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")
