
# 1.) start by freeing target and provided list of
# sources, fix all else- if fit fails, fix all pars
# except norm and try again
# 2.) if that fails to converge then try fixing low TS
# (<4) sources and then refit
# 3.) if that fails to converge then try fixing low-moderate TS (<9) sources and then refit
# 4.) if that fails then fix sources out to 1dg away from center of ROI
# 5.) if that fails set values to 0 in output and print warning message
free_sources = kwargs.get('free_sources', [])
free_background = kwargs.get('free_background', False)
free_params = kwargs.get('free_params', None)
if name in free_sources:
free_sources.remove(name)
free_state = FreeParameterState(gta)
gta.free_sources(free=False)
for niter in range(5):
if free_background:
free_state.restore()
gta.free_source(name, pars=free_params)
if niter == 0:
gta.free_sources_by_name(free_sources)
elif niter == 1:
self.logger.info('Fit Failed. Retrying with free '
'normalizations.')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources, pars='norm')
elif niter == 2:
self.logger.info('Fit Failed with User Supplied List of '
'Free/Fixed Sources.....Lets try '
'fixing TS<4 sources')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources, pars='norm')
gta.free_sources(minmax_ts=[0, 4], free=False, exclude=[name])
elif niter == 3:
self.logger.info('Fit Failed with User Supplied List of '
'Free/Fixed Sources.....Lets try '
'fixing TS<9 sources')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources, pars='norm')
gta.free_sources(minmax_ts=[0, 9], free=False, exclude=[name])
elif niter == 4:
self.logger.info('Fit still did not converge, lets try fixing the '
'sources up to 1dg out from ROI')
gta.free_sources_by_name(free_sources, False)
for s in free_sources:
src = self.roi.get_source_by_name(s)
if src['offset'] < 1.0:
gta.free_source(s, pars='norm')
gta.free_sources(minmax_ts=[0, 9], free=False, exclude=[name])
else:
self.logger.error('Fit still didnt converge.....please examine this data '
'point, setting output to 0')
break
fit_results = gta.fit()
if fit_results['fit_success'] is True:
break
return fit_results
def _fit_lc(gta, name, **kwargs):
# lightcurve fitting routine-
# 1.) start by freeing target and provided list of
# sources, fix all else- if fit fails, fix all pars
# except norm and try again
# 2.) if that fails to converge then try fixing low TS
# (<4) sources and then refit
# 3.) if that fails to converge then try fixing low-moderate TS (<9) sources and then refit
# 4.) if that fails then fix sources out to 1dg away from center of ROI
# 5.) if that fails set values to 0 in output and print warning message
free_sources = kwargs.get('free_sources', [])
free_background = kwargs.get('free_background', False)
free_params = kwargs.get('free_params', None)
shape_ts_threshold = kwargs.get('shape_ts_threshold', 16)
max_free_sources = kwargs.get('max_free_sources', 5)
if name in free_sources:
free_sources.remove(name)
free_state = FreeParameterState(gta)
gta.free_sources(free=False)
gta.free_sources_by_name(free_sources + [name], pars='norm')
gta.fit()
free_sources = sorted(free_sources,
key=lambda t: gta.roi[t]['ts']
if np.isfinite(gta.roi[t]['ts']) else -100.,
reverse=True)
free_sources = free_sources[:max_free_sources]
free_sources_norm = free_sources + [name]
free_sources_shape = []
for t in free_sources_norm:
if gta.roi[t]['ts'] > shape_ts_threshold:
free_sources_shape += [t]
gta.free_sources(free=False)
gta.logger.debug('Free Sources Norm: %s', free_sources_norm)
gta.logger.debug('Free Sources Shape: %s', free_sources_shape)
for niter in range(5):
if free_background:
free_state.restore()
if free_params:
gta.free_source(name, pars=free_params)
if niter == 0:
gta.free_sources_by_name(free_sources_norm, pars='norm')
gta.free_sources_by_name(free_sources_shape, pars='shape')
elif niter == 1:
gta.logger.info('Fit Failed. Retrying with free '
'normalizations.')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources_norm, pars='norm')
elif niter == 2:
gta.logger.info('Fit Failed with User Supplied List of '
'Free/Fixed Sources.....Lets try '
'fixing TS<4 sources')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources_norm, pars='norm')
gta.free_sources(minmax_ts=[None, 4], free=False, exclude=[name])
elif niter == 3:
gta.logger.info('Fit Failed with User Supplied List of '
'Free/Fixed Sources.....Lets try '
'fixing TS<9 sources')
gta.free_sources_by_name(free_sources, False)
gta.free_sources_by_name(free_sources_norm, pars='norm')
gta.free_sources(minmax_ts=[None, 9], free=False, exclude=[name])
elif niter == 4:
gta.logger.info('Fit still did not converge, lets try fixing the '
'sources up to 1dg out from ROI')
gta.free_sources_by_name(free_sources, False)
for s in free_sources:
src = gta.roi.get_source_by_name(s)
if src['offset'] < 1.0:
gta.free_source(s, pars='norm')
gta.free_sources(minmax_ts=[None, 9], free=False, exclude=[name])
else:
gta.logger.error('Fit still didnt converge.....please examine this data '
'point, setting output to 0')
break
fit_results = gta.fit()
if fit_results['fit_success'] is True:
break
return fit_results