def fake_dataset():
axis = MapAxis.from_energy_bounds(0.1, 10, 5, unit="TeV", name="energy")
axis_true = MapAxis.from_energy_bounds(0.05, 20, 10, unit="TeV", name="energy_true")
geom = WcsGeom.create(npix=50, binsz=0.02, axes=[axis])
dataset = MapDataset.create(geom)
dataset.psf = PSFMap.from_gauss(axis_true, sigma="0.05 deg")
dataset.mask_safe += np.ones(dataset.data_shape, dtype=bool)
dataset.background += 1
dataset.exposure += 1e12 * u.cm ** 2 * u.s
spatial_model = PointSpatialModel()
spectral_model = PowerLawSpectralModel(amplitude="1e-10 cm-2s-1TeV-1", index=2)
model = SkyModel(
spatial_model=spatial_model, spectral_model=spectral_model, name="source"
)
dataset.models = [model]
dataset.fake(random_state=42)
return dataset
def test_psf_map_write_gtpsf(tmpdir):
energy_axis_true = MapAxis.from_energy_bounds(
"1 TeV", "10 TeV", nbin=3, name="energy_true"
)
geom = RegionGeom.create("icrs;circle(0, 0, 0.1)")
psf = PSFMap.from_gauss(
energy_axis_true=energy_axis_true, sigma=[0.1, 0.2, 0.3] * u.deg, geom=geom
)
psf.exposure_map = Map.from_geom(geom.to_cube([energy_axis_true]), unit="cm2 s")
filename = tmpdir / "test_psf.fits"
psf.write(filename, format="gtpsf")
psf = PSFMap.read(filename, format="gtpsf")
value = psf.containment_radius(energy_true=energy_axis_true.center, fraction=0.394)
assert_allclose(value, [0.1, 0.2, 0.3] * u.deg, rtol=1e-5)
assert psf.psf_map.unit == "sr-1"
def prepare_dataset():
energy = MapAxis.from_energy_bounds(0.1,
100,
5,
per_decade=True,
unit="TeV")
energy_true = MapAxis.from_energy_bounds(0.1,
100,
5,
unit="TeV",
per_decade=True,
name="energy_true")
geom = WcsGeom.create(npix=500, binsz=0.01, axes=[energy])
dataset = MapDataset.create(geom, energy_axis_true=energy_true)
dataset.exposure += "1 m2 s"
dataset.psf = PSFMap.from_gauss(energy_true)
dataset.edisp = EDispKernelMap.from_gauss(energy, energy_true, 0.1, 0.)
return Datasets([dataset])
def test_significance_map_estimator_map_dataset_exposure(simple_dataset):
simple_dataset.exposure += 1e10 * u.cm**2 * u.s
axis = simple_dataset.exposure.geom.axes[0]
simple_dataset.psf = PSFMap.from_gauss(axis, sigma="0.05 deg")
model = SkyModel(
PowerLawSpectralModel(amplitude="1e-9 cm-2 s-1 TeV-1"),
GaussianSpatialModel(lat_0=0.0 * u.deg,
lon_0=0.0 * u.deg,
sigma=0.1 * u.deg,
frame="icrs"),
name="sky_model",
)
simple_dataset.models = [model]
simple_dataset.npred()
estimator = ExcessMapEstimator(0.1 * u.deg, selection_optional="all")
result = estimator.run(simple_dataset)
assert_allclose(result["npred_excess"].data.sum(), 19733.602, rtol=1e-3)
assert_allclose(result["sqrt_ts"].data[0, 10, 10], 4.217129, rtol=1e-3)
def test_convolve_nd():
energy_axis = MapAxis.from_edges(np.logspace(-1.0, 1.0, 4),
unit="TeV",
name="energy_true")
geom = WcsGeom.create(binsz=0.02 * u.deg,
width=4.0 * u.deg,
axes=[energy_axis])
m = Map.from_geom(geom)
m.fill_by_coord([[0.2, 0.4], [-0.1, 0.6], [0.5, 3.6]])
psf = PSFMap.from_gauss(energy_axis, sigma=[0.1, 0.2, 0.3] * u.deg)
psf_kernel = psf.get_psf_kernel(geom=geom, max_radius=1 * u.deg)
assert psf_kernel.psf_kernel_map.data.shape == (3, 101, 101)
mc = m.convolve(psf_kernel)
assert_allclose(mc.data.sum(axis=(1, 2)), [0, 1, 1], atol=1e-5)
kernel_2d = Box2DKernel(3, mode="center")
kernel_2d.normalize("peak")
mc = m.convolve(kernel_2d.array)
assert_allclose(mc.data[0, :, :].sum(), 0, atol=1e-5)
assert_allclose(mc.data[1, :, :].sum(), 9, atol=1e-5)
kernel_2d = Gaussian2DKernel(15, mode="center")
kernel_2d.normalize("peak")
mc_full = m.convolve(kernel_2d.array, mode="full")
mc_same = m.convolve(kernel_2d.array, mode="same")
coords = [
[0.2, 0.1, 0.4, 0.44, -1.3],
[-0.1, -0.13, 0.6, 0.57, 0.91],
[0.5, 0.5, 3.6, 3.6, 0.5],
]
values_full = mc_full.get_by_coord(coords)
values_same = mc_same.get_by_coord(coords)
assert mc_same.data.shape == (3, 200, 200)
assert mc_full.data.shape == (3, 320, 320)
assert_allclose(values_full, values_same, rtol=1e-5)
def test_interpolate_map_dataset():
energy = MapAxis.from_energy_bounds("1 TeV", "300 TeV", nbin=5, name="energy")
energy_true = MapAxis.from_nodes(np.logspace(-1, 3, 20), name="energy_true", interp="log", unit="TeV")
# make dummy map IRFs
geom_allsky = WcsGeom.create(npix=(5, 3), proj="CAR", binsz=60, axes=[energy], skydir=(0, 0))
geom_allsky_true = geom_allsky.drop('energy').to_cube([energy_true])
#background
value = 30
bkg_map = Map.from_geom(geom_allsky, unit="")
bkg_map.data = value*np.ones(bkg_map.data.shape)
#effective area - with a gradient that also depends on energy
aeff_map = Map.from_geom(geom_allsky_true, unit="cm2 s")
ra_arr = np.arange(aeff_map.data.shape[1])
dec_arr = np.arange(aeff_map.data.shape[2])
for i in np.arange(aeff_map.data.shape[0]):
aeff_map.data[i, :, :] = (i+1)*10*np.meshgrid(dec_arr, ra_arr)[0]+10*np.meshgrid(dec_arr, ra_arr)[1]+10
aeff_map.meta["TELESCOP"] = "HAWC"
#psf map
width = 0.2*u.deg
rad_axis = MapAxis.from_nodes(np.linspace(0, 2, 50), name="rad", unit="deg")
psfMap = PSFMap.from_gauss(energy_true, rad_axis, width)
#edispmap
edispmap = EDispKernelMap.from_gauss(energy, energy_true, sigma=0.1, bias=0.0, geom=geom_allsky)
#events and gti
nr_ev = 10
ev_t = Table()
gti_t = Table()
ev_t['EVENT_ID'] = np.arange(nr_ev)
ev_t['TIME'] = nr_ev*[Time('2011-01-01 00:00:00', scale='utc', format='iso')]
ev_t['RA'] = np.linspace(-1, 1, nr_ev)*u.deg
ev_t['DEC'] = np.linspace(-1, 1, nr_ev)*u.deg
ev_t['ENERGY'] = np.logspace(0, 2, nr_ev)*u.TeV
gti_t['START'] = [Time('2010-12-31 00:00:00', scale='utc', format='iso')]
gti_t['STOP'] = [Time('2011-01-02 00:00:00', scale='utc', format='iso')]
events = EventList(ev_t)
gti = GTI(gti_t)
#define observation
obs = Observation(
obs_id=0,
obs_info={},
gti=gti,
aeff=aeff_map,
edisp=edispmap,
psf=psfMap,
bkg=bkg_map,
events=events,
obs_filter=None,
)
#define analysis geometry
geom_target = WcsGeom.create(
skydir=(0, 0),
width=(10, 10),
binsz=0.1*u.deg,
axes=[energy]
)
maker = MapDatasetMaker(selection=["exposure", "counts", "background", "edisp", "psf"])
dataset = MapDataset.create(geom=geom_target, energy_axis_true=energy_true, name="test")
dataset = maker.run(dataset, obs)
# test counts
assert dataset.counts.data.sum() == nr_ev
#test background
coords_bg = {
'skycoord' : SkyCoord("0 deg", "0 deg"),
'energy' : energy.center[0]
}
assert_allclose(
dataset.background_model.evaluate().get_by_coord(coords_bg)[0],
value,
atol=1e-7)
#test effective area
coords_aeff = {
'skycoord' : SkyCoord("0 deg", "0 deg"),
'energy_true' : energy_true.center[0]
}
assert_allclose(
aeff_map.get_by_coord(coords_aeff)[0]/dataset.exposure.get_by_coord(coords_aeff)[0],
1,
atol=1e-3)
#test edispmap
pdfmatrix_preinterp = edispmap.get_edisp_kernel(SkyCoord("0 deg", "0 deg")).pdf_matrix
pdfmatrix_postinterp = dataset.edisp.get_edisp_kernel(SkyCoord("0 deg", "0 deg")).pdf_matrix
assert_allclose(pdfmatrix_preinterp, pdfmatrix_postinterp, atol=1e-7)
#test psfmap
geom_psf = geom_target.drop('energy').to_cube([energy_true])
psfkernel_preinterp = psfMap.get_psf_kernel(SkyCoord("0 deg", "0 deg"), geom_psf, max_radius=2*u.deg).data
psfkernel_postinterp = dataset.psf.get_psf_kernel(SkyCoord("0 deg", "0 deg"), geom_psf, max_radius=2*u.deg).data
assert_allclose(psfkernel_preinterp, psfkernel_postinterp, atol=1e-4)