def test_large_kernel(input_dataset): """Minimal test of compute_ts_image""" kernel = Gaussian2DKernel(100) ts_estimator = TSMapEstimator() with pytest.raises(ValueError): ts_estimator.run(input_dataset, kernel=kernel)
def test_compute_ts_map_psf(fermi_dataset): spatial_model = PointSpatialModel() spectral_model = PowerLawSpectralModel(amplitude="1e-22 cm-2 s-1 keV-1") model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) estimator = TSMapEstimator(model=model, kernel_width="1 deg", selection_optional="all") result = estimator.run(fermi_dataset) assert_allclose(result["ts"].data[0, 29, 29], 835.140605, rtol=1e-2) assert_allclose(result["niter"].data[0, 29, 29], 7) assert_allclose(result["flux"].data[0, 29, 29], 1.351949e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[0, 29, 29], 7.93751176e-11, rtol=1e-2) assert_allclose(result["flux_errp"].data[0, 29, 29], 7.9376134e-11, rtol=1e-2) assert_allclose(result["flux_errn"].data[0, 29, 29], 7.5180404579e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[0, 29, 29], 1.63222157e-10, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1")
def test_compute_ts_map_energy(fermi_dataset): spatial_model = PointSpatialModel() spectral_model = PowerLawSpectralModel(amplitude="1e-22 cm-2 s-1 keV-1") model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) estimator = TSMapEstimator( model=model, kernel_width="0.6 deg", energy_edges=[10, 100, 1000] * u.GeV, sum_over_energy_groups=False, ) result = estimator.run(fermi_dataset) assert_allclose(result["ts"].data[:, 29, 29], [804.86171, 16.988756], rtol=1e-2) assert_allclose(result["flux"].data[:, 29, 29], [1.233119e-09, 3.590694e-11], rtol=1e-2) assert_allclose(result["flux_err"].data[:, 29, 29], [7.382305e-11, 1.338985e-11], rtol=1e-2) assert_allclose(result["niter"].data[:, 29, 29], [6, 6]) energy_axis = result["ts"].geom.axes["energy"] assert_allclose(energy_axis.edges.to_value("GeV"), [10, 84.471641, 500], rtol=1e-4)
def test_compute_ts_map_newton(input_dataset): """Minimal test of compute_ts_image""" spatial_model = GaussianSpatialModel(sigma="0.1 deg") spectral_model = PowerLawSpectralModel(index=2) model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) ts_estimator = TSMapEstimator(model=model, method="root newton", threshold=1, kernel_width="1 deg") result = ts_estimator.run(input_dataset) assert "root newton" in repr(ts_estimator) assert_allclose(result["ts"].data[99, 99], 1714.23, rtol=1e-2) assert_allclose(result["niter"].data[99, 99], 0) assert_allclose(result["flux"].data[99, 99], 1.02e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[99, 99], 3.84e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[99, 99], 1.10e-09, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1") # Check mask is correctly taken into account assert np.isnan(result["ts"].data[30, 40])
def test_compute_ts_map_downsampled(input_dataset): """Minimal test of compute_ts_image""" spatial_model = GaussianSpatialModel(sigma="0.11 deg") spectral_model = PowerLawSpectralModel(index=2) model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) ts_estimator = TSMapEstimator(model=model, downsampling_factor=2, kernel_width="1 deg", selection_optional=["ul"]) result = ts_estimator.run(input_dataset) assert_allclose(result["ts"].data[0, 99, 99], 1661.49, rtol=1e-2) assert_allclose(result["niter"].data[0, 99, 99], 7) assert_allclose(result["flux"].data[0, 99, 99], 1.065988e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[0, 99, 99], 4.005628e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[0, 99, 99], 8.220152e-11, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1") # Check mask is correctly taken into account assert np.isnan(result["ts"].data[0, 30, 40])
def test_compute_ts_map(input_dataset): """Minimal test of compute_ts_image""" spatial_model = GaussianSpatialModel(sigma="0.1 deg") spectral_model = PowerLawSpectralModel(index=2) model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) ts_estimator = TSMapEstimator(model=model, threshold=1, kernel_width="1 deg", selection_optional=[]) result = ts_estimator.run(input_dataset) assert_allclose(result["ts"].data[0, 99, 99], 1704.23, rtol=1e-2) assert_allclose(result["niter"].data[0, 99, 99], 8) assert_allclose(result["flux"].data[0, 99, 99], 1.02e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[0, 99, 99], 3.84e-11, rtol=1e-2) assert_allclose(result["npred"].data[0, 99, 99], 3627.874063, rtol=1e-2) assert_allclose(result["npred_null"].data[0, 99, 99], 2601, rtol=1e-2) assert_allclose(result["npred_excess"].data[0, 99, 99], 1026.874063, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") # Check mask is correctly taken into account assert np.isnan(result["ts"].data[0, 30, 40]) energy_axis = result["ts"].geom.axes["energy"] assert_allclose(energy_axis.edges.value, [0.1, 1])
def test_large_kernel(input_dataset): """Minimal test of compute_ts_image""" spatial_model = GaussianSpatialModel(sigma="4 deg") spectral_model = PowerLawSpectralModel(index=2) model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) ts_estimator = TSMapEstimator(model=model, kernel_width="4 deg") with pytest.raises(ValueError): ts_estimator.run(input_dataset)
def fit_estimator(dataset): spatial_model = PointSpatialModel() spectral_model = PowerLawSpectralModel(index=2) model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model) estimator = TSMapEstimator(model, kernel_width="1 deg", energy_edges=[10, 30, 300] * u.GeV) images = estimator.run(dataset) return images
def test_compute_ts_map_psf(fermi_dataset): estimator = TSMapEstimator(kernel_width="1 deg") result = estimator.run(fermi_dataset) assert "root brentq" in repr(estimator) assert_allclose(result["ts"].data[29, 29], 852.1548, rtol=1e-2) assert_allclose(result["niter"].data[29, 29], 7) assert_allclose(result["flux"].data[29, 29], 1.419909e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[29, 29], 8.245766e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[29, 29], 1.584825e-09, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1")
def test_compute_ts_map_psf(fermi_dataset): estimator = TSMapEstimator(kernel_width="1 deg") result = estimator.run(fermi_dataset) assert_allclose(result["ts"].data[0, 29, 29], 835.140605, rtol=1e-2) assert_allclose(result["niter"].data[0, 29, 29], 7) assert_allclose(result["flux"].data[0, 29, 29], 1.351949e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[0, 29, 29], 7.93751176e-11, rtol=1e-2) assert_allclose(result["flux_errp"].data[0, 29, 29], 7.9376134e-11, rtol=1e-2) assert_allclose(result["flux_errn"].data[0, 29, 29], 7.5180404579e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[0, 29, 29], 1.63222157e-10, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1")
def test_compute_ts_map_psf(fermi_dataset): estimator = TSMapEstimator(kernel_width="1 deg") result = estimator.run(fermi_dataset) assert_allclose(result["ts"].data[29, 29], 835.140605, rtol=1e-2) assert_allclose(result["niter"].data[29, 29], 7) assert_allclose(result["flux"].data[29, 29], 1.626651e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[29, 29], 9.548939e-11, rtol=1e-2) assert_allclose(result["flux_errp"].data[29, 29], 9.540259e-11, rtol=1e-2) assert_allclose(result["flux_errn"].data[29, 29], 9.034366e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[29, 29], 1.961776e-10, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1")
def test_ts_map_with_model(fake_dataset): model = fake_dataset.models["source"] fake_dataset.models = [] estimator = TSMapEstimator( model, kernel_width="0.3 deg", selection_optional=[], energy_edges=[200, 3500] * u.GeV, ) maps = estimator.run(fake_dataset) assert_allclose(maps["sqrt_ts"].data[:, 25, 25], 18.369942, atol=0.1) assert_allclose(maps["flux"].data[:, 25, 25], 3.513e-10, atol=1e-12) fake_dataset.models = [model] maps = estimator.run(fake_dataset) assert_allclose(maps["sqrt_ts"].data[:, 25, 25], -0.231187, atol=0.1) assert_allclose(maps["flux"].data[:, 25, 25], -5.899423e-12, atol=1e-12) # Try downsmapling estimator = TSMapEstimator( model, kernel_width="0.3 deg", selection_optional=[], downsampling_factor=2, energy_edges=[200, 3500] * u.GeV, ) maps = estimator.run(fake_dataset) assert_allclose(maps["sqrt_ts"].data[:, 25, 25], 0.323, atol=0.1) assert_allclose(maps["flux"].data[:, 25, 25], 1.015417e-12, atol=1e-12)
def test_compute_ts_map_newton(input_dataset): """Minimal test of compute_ts_image""" kernel = Gaussian2DKernel(5) ts_estimator = TSMapEstimator(method="root newton", threshold=1) result = ts_estimator.run(input_dataset, kernel=kernel) assert "root newton" in repr(ts_estimator) assert_allclose(result["ts"].data[99, 99], 1714.23, rtol=1e-2) assert_allclose(result["niter"].data[99, 99], 0) assert_allclose(result["flux"].data[99, 99], 1.02e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[99, 99], 3.84e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[99, 99], 1.10e-09, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1") # Check mask is correctly taken into account assert np.isnan(result["ts"].data[30, 40])
def test_compute_ts_map_energy(fermi_dataset): estimator = TSMapEstimator(kernel_width="0.6 deg", e_edges=[10, 100, 1000] * u.GeV, sum_over_energy_groups=False) result = estimator.run(fermi_dataset) assert_allclose(result["ts"].data[:, 29, 29], [804.86171, 16.988756], rtol=1e-2) assert_allclose(result["flux"].data[:, 29, 29], [1.233119e-09, 3.590694e-11], rtol=1e-2) assert_allclose(result["flux_err"].data[:, 29, 29], [7.382305e-11, 1.338985e-11], rtol=1e-2) assert_allclose(result["niter"].data[:, 29, 29], [6, 6]) energy_axis = result["ts"].geom.axes["energy"] assert_allclose(energy_axis.edges.to_value("GeV"), [10, 84.471641, 500], rtol=1e-4)
def test_compute_ts_map_downsampled(input_dataset): """Minimal test of compute_ts_image""" kernel = Gaussian2DKernel(2.5) ts_estimator = TSMapEstimator(method="root brentq", error_method="conf", ul_method="conf") result = ts_estimator.run(input_dataset, kernel=kernel, downsampling_factor=2) assert_allclose(result["ts"].data[99, 99], 1675.28, rtol=1e-2) assert_allclose(result["niter"].data[99, 99], 7) assert_allclose(result["flux"].data[99, 99], 1.02e-09, rtol=1e-2) assert_allclose(result["flux_err"].data[99, 99], 3.84e-11, rtol=1e-2) assert_allclose(result["flux_ul"].data[99, 99], 1.10e-09, rtol=1e-2) assert result["flux"].unit == u.Unit("cm-2s-1") assert result["flux_err"].unit == u.Unit("cm-2s-1") assert result["flux_ul"].unit == u.Unit("cm-2s-1") # Check mask is correctly taken into account assert np.isnan(result["ts"].data[30, 40])
def test_large_kernel(input_dataset): """Minimal test of compute_ts_image""" ts_estimator = TSMapEstimator(kernel_width="4 deg") with pytest.raises(ValueError): ts_estimator.run(input_dataset)
def test_incorrect_method(): with pytest.raises(ValueError): TSMapEstimator(method="bad") with pytest.raises(ValueError): TSMapEstimator(error_method="bad")
def test_incorrect_method(): model = GaussianSpatialModel(sigma="0.2 deg") with pytest.raises(ValueError): TSMapEstimator(model, method="bad") with pytest.raises(ValueError): TSMapEstimator(model, error_method="bad")