def method(self,**kwargs):
schema = config.ConfigSchema(self.defaults['secA'],
secB=self.defaults['secB'])
schema.add_option('extraOptA',3.0)
schema.add_option('extraOptB',False)
cfg = utils.create_dict(self.config['secA'],secB=self.config['secB'])
return schema.create_config(cfg, **kwargs)
def method(self, **kwargs):
schema = config.ConfigSchema(self.defaults['secA'],
secB=self.defaults['secB'])
schema.add_option('extraOptA', 3.0)
schema.add_option('extraOptB', False)
cfg = utils.create_dict(self.config['secA'], secB=self.config['secB'])
return schema.create_config(cfg, **kwargs)
def lightcurve(self, name, **kwargs):
"""Generate a lightcurve for the named source. The function will
complete the basic analysis steps for each bin and perform a
likelihood fit for each bin. Extracted values (along with
errors) are Integral Flux, spectral model, Spectral index, TS
value, pred. # of photons. Note: successful calculation of
TS:subscript:`var` requires at least one free background
parameter and a previously optimized ROI model.
Parameters
---------
name: str
source name
{options}
Returns
---------
LightCurve : dict
Dictionary containing output of the LC analysis
"""
name = self.roi.get_source_by_name(name).name
# Create schema for method configuration
schema = ConfigSchema(self.defaults['lightcurve'],
optimizer=self.defaults['optimizer'])
schema.add_option('prefix', '')
config = utils.create_dict(self.config['lightcurve'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Computing Lightcurve for %s' % name)
o = self._make_lc(name, **config)
filename = utils.format_filename(
self.workdir,
'lightcurve',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
o['file'] = None
if config['write_fits']:
o['file'] = os.path.basename(filename) + '.fits'
self._make_lc_fits(o, filename + '.fits', **config)
if config['write_npy']:
np.save(filename + '.npy', o)
self.logger.info('Finished Lightcurve')
return o
def lightcurve(self, name, **kwargs):
"""Generate a lightcurve for the named source. The function will
complete the basic analysis steps for each bin and perform a
likelihood fit for each bin. Extracted values (along with
errors) are Integral Flux, spectral model, Spectral index, TS
value, pred. # of photons. Note: successful calculation of
TS:subscript:`var` requires at least one free background
parameter and a previously optimized ROI model.
Parameters
---------
name: str
source name
{options}
Returns
---------
LightCurve : dict
Dictionary containing output of the LC analysis
"""
name = self.roi.get_source_by_name(name).name
# Create schema for method configuration
schema = ConfigSchema(self.defaults['lightcurve'],
optimizer=self.defaults['optimizer'])
schema.add_option('prefix', '')
config = utils.create_dict(self.config['lightcurve'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Computing Lightcurve for %s' % name)
o = self._make_lc(name, **config)
filename = utils.format_filename(self.workdir, 'lightcurve',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
o['file'] = None
if config['write_fits']:
o['file'] = os.path.basename(filename) + '.fits'
self._make_lc_fits(o, filename + '.fits', **config)
if config['write_npy']:
np.save(filename + '.npy', o)
self.logger.info('Finished Lightcurve')
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 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.
"""
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.')
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.
"""
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 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 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 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.
bin_index : float
Spectral index that will be use when fitting the energy
distribution within an energy bin.
use_local_index : bool
Use a power-law approximation to the shape of the global
spectrum in each bin. If this is false then a constant
index set to `bin_index` will be used.
fix_background : bool
Fix background components when fitting the flux
normalization in each energy bin. If fix_background=False
then all background parameters that are currently free in
the fit will be profiled. By default fix_background=True.
ul_confidence : float
Set the confidence level that will be used for the
calculation of flux upper limits in each energy bin.
cov_scale : float
Scaling factor that will be applied when setting the
gaussian prior on the normalization of free background
sources. If this parameter is None then no gaussian prior
will be applied.
make_plots : bool
Generate plots.
write_fits : bool
Write the output to a FITS file.
write_npy : bool
Write the output dictionary to a numpy file.
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
sed : dict
Dictionary containing output of the SED analysis.
"""
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('make_plots', False)
schema.add_option('write_fits', True)
schema.add_option('write_npy', True)
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)
filename = \
utils.format_filename(self.workdir, 'sed',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
o['file'] = None
if config['write_fits']:
o['file'] = os.path.basename(filename) + '.fits'
self._make_sed_fits(o, filename + '.fits', **config)
if config['write_npy']:
np.save(filename + '.npy', o)
if config['make_plots']:
self._plotter.make_sed_plots(o, **config)
self.logger.info('Finished SED')
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.
dtheta_max : float
Maximum offset in RA/DEC in deg from the nominal source
position that will be used to define the boundaries of the
TS map search region.
nstep : int
Number of steps in longitude/latitude that will be taken
when refining the source position. The bounds of the scan
range are set to the 99% positional uncertainty as
determined from the TS map peak fit. The total number of
sampling points will be nstep**2.
fix_background : bool
Fix background parameters when fitting the source position.
update : bool
Update the model for this source with the best-fit
position. If newname=None this will overwrite the
existing source map of this source with one corresponding
to its new location.
newname : str
Name that will be assigned to the relocalized source
when update=True. If newname is None then the existing
source name will be used.
make_plots : bool
Generate plots.
write_fits : bool
Write the output to a FITS file.
write_npy : bool
Write the output dictionary to a numpy file.
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
localize : dict
Dictionary containing results of the localization
analysis. This dictionary is also saved to the
dictionary of this source in 'localize'.
"""
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['localize'],
optimizer=self.defaults['optimizer'])
schema.add_option('make_plots', False)
schema.add_option('write_fits', True)
schema.add_option('write_npy', True)
schema.add_option('newname', name)
schema.add_option('prefix', '')
config = utils.create_dict(self.config['localize'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
nstep = config['nstep']
dtheta_max = config['dtheta_max']
update = config['update']
newname = config['newname']
prefix = config['prefix']
self.logger.info('Running localization for %s' % name)
saved_state = LikelihoodState(self.like)
if config['fix_background']:
self.free_sources(free=False, loglevel=logging.DEBUG)
src = self.roi.copy_source(name)
skydir = src.skydir
skywcs = self._skywcs
src_pix = skydir.to_pixel(skywcs)
tsmap_fit, tsmap = self._localize_tsmap(name,
prefix=prefix,
dtheta_max=dtheta_max)
self.logger.debug(
'Completed localization with TS Map.\n'
'(ra,dec) = (%10.4f,%10.4f)\n'
'(glon,glat) = (%10.4f,%10.4f)', tsmap_fit['ra'], tsmap_fit['dec'],
tsmap_fit['glon'], tsmap_fit['glat'])
# Fit baseline (point-source) model
self.free_norm(name)
fit_output = self._fit(loglevel=logging.DEBUG, **config['optimizer'])
# Save likelihood value for baseline fit
loglike0 = fit_output['loglike']
self.logger.debug('Baseline Model Likelihood: %f', loglike0)
self.zero_source(name)
o = {
'name': name,
'config': config,
'fit_success': True,
'loglike_base': loglike0,
'loglike_loc': np.nan,
'dloglike_loc': np.nan
}
cdelt0 = np.abs(skywcs.wcs.cdelt[0])
cdelt1 = np.abs(skywcs.wcs.cdelt[1])
scan_step = 2.0 * tsmap_fit['r95'] / (nstep - 1.0)
self.logger.debug(
'Refining localization search to '
'region of width: %.4f deg', tsmap_fit['r95'])
scan_map = Map.create(SkyCoord(tsmap_fit['ra'],
tsmap_fit['dec'],
unit='deg'),
scan_step, (nstep, nstep),
coordsys=wcs_utils.get_coordsys(skywcs))
scan_skydir = scan_map.get_pixel_skydirs()
lnlscan = dict(wcs=scan_map.wcs.to_header().items(),
loglike=np.zeros((nstep, nstep)),
dloglike=np.zeros((nstep, nstep)),
dloglike_fit=np.zeros((nstep, nstep)))
for i, t in enumerate(scan_skydir):
model_name = '%s_localize' % (name.replace(' ', '').lower())
src.set_name(model_name)
src.set_position(t)
self.add_source(model_name,
src,
free=True,
init_source=False,
save_source_maps=False,
loglevel=logging.DEBUG)
fit_output = self._fit(loglevel=logging.DEBUG,
**config['optimizer'])
loglike1 = fit_output['loglike']
lnlscan['loglike'].flat[i] = loglike1
self.delete_source(model_name, loglevel=logging.DEBUG)
lnlscan['dloglike'] = lnlscan['loglike'] - np.max(lnlscan['loglike'])
scan_tsmap = Map(2.0 * lnlscan['dloglike'].T, scan_map.wcs)
self.unzero_source(name)
saved_state.restore()
self._sync_params(name)
self._update_roi()
scan_fit, new_skydir = fit_error_ellipse(scan_tsmap, dpix=3)
o.update(scan_fit)
o['loglike_loc'] = np.max(
lnlscan['loglike']) + 0.5 * scan_fit['offset']
o['dloglike_loc'] = o['loglike_loc'] - o['loglike_base']
# lnlscan['dloglike_fit'] = \
# utils.parabola(np.linspace(0,nstep-1.0,nstep)[:,np.newaxis],
# np.linspace(0,nstep-1.0,nstep)[np.newaxis,:],
# *scan_fit['popt']).reshape((nstep,nstep))
o['lnlscan'] = lnlscan
# Best fit position and uncertainty from fit to TS map
o['tsmap_fit'] = tsmap_fit
# Best fit position and uncertainty from pylike scan
o['scan_fit'] = scan_fit
pix = new_skydir.to_pixel(skywcs)
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['offset'] = skydir.separation(new_skydir).deg
if o['offset'] > dtheta_max:
o['fit_success'] = False
if not o['fit_success']:
self.logger.error(
'Localization failed.\n'
'(ra,dec) = (%10.4f,%10.4f)\n'
'(glon,glat) = (%10.4f,%10.4f)\n'
'offset = %8.4f deltax = %8.4f '
'deltay = %8.4f', o['ra'], o['dec'], o['glon'], o['glat'],
o['offset'], o['deltax'], o['deltay'])
else:
self.logger.info(
'Localization succeeded with '
'coordinates:\n'
'(ra,dec) = (%10.4f,%10.4f)\n'
'(glon,glat) = (%10.4f,%10.4f)\n'
'offset = %8.4f r68 = %8.4f', o['ra'], o['dec'], o['glon'],
o['glat'], o['offset'], o['r68'])
self.roi[name]['localize'] = copy.deepcopy(o)
if config['make_plots']:
self._plotter.make_localization_plots(o,
tsmap,
self.roi,
prefix=prefix,
skydir=scan_skydir)
if update and o['fit_success']:
self.logger.info('Updating source %s '
'to localized position.', name)
src = self.delete_source(name)
src.set_position(new_skydir)
src.set_name(newname, names=src.names)
self.add_source(newname, 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(newname)
self.roi[newname]['localize'] = copy.deepcopy(o)
self.logger.info('LogLike: %12.3f DeltaLogLike: %12.3f',
o['loglike_loc'], o['dloglike_loc'])
if o['fit_success']:
src = self.roi.get_source_by_name(newname)
src['pos_sigma'] = o['sigma']
src['pos_sigma_semimajor'] = o['sigma_semimajor']
src['pos_sigma_semiminor'] = o['sigma_semiminor']
src['pos_r68'] = o['r68']
src['pos_r95'] = o['r95']
src['pos_r99'] = o['r99']
src['pos_angle'] = np.degrees(o['theta'])
self.logger.info('Finished localization.')
return o
def find_sources(self, prefix='', **kwargs):
"""An iterative source-finding algorithm.
Parameters
----------
model : dict
Dictionary defining the properties of the test source.
This is the model that will be used for generating TS maps.
sqrt_ts_threshold : float
Source threshold in sqrt(TS). Only peaks with sqrt(TS)
exceeding this threshold will be used as seeds for new
sources.
min_separation : float
Minimum separation in degrees of sources detected in each
iteration. The source finder will look for the maximum peak
in the TS map within a circular region of this radius.
max_iter : int
Maximum number of source finding iterations. The source
finder will continue adding sources until no additional
peaks are found or the number of iterations exceeds this
number.
sources_per_iter : int
Maximum number of sources that will be added in each
iteration. If the number of detected peaks in a given
iteration is larger than this number, only the N peaks with
the largest TS will be used as seeds for the current
iteration.
tsmap_fitter : str
Set the method used internally for generating TS maps.
Valid options:
* tsmap
* tscube
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.
"""
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.')
return o
