def run_region(self, kr, lon, lat, radius): # TODO: for now we have to read/create the allsky maps each in each job # because we can't pickle <functools._lru_cache_wrapper object # send this back to init when fixed # exposure exposure_hpx = Map.read( "$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz" ) exposure_hpx.unit = "cm2 s" # iem iem_filepath = BASE_PATH / "data" / "gll_iem_v06_extrapolated.fits" iem_fermi_extra = Map.read(iem_filepath) # norm=1.1, tilt=0.03 see paper appendix A model_iem = SkyDiffuseCube( iem_fermi_extra, norm=1.1, tilt=0.03, name="iem_extrapolated" ) # ROI roi_time = time() ROI_pos = SkyCoord(lon, lat, frame="galactic", unit="deg") width = 2 * (radius + self.psf_margin) # Counts counts = Map.create( skydir=ROI_pos, width=width, proj="CAR", frame="galactic", binsz=1 / 8.0, axes=[self.energy_axis], dtype=float, ) counts.fill_by_coord( {"skycoord": self.events.radec, "energy": self.events.energy} ) axis = MapAxis.from_nodes( counts.geom.axes[0].center, name="energy_true", unit="GeV", interp="log" ) wcs = counts.geom.wcs geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis]) coords = geom.get_coord() # expo data = exposure_hpx.interp_by_coord(coords) exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit, dtype=float) # read PSF psf_kernel = PSFKernel.from_table_psf( self.psf, geom, max_radius=self.psf_margin * u.deg ) # Energy Dispersion e_true = exposure.geom.axes[0].edges e_reco = counts.geom.axes[0].edges edisp = EDispKernel.from_diagonal_response(e_true=e_true, e_reco=e_reco) # fit mask if coords["lon"].min() < 90 * u.deg and coords["lon"].max() > 270 * u.deg: coords["lon"][coords["lon"].value > 180] -= 360 * u.deg mask = ( (coords["lon"] >= coords["lon"].min() + self.psf_margin * u.deg) & (coords["lon"] <= coords["lon"].max() - self.psf_margin * u.deg) & (coords["lat"] >= coords["lat"].min() + self.psf_margin * u.deg) & (coords["lat"] <= coords["lat"].max() - self.psf_margin * u.deg) ) mask_fermi = WcsNDMap(counts.geom, mask) # IEM eval_iem = MapEvaluator( model=model_iem, exposure=exposure, psf=psf_kernel, edisp=edisp ) bkg_iem = eval_iem.compute_npred() # ISO eval_iso = MapEvaluator(model=self.model_iso, exposure=exposure, edisp=edisp) bkg_iso = eval_iso.compute_npred() # merge iem and iso, only one local normalization is fitted dataset_name = "3FHL_ROI_num" + str(kr) background_total = bkg_iem + bkg_iso background_model = BackgroundModel( background_total, name="bkg_iem+iso", datasets_names=[dataset_name] ) background_model.parameters["norm"].min = 0.0 # Sources model in_roi = self.FHL3.positions.galactic.contained_by(wcs) FHL3_roi = [] for ks in range(len(self.FHL3.table)): if in_roi[ks] == True: model = self.FHL3[ks].sky_model() model.spatial_model.parameters.freeze_all() # freeze spatial model.spectral_model.parameters["amplitude"].min = 0.0 if isinstance(model.spectral_model, PowerLawSpectralModel): model.spectral_model.parameters["index"].min = 0.1 model.spectral_model.parameters["index"].max = 10.0 else: model.spectral_model.parameters["alpha"].min = 0.1 model.spectral_model.parameters["alpha"].max = 10.0 FHL3_roi.append(model) model_total = Models([background_model] + FHL3_roi) # Dataset dataset = MapDataset( models=model_total, counts=counts, exposure=exposure, psf=psf_kernel, edisp=edisp, mask_fit=mask_fermi, name=dataset_name, ) cat_stat = dataset.stat_sum() datasets = Datasets([dataset]) fit = Fit(datasets) results = fit.run(**self.optimize_opts) print("ROI_num", str(kr), "\n", results) fit_stat = datasets.stat_sum() if results.message != "Optimization failed.": datasets.write(path=Path(self.resdir), prefix=dataset.name, overwrite=True) np.savez( self.resdir / f"3FHL_ROI_num{kr}_fit_infos.npz", message=results.message, stat=[cat_stat, fit_stat], ) exec_time = time() - roi_time print("ROI", kr, " time (s): ", exec_time) for model in FHL3_roi: if ( self.FHL3[model.name].data["ROI_num"] == kr and self.FHL3[model.name].data["Signif_Avg"] >= self.sig_cut ): flux_points = FluxPointsEstimator( e_edges=self.El_flux, source=model.name, n_sigma_ul=2, ).run(datasets=datasets) filename = self.resdir / f"{model.name}_flux_points.fits" flux_points.write(filename, overwrite=True) exec_time = time() - roi_time - exec_time print("ROI", kr, " Flux points time (s): ", exec_time)
def run_region(self, kr, lon, lat, radius): # TODO: for now we have to read/create the allsky maps each in each job # because we can't pickle <functools._lru_cache_wrapper object # send this back to init when fixed log.info(f"ROI {kr}: loading data") # exposure exposure_hpx = Map.read( "$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz") exposure_hpx.unit = "cm2 s" # psf psf_map = PSFMap.read( "$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz", format="gtpsf") # reduce size of the PSF axis = psf_map.psf_map.geom.axes["rad"].center.to_value(u.deg) indmax = np.argmin(np.abs(self.psf_margin - axis)) psf_map = psf_map.slice_by_idx(slices={"rad": slice(0, indmax)}) # iem iem_filepath = BASE_PATH / "data" / "gll_iem_v06_extrapolated.fits" iem_fermi_extra = Map.read(iem_filepath) # norm=1.1, tilt=0.03 see paper appendix A model_iem = SkyModel( PowerLawNormSpectralModel(norm=1.1, tilt=0.03), TemplateSpatialModel(iem_fermi_extra, normalize=False), name="iem_extrapolated", ) # ROI roi_time = time() ROI_pos = SkyCoord(lon, lat, frame="galactic", unit="deg") width = 2 * (radius + self.psf_margin) # Counts counts = Map.create( skydir=ROI_pos, width=width, proj="CAR", frame="galactic", binsz=1 / 8.0, axes=[self.energy_axis], dtype=float, ) counts.fill_by_coord({ "skycoord": self.events.radec, "energy": self.events.energy }) axis = MapAxis.from_nodes(counts.geom.axes[0].center, name="energy_true", unit="GeV", interp="log") wcs = counts.geom.wcs geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis]) coords = geom.get_coord() # expo data = exposure_hpx.interp_by_coord(coords) exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit, dtype=float) # Energy Dispersion edisp = EDispKernelMap.from_diagonal_response( energy_axis_true=axis, energy_axis=self.energy_axis) # fit mask if coords["lon"].min() < 90 * u.deg and coords["lon"].max( ) > 270 * u.deg: coords["lon"][coords["lon"].value > 180] -= 360 * u.deg mask = ( (coords["lon"] >= coords["lon"].min() + self.psf_margin * u.deg) & (coords["lon"] <= coords["lon"].max() - self.psf_margin * u.deg) & (coords["lat"] >= coords["lat"].min() + self.psf_margin * u.deg) & (coords["lat"] <= coords["lat"].max() - self.psf_margin * u.deg)) mask_fermi = WcsNDMap(counts.geom, mask) mask_safe_fermi = WcsNDMap(counts.geom, np.ones(mask.shape, dtype=bool)) log.info(f"ROI {kr}: pre-computing diffuse") # IEM eval_iem = MapEvaluator( model=model_iem, exposure=exposure, psf=psf_map.get_psf_kernel(geom), edisp=edisp.get_edisp_kernel(), ) bkg_iem = eval_iem.compute_npred() # ISO eval_iso = MapEvaluator(model=self.model_iso, exposure=exposure, edisp=edisp.get_edisp_kernel()) bkg_iso = eval_iso.compute_npred() # merge iem and iso, only one local normalization is fitted dataset_name = "3FHL_ROI_num" + str(kr) background_total = bkg_iem + bkg_iso # Dataset dataset = MapDataset( counts=counts, exposure=exposure, background=background_total, psf=psf_map, edisp=edisp, mask_fit=mask_fermi, mask_safe=mask_safe_fermi, name=dataset_name, ) background_model = FoVBackgroundModel(dataset_name=dataset_name) background_model.parameters["norm"].min = 0.0 # Sources model in_roi = self.FHL3.positions.galactic.contained_by(wcs) FHL3_roi = [] for ks in range(len(self.FHL3.table)): if in_roi[ks] == True: model = self.FHL3[ks].sky_model() model.spatial_model.parameters.freeze_all() # freeze spatial model.spectral_model.parameters["amplitude"].min = 0.0 if isinstance(model.spectral_model, PowerLawSpectralModel): model.spectral_model.parameters["index"].min = 0.1 model.spectral_model.parameters["index"].max = 10.0 else: model.spectral_model.parameters["alpha"].min = 0.1 model.spectral_model.parameters["alpha"].max = 10.0 FHL3_roi.append(model) model_total = Models(FHL3_roi + [background_model]) dataset.models = model_total cat_stat = dataset.stat_sum() datasets = Datasets([dataset]) log.info(f"ROI {kr}: running fit") fit = Fit(**self.fit_opts) results = fit.run(datasets=datasets) print("ROI_num", str(kr), "\n", results) fit_stat = datasets.stat_sum() if results.message != "Optimization failed.": filedata = Path(self.resdir) / f"3FHL_ROI_num{kr}_datasets.yaml" filemodel = Path(self.resdir) / f"3FHL_ROI_num{kr}_models.yaml" datasets.write(filedata, filemodel, overwrite=True) np.savez( self.resdir / f"3FHL_ROI_num{kr}_fit_infos.npz", message=results.message, stat=[cat_stat, fit_stat], ) exec_time = time() - roi_time print("ROI", kr, " time (s): ", exec_time) log.info(f"ROI {kr}: running flux points") for model in FHL3_roi: if (self.FHL3[model.name].data["ROI_num"] == kr and self.FHL3[model.name].data["Signif_Avg"] >= self.sig_cut): print(model.name) flux_points = FluxPointsEstimator( energy_edges=self.El_flux, source=model.name, n_sigma_ul=2, selection_optional=["ul"], ).run(datasets=datasets) flux_points.meta["sqrt_ts_threshold_ul"] = 1 filename = self.resdir / f"{model.name}_flux_points.fits" flux_points.write(filename, overwrite=True) exec_time = time() - roi_time - exec_time print("ROI", kr, " Flux points time (s): ", exec_time)