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 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 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 _make_ts_cube(self, prefix, **kwargs):
skywcs = kwargs.get('wcs', self.geom.wcs)
npix = kwargs.get('npix', self.npix)
galactic = wcs_utils.is_galactic(skywcs)
ref_skydir = wcs_utils.wcs_to_skydir(skywcs)
refdir = pyLike.SkyDir(ref_skydir.ra.deg, ref_skydir.dec.deg)
pixsize = np.abs(skywcs.wcs.cdelt[0])
skyproj = pyLike.FitScanner.buildSkyProj(str("AIT"), refdir, pixsize,
npix, galactic)
src_dict = copy.deepcopy(kwargs.setdefault('model', {}))
src_dict = {} if src_dict is None else src_dict
xpix, ypix = (np.round(
(self.npix - 1.0) / 2.), np.round((self.npix - 1.0) / 2.))
skydir = wcs_utils.pix_to_skydir(xpix, ypix, skywcs)
src_dict['ra'] = skydir.ra.deg
src_dict['dec'] = skydir.dec.deg
src_dict.setdefault('SpatialModel', 'PointSource')
src_dict.setdefault('SpatialWidth', 0.3)
src_dict.setdefault('Index', 2.0)
src_dict.setdefault('Prefactor', 1E-13)
src_dict['name'] = 'tscube_testsource'
src = Source.create_from_dict(src_dict)
modelname = utils.create_model_name(src)
optFactory = pyLike.OptimizerFactory_instance()
optObject = optFactory.create(str("MINUIT"),
self.components[0].like.logLike)
pylike_src = self.components[0]._create_source(src)
fitScanner = pyLike.FitScanner(self.like.composite, optObject, skyproj,
npix, npix)
pylike_src.spectrum().normPar().setBounds(0, 1E6)
fitScanner.setTestSource(pylike_src)
self.logger.info("Running tscube")
outfile = utils.format_filename(self.config['fileio']['workdir'],
'tscube.fits',
prefix=[prefix])
try:
fitScanner.run_tscube(True, kwargs['do_sed'], kwargs['nnorm'],
kwargs['norm_sigma'], kwargs['cov_scale_bb'],
kwargs['cov_scale'], kwargs['tol'],
kwargs['max_iter'], kwargs['tol_type'],
kwargs['remake_test_source'],
kwargs['st_scan_level'], str(''),
kwargs['init_lambda'])
except Exception:
fitScanner.run_tscube(True, kwargs['do_sed'], kwargs['nnorm'],
kwargs['norm_sigma'], kwargs['cov_scale_bb'],
kwargs['cov_scale'], kwargs['tol'],
kwargs['max_iter'], kwargs['tol_type'],
kwargs['remake_test_source'],
kwargs['st_scan_level'])
self.logger.info("Writing FITS output")
fitScanner.writeFitsFile(str(outfile), str("gttscube"))
convert_tscube(str(outfile), str(outfile))
tscube = castro.TSCube.create_from_fits(outfile)
ts_map = tscube.tsmap
norm_map = tscube.normmap
npred_map = copy.deepcopy(norm_map)
npred_map.data *= tscube.refSpec.ref_npred.sum()
amp_map = copy.deepcopy(norm_map)
amp_map.data *= src_dict['Prefactor']
sqrt_ts_map = copy.deepcopy(ts_map)
sqrt_ts_map.data[...] = np.abs(sqrt_ts_map.data)**0.5
o = {
'name': utils.join_strings([prefix, modelname]),
'src_dict': copy.deepcopy(src_dict),
'file': os.path.basename(outfile),
'ts': ts_map,
'sqrt_ts': sqrt_ts_map,
'npred': npred_map,
'amplitude': amp_map,
'config': kwargs,
'tscube': tscube
}
if not kwargs['write_fits']:
os.remove(outfile)
os['file'] = None
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 _make_ts_cube(self, prefix, **kwargs):
map_skydir = kwargs.get('map_skydir')
map_size = kwargs.get('map_size')
exclude = kwargs.get('exclude', [])
# We take the coordinate system and the bin size from the underlying map
skywcs = self._geom.wcs
galactic = wcs_utils.is_galactic(skywcs)
pixsize = max(np.abs(skywcs.wcs.cdelt))
# If the map_size is not specified we need to find the right number of pixels
if map_size is None:
npix = max(self._geom.npix)[0]
map_size = pixsize * npix
else:
npix = int(np.round(map_size / pixsize))
saved_state = LikelihoodState(self.like)
for ex_src in exclude:
self.zero_source(ex_src)
if map_skydir is None:
# Take the center of the wcs
map_geom = self._geom.to_image()
frame = coordsys_to_frame(map_geom.coordsys)
map_skydir = SkyCoord(*map_geom.pix_to_coord(
self._geom.wcs.wcs.crpix),
frame=frame,
unit='deg')
map_skydir = map_skydir.transform_to('icrs')
refdir = pyLike.SkyDir(map_skydir.ra.deg, map_skydir.dec.deg)
src_dict = copy.deepcopy(kwargs.setdefault('model', {}))
src_dict = {} if src_dict is None else src_dict
src_dict['ra'] = map_skydir.ra.deg
src_dict['dec'] = map_skydir.dec.deg
src_dict.setdefault('SpatialModel', 'PointSource')
src_dict.setdefault('SpatialWidth', 0.3)
src_dict.setdefault('Index', 2.0)
src_dict.setdefault('Prefactor', 1.0)
src_dict['name'] = 'tscube_testsource'
src = Source.create_from_dict(src_dict)
if 'Prefactor' in src.spectral_pars:
src.spectral_pars['Prefactor']['scale'] = 1.0e-10
modelname = utils.create_model_name(src)
pylike_src = self.components[0]._create_source(src)
pylike_src.spectrum().normPar().setBounds(0, 1E6)
skyproj = pyLike.FitScanner.buildSkyProj(str("AIT"), refdir, pixsize,
npix, galactic)
optFactory = pyLike.OptimizerFactory_instance()
optObject = optFactory.create(str("MINUIT"), self.like.composite)
fitScanner = pyLike.FitScanner(self.like.composite, optObject, skyproj,
npix, npix)
fitScanner.set_quiet(True)
fitScanner.setTestSource(pylike_src)
self.logger.info("Running tscube")
outfile = utils.format_filename(self.config['fileio']['workdir'],
'tscube.fits',
prefix=[prefix])
fitScanner.run_tscube(True, kwargs['do_sed'], kwargs['nnorm'],
kwargs['norm_sigma'], kwargs['cov_scale_bb'],
kwargs['cov_scale'], kwargs['tol'],
kwargs['max_iter'], kwargs['tol_type'],
kwargs['remake_test_source'],
kwargs['st_scan_level'], str(''),
kwargs['init_lambda'])
self.logger.info("Writing FITS output")
fitScanner.writeFitsFile(str(outfile), str("gttscube"), "", False,
pyLike.FitScanner.TSMAP_ONLY)
saved_state.restore()
convert_tscube(str(outfile), str(outfile))
tscube = castro.TSCube.create_from_fits(outfile)
ts_map = tscube.tsmap
norm_map = tscube.normmap
npred_map = copy.deepcopy(norm_map)
npred_map.data *= tscube.refSpec.ref_npred.sum()
amp_map = copy.deepcopy(norm_map)
amp_map.data *= pylike_src.spectrum().normPar().getValue()
sqrt_ts_map = copy.deepcopy(ts_map)
sqrt_ts_map.data[...] = np.abs(sqrt_ts_map.data)**0.5
o = {
'name': utils.join_strings([prefix, modelname]),
'src_dict': copy.deepcopy(src_dict),
'file': os.path.basename(outfile),
'ts': ts_map,
'sqrt_ts': sqrt_ts_map,
'npred': npred_map,
'amplitude': amp_map,
'config': kwargs,
'tscube': tscube
}
if not kwargs['write_fits']:
os.remove(outfile)
o['file'] = None
self.logger.info("Done")
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.
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 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 _make_ts_cube(self, prefix, **kwargs):
skywcs = kwargs.get('wcs', self.geom.wcs)
npix = kwargs.get('npix', self.npix)
galactic = wcs_utils.is_galactic(skywcs)
ref_skydir = wcs_utils.wcs_to_skydir(skywcs)
refdir = pyLike.SkyDir(ref_skydir.ra.deg,
ref_skydir.dec.deg)
pixsize = np.abs(skywcs.wcs.cdelt[0])
skyproj = pyLike.FitScanner.buildSkyProj(str("AIT"),
refdir, pixsize, npix,
galactic)
src_dict = copy.deepcopy(kwargs.setdefault('model', {}))
src_dict = {} if src_dict is None else src_dict
xpix, ypix = (np.round((self.npix - 1.0) / 2.),
np.round((self.npix - 1.0) / 2.))
skydir = wcs_utils.pix_to_skydir(xpix, ypix, skywcs)
src_dict['ra'] = skydir.ra.deg
src_dict['dec'] = skydir.dec.deg
src_dict.setdefault('SpatialModel', 'PointSource')
src_dict.setdefault('SpatialWidth', 0.3)
src_dict.setdefault('Index', 2.0)
src_dict.setdefault('Prefactor', 1E-13)
src_dict['name'] = 'tscube_testsource'
src = Source.create_from_dict(src_dict)
modelname = utils.create_model_name(src)
optFactory = pyLike.OptimizerFactory_instance()
optObject = optFactory.create(str("MINUIT"),
self.components[0].like.logLike)
pylike_src = self.components[0]._create_source(src)
fitScanner = pyLike.FitScanner(self.like.composite, optObject, skyproj,
npix, npix)
pylike_src.spectrum().normPar().setBounds(0, 1E6)
fitScanner.setTestSource(pylike_src)
self.logger.info("Running tscube")
outfile = utils.format_filename(self.config['fileio']['workdir'],
'tscube.fits',
prefix=[prefix])
try:
fitScanner.run_tscube(True,
kwargs['do_sed'], kwargs['nnorm'],
kwargs['norm_sigma'],
kwargs['cov_scale_bb'], kwargs['cov_scale'],
kwargs['tol'], kwargs['max_iter'],
kwargs['tol_type'],
kwargs['remake_test_source'],
kwargs['st_scan_level'],
str(''),
kwargs['init_lambda'])
except Exception:
fitScanner.run_tscube(True,
kwargs['do_sed'], kwargs['nnorm'],
kwargs['norm_sigma'],
kwargs['cov_scale_bb'], kwargs['cov_scale'],
kwargs['tol'], kwargs['max_iter'],
kwargs['tol_type'],
kwargs['remake_test_source'],
kwargs['st_scan_level'])
self.logger.info("Writing FITS output")
fitScanner.writeFitsFile(str(outfile), str("gttscube"))
convert_tscube(str(outfile), str(outfile))
tscube = castro.TSCube.create_from_fits(outfile)
ts_map = tscube.tsmap
norm_map = tscube.normmap
npred_map = copy.deepcopy(norm_map)
npred_map.data *= tscube.refSpec.ref_npred.sum()
amp_map = copy.deepcopy(norm_map)
amp_map.data *= src_dict['Prefactor']
sqrt_ts_map = copy.deepcopy(ts_map)
sqrt_ts_map.data[...] = np.abs(sqrt_ts_map.data)**0.5
o = {'name': utils.join_strings([prefix, modelname]),
'src_dict': copy.deepcopy(src_dict),
'file': os.path.basename(outfile),
'ts': ts_map,
'sqrt_ts': sqrt_ts_map,
'npred': npred_map,
'amplitude': amp_map,
'config': kwargs,
'tscube': tscube
}
if not kwargs['write_fits']:
os.remove(outfile)
os['file'] = None
self.logger.info("Done")
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 _make_tsmap_fast(self, prefix, **kwargs):
"""
Make a TS map from a GTAnalysis instance. This is a
simplified implementation optimized for speed that only fits
for the source normalization (all background components are
kept fixed). The spectral/spatial characteristics of the test
source can be defined with the src_dict argument. By default
this method will generate a TS map for a point source with an
index=2.0 power-law spectrum.
Parameters
----------
model : dict or `~fermipy.roi_model.Source`
Dictionary or Source object defining the properties of the
test source that will be used in the scan.
"""
src_dict = copy.deepcopy(kwargs.setdefault('model', {}))
src_dict = {} if src_dict is None else src_dict
multithread = kwargs.setdefault('multithread', False)
threshold = kwargs.setdefault('threshold', 1E-2)
max_kernel_radius = kwargs.get('max_kernel_radius')
loge_bounds = kwargs.setdefault('loge_bounds', None)
if loge_bounds is not None:
if len(loge_bounds) == 0:
loge_bounds = [None, None]
elif len(loge_bounds) == 1:
loge_bounds += [None]
loge_bounds[0] = (loge_bounds[0] if loge_bounds[0] is not None
else self.log_energies[0])
loge_bounds[1] = (loge_bounds[1] if loge_bounds[1] is not None
else self.log_energies[-1])
else:
loge_bounds = [self.log_energies[0], self.log_energies[-1]]
# Put the test source at the pixel closest to the ROI center
xpix, ypix = (np.round((self.npix - 1.0) / 2.),
np.round((self.npix - 1.0) / 2.))
cpix = np.array([xpix, ypix])
skywcs = self._skywcs
skydir = wcs_utils.pix_to_skydir(cpix[0], cpix[1], skywcs)
src_dict['ra'] = skydir.ra.deg
src_dict['dec'] = skydir.dec.deg
src_dict.setdefault('SpatialModel', 'PointSource')
src_dict.setdefault('SpatialWidth', 0.3)
src_dict.setdefault('Index', 2.0)
src_dict.setdefault('Prefactor', 1E-13)
counts = []
bkg = []
model = []
c0_map = []
eslices = []
enumbins = []
model_npred = 0
for c in self.components:
imin = utils.val_to_edge(c.log_energies, loge_bounds[0])[0]
imax = utils.val_to_edge(c.log_energies, loge_bounds[1])[0]
eslice = slice(imin, imax)
bm = c.model_counts_map(exclude=kwargs['exclude']).counts.astype('float')[
eslice, ...]
cm = c.counts_map().counts.astype('float')[eslice, ...]
bkg += [bm]
counts += [cm]
c0_map += [cash(cm, bm)]
eslices += [eslice]
enumbins += [cm.shape[0]]
self.add_source('tsmap_testsource', src_dict, free=True,
init_source=False)
src = self.roi['tsmap_testsource']
# self.logger.info(str(src_dict))
modelname = utils.create_model_name(src)
for c, eslice in zip(self.components, eslices):
mm = c.model_counts_map('tsmap_testsource').counts.astype('float')[
eslice, ...]
model_npred += np.sum(mm)
model += [mm]
self.delete_source('tsmap_testsource')
for i, mm in enumerate(model):
dpix = 3
for j in range(mm.shape[0]):
ix, iy = np.unravel_index(
np.argmax(mm[j, ...]), mm[j, ...].shape)
mx = mm[j, ix, :] > mm[j, ix, iy] * threshold
my = mm[j, :, iy] > mm[j, ix, iy] * threshold
dpix = max(dpix, np.round(np.sum(mx) / 2.))
dpix = max(dpix, np.round(np.sum(my) / 2.))
if max_kernel_radius is not None and \
dpix > int(max_kernel_radius / self.components[i].binsz):
dpix = int(max_kernel_radius / self.components[i].binsz)
xslice = slice(max(int(xpix - dpix), 0),
min(int(xpix + dpix + 1), self.npix))
model[i] = model[i][:, xslice, xslice]
ts_values = np.zeros((self.npix, self.npix))
amp_values = np.zeros((self.npix, self.npix))
wrap = functools.partial(_ts_value_newton, counts=counts,
bkg=bkg, model=model,
C_0_map=c0_map)
if kwargs['map_skydir'] is not None:
map_offset = wcs_utils.skydir_to_pix(kwargs['map_skydir'],
self._skywcs)
map_delta = 0.5 * kwargs['map_size'] / self.components[0].binsz
xmin = max(int(np.ceil(map_offset[1] - map_delta)), 0)
xmax = min(int(np.floor(map_offset[1] + map_delta)) + 1, self.npix)
ymin = max(int(np.ceil(map_offset[0] - map_delta)), 0)
ymax = min(int(np.floor(map_offset[0] + map_delta)) + 1, self.npix)
xslice = slice(xmin, xmax)
yslice = slice(ymin, ymax)
xyrange = [range(xmin, xmax), range(ymin, ymax)]
map_wcs = skywcs.deepcopy()
map_wcs.wcs.crpix[0] -= ymin
map_wcs.wcs.crpix[1] -= xmin
else:
xyrange = [range(self.npix), range(self.npix)]
map_wcs = skywcs
xslice = slice(0, self.npix)
yslice = slice(0, self.npix)
positions = []
for i, j in itertools.product(xyrange[0], xyrange[1]):
p = [[k // 2, i, j] for k in enumbins]
positions += [p]
if multithread:
pool = Pool()
results = pool.map(wrap, positions)
pool.close()
pool.join()
else:
results = map(wrap, positions)
for i, r in enumerate(results):
ix = positions[i][0][1]
iy = positions[i][0][2]
ts_values[ix, iy] = r[0]
amp_values[ix, iy] = r[1]
ts_values = ts_values[xslice, yslice]
amp_values = amp_values[xslice, yslice]
ts_map = Map(ts_values, map_wcs)
sqrt_ts_map = Map(ts_values**0.5, map_wcs)
npred_map = Map(amp_values * model_npred, map_wcs)
amp_map = Map(amp_values * src.get_norm(), map_wcs)
o = {'name': utils.join_strings([prefix, modelname]),
'src_dict': copy.deepcopy(src_dict),
'file': None,
'ts': ts_map,
'sqrt_ts': sqrt_ts_map,
'npred': npred_map,
'amplitude': amp_map,
'config': kwargs
}
fits_file = utils.format_filename(self.config['fileio']['workdir'],
'tsmap.fits',
prefix=[prefix, modelname])
if kwargs['write_fits']:
fits_utils.write_maps(ts_map,
{'SQRT_TS_MAP': sqrt_ts_map,
'NPRED_MAP': npred_map,
'N_MAP': amp_map},
fits_file)
o['file'] = os.path.basename(fits_file)
if kwargs['write_npy']:
np.save(os.path.splitext(fits_file)[0] + '.npy', o)
return o
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 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 _make_residual_map(self, prefix, config, **kwargs):
write_fits = kwargs.get('write_fits', True)
write_npy = kwargs.get('write_npy', True)
src_dict = copy.deepcopy(config.setdefault('model', {}))
exclude = config.setdefault('exclude', None)
loge_bounds = config.setdefault('loge_bounds', None)
if loge_bounds is not None:
if len(loge_bounds) == 0:
loge_bounds = [None, None]
elif len(loge_bounds) == 1:
loge_bounds += [None]
loge_bounds[0] = (loge_bounds[0] if loge_bounds[0] is not None else
self.energies[0])
loge_bounds[1] = (loge_bounds[1] if loge_bounds[1] is not None else
self.energies[-1])
else:
loge_bounds = [self.energies[0], self.energies[-1]]
# Put the test source at the pixel closest to the ROI center
xpix, ypix = (np.round(
(self.npix - 1.0) / 2.), np.round((self.npix - 1.0) / 2.))
cpix = np.array([xpix, ypix])
skywcs = self._skywcs
skydir = wcs_utils.pix_to_skydir(cpix[0], cpix[1], skywcs)
if src_dict is None:
src_dict = {}
src_dict['ra'] = skydir.ra.deg
src_dict['dec'] = skydir.dec.deg
src_dict.setdefault('SpatialModel', 'PointSource')
src_dict.setdefault('SpatialWidth', 0.3)
src_dict.setdefault('Index', 2.0)
kernel = None
if src_dict['SpatialModel'] == 'Gaussian':
kernel = utils.make_gaussian_kernel(src_dict['SpatialWidth'],
cdelt=self.components[0].binsz,
npix=101)
kernel /= np.sum(kernel)
cpix = [50, 50]
self.add_source('residmap_testsource',
src_dict,
free=True,
init_source=False,
save_source_maps=False)
src = self.roi.get_source_by_name('residmap_testsource')
modelname = utils.create_model_name(src)
npix = self.components[0].npix
mmst = np.zeros((npix, npix))
cmst = np.zeros((npix, npix))
emst = np.zeros((npix, npix))
sm = get_source_kernel(self, 'residmap_testsource', kernel)
ts = np.zeros((npix, npix))
sigma = np.zeros((npix, npix))
excess = np.zeros((npix, npix))
self.delete_source('residmap_testsource')
for i, c in enumerate(self.components):
imin = utils.val_to_edge(c.energies, loge_bounds[0])[0]
imax = utils.val_to_edge(c.energies, loge_bounds[1])[0]
mc = c.model_counts_map(exclude=exclude).counts.astype('float')
cc = c.counts_map().counts.astype('float')
ec = np.ones(mc.shape)
ccs = convolve_map(cc, sm[i], cpix, imin=imin, imax=imax)
mcs = convolve_map(mc, sm[i], cpix, imin=imin, imax=imax)
ecs = convolve_map(ec, sm[i], cpix, imin=imin, imax=imax)
cms = np.sum(ccs, axis=0)
mms = np.sum(mcs, axis=0)
ems = np.sum(ecs, axis=0)
cmst += cms
mmst += mms
emst += ems
# cts = 2.0 * (poisson_lnl(cms, cms) - poisson_lnl(cms, mms))
excess += cms - mms
ts = 2.0 * (poisson_lnl(cmst, cmst) - poisson_lnl(cmst, mmst))
sigma = np.sqrt(ts)
sigma[excess < 0] *= -1
emst /= np.max(emst)
sigma_map = Map(sigma, skywcs)
model_map = Map(mmst / emst, skywcs)
data_map = Map(cmst / emst, skywcs)
excess_map = Map(excess / emst, skywcs)
o = {
'name': utils.join_strings([prefix, modelname]),
'file': None,
'sigma': sigma_map,
'model': model_map,
'data': data_map,
'excess': excess_map,
'config': config
}
fits_file = utils.format_filename(self.config['fileio']['workdir'],
'residmap.fits',
prefix=[prefix, modelname])
if write_fits:
fits_utils.write_maps(
sigma_map, {
'DATA_MAP': data_map,
'MODEL_MAP': model_map,
'EXCESS_MAP': excess_map
}, fits_file)
o['file'] = os.path.basename(fits_file)
if write_npy:
np.save(os.path.splitext(fits_file)[0] + '.npy', o)
return o
