def residmap(self, prefix='', **kwargs):
"""Generate 2-D spatial residual maps using the current ROI
model and the convolution kernel defined with the `model`
argument.
Parameters
----------
prefix : str
String that will be prefixed to the output residual map files.
{options}
Returns
-------
maps : dict
A dictionary containing the `~fermipy.utils.Map` objects
for the residual significance and amplitude.
"""
timer = Timer.create(start=True)
self.logger.info('Generating residual maps')
schema = ConfigSchema(self.defaults['residmap'])
config = schema.create_config(self.config['residmap'], **kwargs)
# Defining default properties of test source model
config['model'].setdefault('Index', 2.0)
config['model'].setdefault('SpectrumType', 'PowerLaw')
config['model'].setdefault('SpatialModel', 'PointSource')
config['model'].setdefault('Prefactor', 1E-13)
o = self._make_residual_map(prefix, **config)
if config['make_plots']:
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.make_residmap_plots(o, self.roi)
self.logger.info('Finished residual maps')
outfile = utils.format_filename(self.workdir,
'residmap',
prefix=[o['name']])
if config['write_fits']:
o['file'] = os.path.basename(outfile) + '.fits'
self._make_residmap_fits(o, outfile + '.fits')
if config['write_npy']:
np.save(outfile + '.npy', o)
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return o
def residmap(self, prefix='', **kwargs):
"""Generate 2-D spatial residual maps using the current ROI
model and the convolution kernel defined with the `model`
argument.
Parameters
----------
prefix : str
String that will be prefixed to the output residual map files.
{options}
Returns
-------
maps : dict
A dictionary containing the `~fermipy.utils.Map` objects
for the residual significance and amplitude.
"""
timer = Timer.create(start=True)
self.logger.info('Generating residual maps')
schema = ConfigSchema(self.defaults['residmap'])
config = schema.create_config(self.config['residmap'], **kwargs)
# Defining default properties of test source model
config['model'].setdefault('Index', 2.0)
config['model'].setdefault('SpectrumType', 'PowerLaw')
config['model'].setdefault('SpatialModel', 'PointSource')
config['model'].setdefault('Prefactor', 1E-13)
o = self._make_residual_map(prefix, **config)
if config['make_plots']:
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.make_residmap_plots(o, self.roi)
self.logger.info('Finished residual maps')
outfile = utils.format_filename(self.workdir, 'residmap',
prefix=[o['name']])
if config['write_fits']:
o['file'] = os.path.basename(outfile) + '.fits'
self._make_residmap_fits(o, outfile + '.fits')
if config['write_npy']:
np.save(outfile + '.npy', o)
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return o
def find_sources(self, prefix='', **kwargs):
"""An iterative source-finding algorithm that uses likelihood
ratio (TS) maps of the region of interest to find new sources.
After each iteration a new TS map is generated incorporating
sources found in the previous iteration. The method stops
when the number of iterations exceeds ``max_iter`` or no
sources exceeding ``sqrt_ts_threshold`` are found.
Parameters
----------
{options}
tsmap : dict
Keyword arguments dictionary for tsmap method.
tscube : dict
Keyword arguments dictionary for tscube method.
Returns
-------
peaks : list
List of peak objects.
sources : list
List of source objects.
"""
timer = Timer.create(start=True)
self.logger.info('Starting.')
schema = ConfigSchema(self.defaults['sourcefind'],
tsmap=self.defaults['tsmap'],
tscube=self.defaults['tscube'])
schema.add_option('search_skydir', None, '', SkyCoord)
schema.add_option('search_minmax_radius', [None, 1.0], '', list)
config = utils.create_dict(self.config['sourcefind'],
tsmap=self.config['tsmap'],
tscube=self.config['tscube'])
config = schema.create_config(config, **kwargs)
# Defining default properties of test source model
config['model'].setdefault('Index', 2.0)
config['model'].setdefault('SpectrumType', 'PowerLaw')
config['model'].setdefault('SpatialModel', 'PointSource')
config['model'].setdefault('Prefactor', 1E-13)
o = {'sources': [], 'peaks': []}
for i in range(config['max_iter']):
srcs, peaks = self._find_sources_iterate(prefix, i, **config)
self.logger.info('Found %i sources in iteration %i.' %
(len(srcs), i))
o['sources'] += srcs
o['peaks'] += peaks
if len(srcs) == 0:
break
self.logger.info('Done.')
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return o
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 tsmap(self, prefix='', **kwargs):
"""Generate a spatial TS map for a source component with
properties defined by the `model` argument. The TS map will
have the same geometry as the ROI. The output of this method
is a dictionary containing `~fermipy.skymap.Map` objects with
the TS and amplitude of the best-fit test source. By default
this method will also save maps to FITS files and render them
as image files.
This method uses a simplified likelihood fitting
implementation that only fits for the normalization of the
test source. Before running this method it is recommended to
first optimize the ROI model (e.g. by running
:py:meth:`~fermipy.gtanalysis.GTAnalysis.optimize`).
Parameters
----------
prefix : str
Optional string that will be prepended to all output files.
{options}
Returns
-------
tsmap : dict
A dictionary containing the `~fermipy.skymap.Map` objects
for TS and source amplitude.
"""
timer = Timer.create(start=True)
schema = ConfigSchema(self.defaults['tsmap'])
schema.add_option('loglevel', logging.INFO)
schema.add_option('map_skydir', None, '', astropy.coordinates.SkyCoord)
schema.add_option('map_size', 1.0)
schema.add_option('threshold', 1E-2, '', float)
schema.add_option('use_pylike', True, '', bool)
schema.add_option('outfile', None, '', str)
config = schema.create_config(self.config['tsmap'], **kwargs)
# Defining default properties of test source model
config['model'].setdefault('Index', 2.0)
config['model'].setdefault('SpectrumType', 'PowerLaw')
config['model'].setdefault('SpatialModel', 'PointSource')
self.logger.log(config['loglevel'], 'Generating TS map')
o = self._make_tsmap_fast(prefix, **config)
if config['make_plots']:
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.make_tsmap_plots(o, self.roi)
self.logger.log(config['loglevel'], 'Finished TS map')
outfile = config.get('outfile', None)
if outfile is None:
outfile = utils.format_filename(self.workdir,
'tsmap',
prefix=[o['name']])
else:
outfile = os.path.join(self.workdir, os.path.splitext(outfile)[0])
if config['write_fits']:
o['file'] = os.path.basename(outfile) + '.fits'
self._make_tsmap_fits(o, outfile + '.fits')
if config['write_npy']:
np.save(outfile + '.npy', o)
self.logger.log(config['loglevel'], 'Execution time: %.2f s',
timer.elapsed_time)
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
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 sed(self, name, **kwargs):
"""Generate a spectral energy distribution (SED) for a source. This
function will fit the normalization of the source in each
energy bin. By default the SED will be generated with the
analysis energy bins but a custom binning can be defined with
the ``loge_bins`` parameter.
Parameters
----------
name : str
Source name.
prefix : str
Optional string that will be prepended to all output files
(FITS and rendered images).
loge_bins : `~numpy.ndarray`
Sequence of energies in log10(E/MeV) defining the edges of
the energy bins. If this argument is None then the
analysis energy bins will be used. The energies in this
sequence must align with the bin edges of the underyling
analysis instance.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
sed : dict
Dictionary containing output of the SED analysis.
"""
timer = Timer.create(start=True)
name = self.roi.get_source_by_name(name).name
# Create schema for method configuration
schema = ConfigSchema(self.defaults['sed'],
optimizer=self.defaults['optimizer'])
schema.add_option('prefix', '')
schema.add_option('outfile', None, '', str)
schema.add_option('loge_bins', None, '', list)
config = utils.create_dict(self.config['sed'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Computing SED for %s' % name)
o = self._make_sed(name, **config)
self.logger.info('Finished SED')
outfile = config.get('outfile', None)
if outfile is None:
outfile = utils.format_filename(self.workdir, 'sed',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
else:
outfile = os.path.join(self.workdir,
os.path.splitext(outfile)[0])
o['file'] = None
if config['write_fits']:
o['file'] = os.path.basename(outfile) + '.fits'
self._make_sed_fits(o, outfile + '.fits', **config)
if config['write_npy']:
np.save(outfile + '.npy', o)
if config['make_plots']:
self._plotter.make_sed_plots(o, **config)
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return o
def tsmap(self, prefix='', **kwargs):
"""Generate a spatial TS map for a source component with
properties defined by the `model` argument. The TS map will
have the same geometry as the ROI. The output of this method
is a dictionary containing `~fermipy.skymap.Map` objects with
the TS and amplitude of the best-fit test source. By default
this method will also save maps to FITS files and render them
as image files.
This method uses a simplified likelihood fitting
implementation that only fits for the normalization of the
test source. Before running this method it is recommended to
first optimize the ROI model (e.g. by running
:py:meth:`~fermipy.gtanalysis.GTAnalysis.optimize`).
Parameters
----------
prefix : str
Optional string that will be prepended to all output files.
{options}
Returns
-------
tsmap : dict
A dictionary containing the `~fermipy.skymap.Map` objects
for TS and source amplitude.
"""
timer = Timer.create(start=True)
schema = ConfigSchema(self.defaults['tsmap'])
schema.add_option('loglevel', logging.INFO)
schema.add_option('map_skydir', None, '', astropy.coordinates.SkyCoord)
schema.add_option('map_size', 1.0)
schema.add_option('threshold', 1E-2, '', float)
schema.add_option('use_pylike', True, '', bool)
schema.add_option('outfile', None, '', str)
config = schema.create_config(self.config['tsmap'], **kwargs)
# Defining default properties of test source model
config['model'].setdefault('Index', 2.0)
config['model'].setdefault('SpectrumType', 'PowerLaw')
config['model'].setdefault('SpatialModel', 'PointSource')
self.logger.log(config['loglevel'], 'Generating TS map')
o = self._make_tsmap_fast(prefix, **config)
if config['make_plots']:
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.make_tsmap_plots(o, self.roi)
self.logger.log(config['loglevel'], 'Finished TS map')
outfile = config.get('outfile', None)
if outfile is None:
outfile = utils.format_filename(self.workdir, 'tsmap',
prefix=[o['name']])
else:
outfile = os.path.join(self.workdir,
os.path.splitext(outfile)[0])
if config['write_fits']:
o['file'] = os.path.basename(outfile) + '.fits'
self._make_tsmap_fits(o, outfile + '.fits')
if config['write_npy']:
np.save(outfile + '.npy', o)
self.logger.log(config['loglevel'],
'Execution time: %.2f s', timer.elapsed_time)
return o
def find_sources(self, prefix='', **kwargs):
"""An iterative source-finding algorithm that uses likelihood
ratio (TS) maps of the region of interest to find new sources.
After each iteration a new TS map is generated incorporating
sources found in the previous iteration. The method stops
when the number of iterations exceeds ``max_iter`` or no
sources exceeding ``sqrt_ts_threshold`` are found.
Parameters
----------
{options}
tsmap : dict
Keyword arguments dictionary for tsmap method.
tscube : dict
Keyword arguments dictionary for tscube method.
Returns
-------
peaks : list
List of peak objects.
sources : list
List of source objects.
"""
timer = Timer.create(start=True)
self.logger.info('Starting.')
schema = ConfigSchema(self.defaults['sourcefind'],
tsmap=self.defaults['tsmap'],
tscube=self.defaults['tscube'])
schema.add_option('search_skydir', None, '', SkyCoord)
schema.add_option('search_minmax_radius', [None, 1.0], '', list)
config = utils.create_dict(self.config['sourcefind'],
tsmap=self.config['tsmap'],
tscube=self.config['tscube'])
config = schema.create_config(config, **kwargs)
# Defining default properties of test source model
config['model'].setdefault('Index', 2.0)
config['model'].setdefault('SpectrumType', 'PowerLaw')
config['model'].setdefault('SpatialModel', 'PointSource')
config['model'].setdefault('Prefactor', 1E-13)
o = {'sources': [], 'peaks': []}
for i in range(config['max_iter']):
srcs, peaks = self._find_sources_iterate(prefix, i, **config)
self.logger.info('Found %i sources in iteration %i.' %
(len(srcs), i))
o['sources'] += srcs
o['peaks'] += peaks
if len(srcs) == 0:
break
self.logger.info('Done.')
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return o
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 _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 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
