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 run_analysis(self, argv):
"""Run this analysis"""
args = self._parser.parse_args(argv)
if not HAVE_ST:
raise RuntimeError(
"Trying to run fermipy analysis, but don't have ST")
if args.load_baseline:
gta = GTAnalysis.create(args.roi_baseline, args.config)
else:
gta = GTAnalysis(args.config,
logging={'verbosity': 3},
fileio={'workdir_regex': '\.xml$|\.npy$'})
gta.setup()
if is_not_null(args.input_pars):
gta.load_parameters_from_yaml(args.input_pars)
gta.write_roi(args.roi_baseline,
save_model_map=True,
save_weight_map=True,
make_plots=args.make_plots)
src_list = get_src_names(gta)
plotter = plotting.AnalysisPlotter(gta.config['plotting'],
fileio=gta.config['fileio'],
logging=gta.config['logging'])
if is_null(args.fit_strategy):
return
fit_strategy = load_yaml(args.fit_strategy)
npred_current = None
npred_prev = None
plots_only = False
for fit_stage in fit_strategy:
mask = fit_stage.get('mask', None)
npred_threshold = fit_stage.get('npred_threshold', 1.0e4)
frac_threshold = fit_stage.get('frac_threshold', 0.5)
npred_frac = fit_stage.get('npred_frac', 0.9999)
if plots_only:
gta.load_roi("%s.npy" % fit_stage['key'])
npred_current = set_wts_get_npred_wt(gta, mask)
skip_list_region = get_unchanged(src_list,
npred_current,
npred_prev,
frac_threshold=frac_threshold)
else:
npred_current = set_wts_get_npred_wt(gta, mask)
skip_list_region = get_unchanged(src_list,
npred_current,
npred_prev,
frac_threshold=frac_threshold)
gta.optimize(npred_frac=npred_frac,
npred_threshold=npred_threshold,
skip=skip_list_region)
snapshot(gta,
plotter,
fit_stage['key'],
make_plots=args.make_plots)
npred_prev = npred_current
npred_current = build_srcdict(gta, 'npred_wt')
def tscube(self, prefix='', **kwargs):
"""Generate a spatial TS map for a source component with
properties defined by the `model` argument. This method uses
the `gttscube` ST application for source fitting and will
simultaneously fit the test source normalization as well as
the normalizations of any background components that are
currently free. 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.
Parameters
----------
prefix : str
Optional string that will be prepended to all output files
(FITS and rendered images).
model : dict
Dictionary defining the properties of the test source.
do_sed : bool
Compute the energy bin-by-bin fits.
nnorm : int
Number of points in the likelihood v. normalization scan.
norm_sigma : float
Number of sigma to use for the scan range.
tol : float
Critetia for fit convergence (estimated vertical distance
to min < tol ).
tol_type : int
Absoulte (0) or relative (1) criteria for convergence.
max_iter : int
Maximum number of iterations for the Newton's method fitter
remake_test_source : bool
If true, recomputes the test source image (otherwise just shifts it)
st_scan_level : int
make_plots : bool
Write image files.
write_fits : bool
Write a FITS file with the results of the analysis.
Returns
-------
maps : dict
A dictionary containing the `~fermipy.skymap.Map` objects
for TS and source amplitude.
"""
self.logger.info('Generating TS cube')
schema = ConfigSchema(self.defaults['tscube'])
schema.add_option('make_plots', True)
schema.add_option('write_fits', True)
schema.add_option('write_npy', True)
config = schema.create_config(self.config['tscube'], **kwargs)
maps = self._make_ts_cube(prefix, **config)
if config['make_plots']:
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.make_tsmap_plots(maps, self.roi, suffix='tscube')
self.logger.info("Finished TS cube")
return maps
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 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
(FITS and rendered images).
model : dict
Dictionary defining the properties of the test source.
exclude : list
Source or sources that will be removed from the model when
computing the TS map.
loge_bounds : list
Restrict the analysis to an energy range (emin,emax) in
log10(E/MeV) that is a subset of the analysis energy range.
By default the full analysis energy range will be used. If
either emin/emax are None then only an upper/lower bound on
the energy range wil be applied.
max_kernel_radius : float
Set the maximum radius of the test source kernel. Using a
smaller value will speed up the TS calculation at the loss of
accuracy. The default value is 3 degrees.
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.
Returns
-------
maps : dict
A dictionary containing the `~fermipy.skymap.Map` objects
for TS and source amplitude.
"""
self.logger.info('Generating TS map')
schema = ConfigSchema(self.defaults['tsmap'])
schema.add_option('make_plots', False)
schema.add_option('write_fits', True)
schema.add_option('write_npy', True)
schema.add_option('map_skydir', None, '', astropy.coordinates.SkyCoord)
schema.add_option('map_size', 1.0)
schema.add_option('exclude', None, '', list)
schema.add_option('threshold', 1E-2, '', float)
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')
config['model'].setdefault('Prefactor', 1E-13)
maps = 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(maps, self.roi)
self.logger.info('Finished TS map')
return maps
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.
model : dict
Dictionary defining the properties of the convolution kernel.
exclude : str or list of str
Source or sources that will be removed from the model when
computing the residual map.
loge_bounds : list
Restrict the analysis to an energy range (emin,emax) in
log10(E/MeV) that is a subset of the analysis energy range.
By default the full analysis energy range will be used. If
either emin/emax are None then only an upper/lower bound on
the energy range wil 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.
Returns
-------
maps : dict
A dictionary containing the `~fermipy.utils.Map` objects
for the residual significance and amplitude.
"""
self.logger.info('Generating residual maps')
config = copy.deepcopy(self.config['residmap'])
config = utils.merge_dict(config, kwargs, add_new_keys=True)
# 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)
make_plots = kwargs.get('make_plots', False)
maps = self._make_residual_map(prefix, config, **kwargs)
if make_plots:
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.make_residmap_plots(maps, self.roi)
self.logger.info('Finished residual maps')
return maps
