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 _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 _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