def test_compute_flux_spatial(): center = SkyCoord("0 deg", "0 deg", frame="galactic") region = CircleSkyRegion(center=center, radius=0.1 * u.deg) nbin = 2 energy_axis_true = MapAxis.from_energy_bounds(".1 TeV", "10 TeV", nbin=nbin, name="energy_true") spectral_model = ConstantSpectralModel() spatial_model = PointSpatialModel(lon_0=0 * u.deg, lat_0=0 * u.deg, frame="galactic") models = SkyModel(spectral_model=spectral_model, spatial_model=spatial_model) model = Models(models) exposure_region = RegionNDMap.create(region, axes=[energy_axis_true], binsz_wcs="0.01deg") exposure_region.data += 1.0 exposure_region.unit = "m2 s" geom = RegionGeom(region, axes=[energy_axis_true], binsz_wcs="0.01deg") psf = PSFKernel.from_gauss(geom.to_wcs_geom(), sigma="0.1 deg") evaluator = MapEvaluator(model=model[0], exposure=exposure_region, psf=psf) flux = evaluator.compute_flux_spatial() g = Gauss2DPDF(0.1) reference = g.containment_fraction(0.1) assert_allclose(flux.value, reference, rtol=0.003)
def test_convolve_pixel_scale_error(): m = WcsNDMap.create(binsz=0.05 * u.deg, width=5 * u.deg) kgeom = WcsGeom.create(binsz=0.04 * u.deg, width=0.5 * u.deg) kernel = PSFKernel.from_gauss(kgeom, sigma=0.1 * u.deg, max_radius=1.5 * u.deg) with pytest.raises(ValueError): m.convolve(kernel)
def test_convolve_kernel_size_error(): axis_1 = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=2) axis_2 = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=3) m = WcsNDMap.create(binsz=0.05 * u.deg, width=5 * u.deg, axes=[axis_1]) kgeom = WcsGeom.create(binsz=0.05 * u.deg, width=0.5 * u.deg, axes=[axis_2]) kernel = PSFKernel.from_gauss(kgeom, sigma=0.1 * u.deg, max_radius=1.5 * u.deg) with pytest.raises(ValueError): m.convolve(kernel)
def test_convolve_wcs(nest): energy = MapAxis.from_bounds(1, 100, unit='TeV', nbin=2, name='energy') nside = 256 hpx_geom = HpxGeom.create(nside=nside, axes=[energy], region='DISK(0,0,2.5)', nest=nest) hpx_map = Map.from_geom(hpx_geom) hpx_map.set_by_coord((0, 0, [2, 90]), 1) wcs_geom = WcsGeom.create(width=5, binsz=0.04, axes=[energy]) kernel = PSFKernel.from_gauss(wcs_geom, 0.4 * u.deg) convolved_map = hpx_map.convolve_wcs(kernel) assert_allclose(convolved_map.data.sum(), 2, rtol=0.001)
def test_psf_kernel_from_gauss_read_write(tmp_path): sigma = 0.5 * u.deg binsz = 0.1 * u.deg geom = WcsGeom.create(binsz=binsz, npix=150, axes=[MapAxis((0, 1, 2))]) kernel = PSFKernel.from_gauss(geom, sigma) # Check that both maps are identical assert_allclose(kernel.psf_kernel_map.data[0], kernel.psf_kernel_map.data[1]) # Is there an odd number of pixels assert_allclose(np.array(kernel.psf_kernel_map.geom.npix) % 2, 1) kernel.write(tmp_path / "tmp.fits", overwrite=True) kernel2 = PSFKernel.read(tmp_path / "tmp.fits") assert_allclose(kernel.psf_kernel_map.data, kernel2.psf_kernel_map.data)
def test_large_oversampling(): nbin = 2 energy_axis_true = MapAxis.from_energy_bounds(".1 TeV", "10 TeV", nbin=nbin, name="energy_true") geom = WcsGeom.create(width=1, binsz=0.02, axes=[energy_axis_true]) spectral_model = ConstantSpectralModel() spatial_model = GaussianSpatialModel(lon_0=0 * u.deg, lat_0=0 * u.deg, sigma=1e-4 * u.deg, frame="icrs") models = SkyModel(spectral_model=spectral_model, spatial_model=spatial_model) model = Models(models) exposure = Map.from_geom(geom, unit="m2 s") exposure.data += 1.0 psf = PSFKernel.from_gauss(geom, sigma="0.1 deg") evaluator = MapEvaluator(model=model[0], exposure=exposure, psf=psf) flux_1 = evaluator.compute_flux_spatial() spatial_model.sigma.value = 0.001 flux_2 = evaluator.compute_flux_spatial() spatial_model.sigma.value = 0.01 flux_3 = evaluator.compute_flux_spatial() spatial_model.sigma.value = 0.03 flux_4 = evaluator.compute_flux_spatial() assert_allclose(flux_1.data.sum(), nbin, rtol=1e-4) assert_allclose(flux_2.data.sum(), nbin, rtol=1e-4) assert_allclose(flux_3.data.sum(), nbin, rtol=1e-4) assert_allclose(flux_4.data.sum(), nbin, rtol=1e-4)
def test_compute_npred_sign(): center = SkyCoord("0 deg", "0 deg", frame="galactic") energy_axis_true = MapAxis.from_energy_bounds(".1 TeV", "10 TeV", nbin=2, name="energy_true") geom = WcsGeom.create(skydir=center, width=1 * u.deg, axes=[energy_axis_true], frame='galactic', binsz=0.2 * u.deg) spectral_model_pos = PowerLawSpectralModel(index=2, amplitude="1e-11 TeV-1 s-1 m-2") spectral_model_neg = PowerLawSpectralModel( index=2, amplitude="-1e-11 TeV-1 s-1 m-2") spatial_model = PointSpatialModel(lon_0=0 * u.deg, lat_0=0 * u.deg, frame="galactic") model_pos = SkyModel(spectral_model=spectral_model_pos, spatial_model=spatial_model) model_neg = SkyModel(spectral_model=spectral_model_neg, spatial_model=spatial_model) exposure = Map.from_geom(geom, unit="m2 s") exposure.data += 1.0 psf = PSFKernel.from_gauss(geom, sigma="0.1 deg") evaluator_pos = MapEvaluator(model=model_pos, exposure=exposure, psf=psf) evaluator_neg = MapEvaluator(model=model_neg, exposure=exposure, psf=psf) npred_pos = evaluator_pos.compute_npred() npred_neg = evaluator_neg.compute_npred() assert (npred_pos.data == -npred_neg.data).all() assert np.all(npred_pos.data >= 0) assert np.all(npred_neg.data <= 0)
def psf(geom_true): sigma = 0.5 * u.deg return PSFKernel.from_gauss(geom_true, sigma)