def test_significance_map_estimator_map_dataset(simple_dataset):
estimator = LiMaMapEstimator(0.1 * u.deg)
result = estimator.run(simple_dataset)
assert_allclose(result["counts"].data[0, 25, 25], 162)
assert_allclose(result["excess"].data[0, 25, 25], 81)
assert_allclose(result["background"].data[0, 25, 25], 81)
assert_allclose(result["significance"].data[0, 25, 25], 7.910732)
def test_significance_map_estimator_map_dataset_on_off(simple_dataset_on_off):
estimator = LiMaMapEstimator(0.1 * u.deg)
result = estimator.run(simple_dataset_on_off)
assert_allclose(result["n_on"].data[0, 25, 25], 162)
assert_allclose(result["excess"].data[0, 25, 25], 81)
assert_allclose(result["background"].data[0, 25, 25], 81)
assert_allclose(result["significance"].data[0, 25, 25], 5.246298)
def test_compute_lima_on_off_image():
"""
Test Li & Ma image with snippet from the H.E.S.S. survey data.
"""
filename = "$GAMMAPY_DATA/tests/unbundled/hess/survey/hess_survey_snippet.fits.gz"
n_on = Map.read(filename, hdu="ON")
n_off = Map.read(filename, hdu="OFF")
a_on = Map.read(filename, hdu="ONEXPOSURE")
a_off = Map.read(filename, hdu="OFFEXPOSURE")
significance = Map.read(filename, hdu="SIGNIFICANCE")
kernel = Tophat2DKernel(5)
results = LiMaMapEstimator.compute_lima_on_off_image(
n_on, n_off, a_on, a_off, kernel)
# Reproduce safe significance threshold from HESS software
results["significance"].data[results["n_on"].data < 5] = 0
# crop the image at the boundaries, because the reference image
# is cut out from a large map, there is no way to reproduce the
# result with regular boundary handling
actual = results["significance"].crop(kernel.shape).data
desired = significance.crop(kernel.shape).data
# Set boundary to NaN in reference image
# The absolute tolerance is low because the method used here is slightly different from the one used in HGPS
# n_off is convolved as well to ensure the method applies to true ON-OFF datasets
assert_allclose(actual, desired, atol=0.2)
def plot_residual_distribution(dataset, obs_id):
# plot residual significance distribution
model = dataset.models[1]
if model.tag == "SkyDiffuseCube":
log.info(f"SkyDiffuseCube: no spectral model to plot")
else:
tophat_2D_kernel = Tophat2DKernel(5)
l_m = lima.compute_lima_image(
dataset.counts.sum_over_axes(keepdims=False),
dataset.npred().sum_over_axes(keepdims=False),
tophat_2D_kernel,
)
sig_resid = l_m["significance"].data[np.isfinite(
l_m["significance"].data)]
# resid = dataset.residuals()
# sig_resid = resid.data[np.isfinite(resid.data)]
plt.hist(
sig_resid,
density=True,
alpha=0.5,
color="red",
bins=100,
)
mu, std = norm.fit(sig_resid)
# replace with log.info()
print("Fit results: mu = {:.2f}, std = {:.2f}".format(mu, std))
x = np.linspace(-8, 8, 50)
p = norm.pdf(x, mu, std)
plt.plot(
x,
p,
lw=2,
color="black",
label="Fit results: mu = {:.2f}, std = {:.2f}".format(mu, std),
)
plt.legend()
plt.xlabel("Significance")
plt.yscale("log")
plt.ylim(1e-5, 1)
xmin, xmax = np.min(sig_resid), np.max(sig_resid)
plt.xlim(xmin, xmax)
obs_id = int(obs_id)
filename = f"results/models/{model.name}/plots/residuals-distribution/residuals-distribution_{obs_id:04d}.png"
save_figure(filename)
def test_compute_lima_image():
"""
Test Li & Ma image against TS image for Tophat kernel
"""
filename = "$GAMMAPY_DATA/tests/unbundled/poisson_stats_image/input_all.fits.gz"
counts = Map.read(filename, hdu="counts")
background = Map.read(filename, hdu="background")
kernel = Tophat2DKernel(5)
result_lima = LiMaMapEstimator.compute_lima_image(counts, background,
kernel)
assert_allclose(result_lima["significance"].data[100, 100],
30.814916,
atol=1e-3)
assert_allclose(result_lima["significance"].data[1, 1], 0.164, atol=1e-3)
def test_significance_map_estimator_incorrect_dataset():
estimator = LiMaMapEstimator("0.1 deg")
with pytest.raises(ValueError):
estimator.run("bad")