def test_asmooth_map_dataset_on_off():
kernel = Tophat2DKernel
scales = ASmoothMapEstimator.get_scales(3, factor=2,
kernel=kernel) * 0.1 * u.deg
asmooth = ASmoothMapEstimator(kernel=kernel,
scales=scales,
method="lima",
threshold=2.5)
counts = WcsNDMap.create(npix=(50, 50), binsz=0.02, unit="")
counts += 2
counts_off = WcsNDMap.create(npix=(50, 50), binsz=0.02, unit="")
counts_off += 3
acceptance = WcsNDMap.create(npix=(50, 50), binsz=0.02, unit="")
acceptance += 1
acceptance_off = WcsNDMap.create(npix=(50, 50), binsz=0.02, unit="")
acceptance_off += 3
dataset = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
smoothed = asmooth.run(dataset)
assert_allclose(smoothed["counts"].data[25, 25], 2)
assert_allclose(smoothed["background"].data[25, 25], 1)
assert_allclose(smoothed["significance"].data[25, 25], 4.391334)
def test_map_dataset_on_off_to_image():
axis = MapAxis.from_energy_bounds(1, 10, 2, unit="TeV")
geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis])
counts = Map.from_geom(geom, data=np.ones((2, 10, 10)))
counts_off = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance_off = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance_off *= 2
dataset = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
image_dataset = dataset.to_image()
assert image_dataset.counts.data.shape == (1, 10, 10)
assert image_dataset.acceptance_off.data.shape == (1, 10, 10)
assert_allclose(image_dataset.acceptance, 2)
assert_allclose(image_dataset.acceptance_off, 4)
assert_allclose(image_dataset.counts_off, 2)
assert image_dataset.name != dataset.name
# Try with a safe_mask
mask_safe = Map.from_geom(geom, data=np.ones((2, 10, 10), dtype="bool"))
mask_safe.data[0] = 0
dataset.mask_safe = mask_safe
image_dataset = dataset.to_image()
assert_allclose(image_dataset.acceptance, 1)
assert_allclose(image_dataset.acceptance_off, 2)
assert_allclose(image_dataset.counts_off, 1)
def get_map_dataset_onoff(images, **kwargs):
"""Returns a MapDatasetOnOff"""
mask_geom = images["counts"].geom
mask_data = np.ones(images["counts"].data.shape, dtype=bool)
mask_safe = Map.from_geom(mask_geom, data=mask_data)
return MapDatasetOnOff(counts=images["counts"],
counts_off=images["counts_off"],
acceptance=images["acceptance"],
acceptance_off=images["acceptance_off"],
exposure=images["exposure"],
mask_safe=mask_safe,
**kwargs)
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")
counts = image_to_cube(n_on, "1 TeV", "100 TeV")
n_off = Map.read(filename, hdu="OFF")
counts_off = image_to_cube(n_off, "1 TeV", "100 TeV")
a_on = Map.read(filename, hdu="ONEXPOSURE")
acceptance = image_to_cube(a_on, "1 TeV", "100 TeV")
a_off = Map.read(filename, hdu="OFFEXPOSURE")
acceptance_off = image_to_cube(a_off, "1 TeV", "100 TeV")
dataset = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
significance = Map.read(filename, hdu="SIGNIFICANCE")
significance = image_to_cube(significance, "1 TeV", "10 TeV")
estimator = ExcessMapEstimator("0.1 deg", correlate_off=False)
results = estimator.run(dataset)
# Reproduce safe significance threshold from HESS software
results["sqrt_ts"].data[results["npred"].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["sqrt_ts"].crop((11, 11)).data
desired = significance.crop((11, 11)).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, rtol=1e-5)
actual = np.nan_to_num(results["npred_background"].crop((11, 11)).data)
background_corr = image_to_cube(
Map.read(filename, hdu="BACKGROUNDCORRELATED"), "1 TeV", "100 TeV")
desired = background_corr.crop((11, 11)).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, rtol=1e-5)
def get_map_dataset_onoff(images, **kwargs):
"""Returns a MapDatasetOnOff"""
mask_geom = images["counts"].geom
mask_data = np.ones(images["counts"].data.shape, dtype=bool)
mask_safe = Map.from_geom(mask_geom, data=mask_data)
gti = GTI.create([0 * u.s], [1 * u.h],
reference_time="2010-01-01T00:00:00")
return MapDatasetOnOff(counts=images["counts"],
counts_off=images["counts_off"],
acceptance=images["acceptance"],
acceptance_off=images["acceptance_off"],
exposure=images["exposure"],
mask_safe=mask_safe,
gti=gti,
**kwargs)
def test_asmooth_map_dataset_on_off():
kernel = Tophat2DKernel
scales = ASmoothMapEstimator.get_scales(3, factor=2,
kernel=kernel) * 0.1 * u.deg
asmooth = ASmoothMapEstimator(kernel=kernel,
scales=scales,
method="lima",
threshold=2.5)
axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=1)
counts = WcsNDMap.create(npix=(50, 50), binsz=0.02, unit="", axes=[axis])
counts += 2
counts_off = WcsNDMap.create(npix=(50, 50),
binsz=0.02,
unit="",
axes=[axis])
counts_off += 3
acceptance = WcsNDMap.create(npix=(50, 50),
binsz=0.02,
unit="",
axes=[axis])
acceptance += 1
acceptance_off = WcsNDMap.create(npix=(50, 50),
binsz=0.02,
unit="",
axes=[axis])
acceptance_off += 3
dataset = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
smoothed = asmooth.run(dataset)
assert_allclose(smoothed["counts"].data[25, 25], 2)
assert_allclose(smoothed["background"].data[25, 25], 1.25)
assert_allclose(smoothed["significance"].data[25, 25],
3.079799117645,
rtol=1e-2)
def test_plot_residual_onoff():
axis = MapAxis.from_energy_bounds(1, 10, 2, unit="TeV")
geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis])
counts = Map.from_geom(geom, data=np.ones((2, 10, 10)))
counts_off = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance_off = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance_off *= 2
dataset = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
with mpl_plot_check():
dataset.plot_residuals_spatial()
def test_map_dataset_on_off_to_spectrum_dataset_weights():
e_reco = MapAxis.from_bounds(1, 10, nbin=3, unit="TeV", name="energy")
geom = WcsGeom.create(skydir=(0, 0),
width=(2.5, 2.5),
binsz=0.5,
axes=[e_reco],
frame="galactic")
counts = Map.from_geom(geom)
counts.data += 1
counts_off = Map.from_geom(geom)
counts_off.data += 2
acceptance = Map.from_geom(geom)
acceptance.data += 1
acceptance_off = Map.from_geom(geom)
acceptance_off.data += 4
weights = Map.from_geom(geom, dtype='bool')
weights.data[1:, 2:4, 2] = True
gti = GTI.create([0 * u.s], [1 * u.h],
reference_time="2010-01-01T00:00:00")
dataset = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
mask_safe=weights,
gti=gti,
)
on_region = CircleSkyRegion(center=dataset.counts.geom.center_skydir,
radius=1.5 * u.deg)
spectrum_dataset = dataset.to_spectrum_dataset(on_region)
assert_allclose(spectrum_dataset.counts.data[:, 0, 0], [0, 2, 2])
assert_allclose(spectrum_dataset.counts_off.data[:, 0, 0], [0, 4, 4])
assert_allclose(spectrum_dataset.acceptance.data[:, 0, 0], [0, 0.08, 0.08])
assert_allclose(spectrum_dataset.acceptance_off.data[:, 0, 0],
[0, 0.32, 0.32])
assert_allclose(spectrum_dataset.alpha.data[:, 0, 0], [0, 0.25, 0.25])
def test_downsample_onoff():
axis = MapAxis.from_energy_bounds(1, 10, 4, unit="TeV")
geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis])
counts = Map.from_geom(geom, data=np.ones((4, 10, 10)))
counts_off = Map.from_geom(geom, data=np.ones((4, 10, 10)))
acceptance = Map.from_geom(geom, data=np.ones((4, 10, 10)))
acceptance_off = Map.from_geom(geom, data=np.ones((4, 10, 10)))
acceptance_off *= 2
dataset_onoff = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
downsampled = dataset_onoff.downsample(2, axis_name="energy")
assert downsampled.counts.data.shape == (2, 10, 10)
assert downsampled.counts.data.sum() == dataset_onoff.counts.data.sum()
assert downsampled.counts_off.data.sum() == dataset_onoff.counts_off.data.sum()
assert_allclose(downsampled.alpha.data, 0.5)
def test_to_map_dataset():
axis = MapAxis.from_energy_bounds(1, 10, 2, unit="TeV")
geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis])
counts = Map.from_geom(geom, data=np.ones((2, 10, 10)))
counts_off = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance_off = Map.from_geom(geom, data=np.ones((2, 10, 10)))
acceptance_off *= 2
dataset_onoff = MapDatasetOnOff(
counts=counts,
counts_off=counts_off,
acceptance=acceptance,
acceptance_off=acceptance_off,
)
dataset = dataset_onoff.to_map_dataset(name="ds")
assert dataset.name == "ds"
assert_allclose(dataset.npred_background().data.sum(), 100)
assert isinstance(dataset, MapDataset)
assert dataset.counts == dataset_onoff.counts