def test_spectrum_dataset_stack_nondiagonal_no_bkg(spectrum_dataset):
energy = spectrum_dataset.counts.geom.axes["energy"]
geom = spectrum_dataset.counts.geom.to_image()
edisp1 = EDispKernelMap.from_gauss(
energy_axis=energy,
energy_axis_true=energy.copy(name="energy_true"),
sigma=0.1,
bias=0,
geom=geom)
edisp1.exposure_map.data += 1
aeff = EffectiveAreaTable.from_parametrization(
energy.edges, "HESS").to_region_map(geom.region)
geom = spectrum_dataset.counts.geom
counts = RegionNDMap.from_geom(geom=geom)
gti = GTI.create(start=0 * u.s, stop=100 * u.s)
spectrum_dataset1 = SpectrumDataset(
counts=counts,
exposure=aeff * gti.time_sum,
edisp=edisp1,
meta_table=Table({"OBS_ID": [0]}),
gti=gti.copy(),
)
edisp2 = EDispKernelMap.from_gauss(
energy_axis=energy,
energy_axis_true=energy.copy(name="energy_true"),
sigma=0.2,
bias=0.0,
geom=geom)
edisp2.exposure_map.data += 1
gti2 = GTI.create(start=100 * u.s, stop=200 * u.s)
spectrum_dataset2 = SpectrumDataset(
counts=counts,
exposure=aeff * gti2.time_sum,
edisp=edisp2,
meta_table=Table({"OBS_ID": [1]}),
gti=gti2,
)
spectrum_dataset1.stack(spectrum_dataset2)
assert_allclose(spectrum_dataset1.meta_table["OBS_ID"][0], [0, 1])
assert spectrum_dataset1.background_model is None
assert_allclose(spectrum_dataset1.gti.time_sum.to_value("s"), 200)
assert_allclose(spectrum_dataset1.exposure.quantity[2].to_value("m2 s"),
1573851.079861)
kernel = edisp1.get_edisp_kernel()
assert_allclose(kernel.get_bias(1 * u.TeV), 0.0, atol=1.2e-3)
assert_allclose(kernel.get_resolution(1 * u.TeV), 0.1581, atol=1e-2)
def make_observation_list():
"""obs with dummy IRF"""
nbin = 3
energy = np.logspace(-1, 1, nbin + 1) * u.TeV
livetime = 2 * u.h
data_on = np.arange(nbin)
dataoff_1 = np.ones(3)
dataoff_2 = np.ones(3) * 3
dataoff_1[1] = 0
dataoff_2[1] = 0
axis = MapAxis.from_edges(energy, name="energy", interp="log")
axis_true = axis.copy(name="energy_true")
geom = RegionGeom(region=None, axes=[axis])
geom_true = RegionGeom(region=None, axes=[axis_true])
on_vector = RegionNDMap.from_geom(geom=geom, data=data_on)
off_vector1 = RegionNDMap.from_geom(geom=geom, data=dataoff_1)
off_vector2 = RegionNDMap.from_geom(geom=geom, data=dataoff_2)
mask_safe = RegionNDMap.from_geom(geom, dtype=bool)
mask_safe.data += True
aeff = RegionNDMap.from_geom(geom_true, data=1, unit="m2")
edisp = EDispKernelMap.from_gauss(
energy_axis=axis, energy_axis_true=axis, sigma=0.2, bias=0, geom=geom
)
time_ref = Time("2010-01-01")
gti1 = make_gti({"START": [5, 6, 1, 2], "STOP": [8, 7, 3, 4]}, time_ref=time_ref)
gti2 = make_gti({"START": [14], "STOP": [15]}, time_ref=time_ref)
exposure = aeff * livetime
exposure.meta["livetime"] = livetime
obs1 = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=off_vector1,
exposure=exposure,
edisp=edisp,
mask_safe=mask_safe,
acceptance=1,
acceptance_off=2,
name="1",
gti=gti1,
)
obs2 = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=off_vector2,
exposure=exposure.copy(),
edisp=edisp,
mask_safe=mask_safe,
acceptance=1,
acceptance_off=4,
name="2",
gti=gti2,
)
obs_list = [obs1, obs2]
return obs_list
def run(self, geom):
"""Create and fill the map dataset"""
energy = geom.axes[0]
energy_true = energy.copy(name="energy_true")
geom_true = geom.to_image().to_cube([energy_true])
dataset = MapDataset.create(geom,
energy_axis_true=energy_true,
binsz_irf=1.0)
dataset.mask_safe.data += True
dataset.counts.fill_events(self.events)
dataset.gti = self._make_gti()
dataset.psf = self.psf
# recompute exposure on geom
coords = geom_true.get_coord()
data = self.exposure.interp_by_coord(coords)
dataset.exposure = Map.from_geom(geom_true,
data=data,
unit=self.exposure.unit)
# Not the real Fermi-LAT EDISP: Use 5% energy resolution as approximation
edisp = EDispKernelMap.from_gauss(energy_axis=energy,
energy_axis_true=energy_true,
sigma=0.05,
bias=0)
dataset.edisp = edisp
return dataset
def test_spectrum_dataset_stack_nondiagonal_no_bkg(spectrum_dataset):
energy = spectrum_dataset.counts.geom.axes[0]
aeff = EffectiveAreaTable.from_parametrization(energy.edges, "HESS")
geom = spectrum_dataset.counts.geom.to_image()
edisp1 = EDispKernelMap.from_gauss(energy, energy, 0.1, 0, geom=geom)
edisp1.exposure_map.data += 1
livetime = 100 * u.s
spectrum_dataset1 = SpectrumDataset(
counts=spectrum_dataset.counts.copy(),
livetime=livetime,
aeff=aeff,
edisp=edisp1,
)
livetime2 = livetime
aeff2 = EffectiveAreaTable(energy.edges[:-1], energy.edges[1:],
aeff.data.data)
edisp2 = EDispKernelMap.from_gauss(energy, energy, 0.2, 0.0, geom=geom)
edisp2.exposure_map.data += 1
spectrum_dataset2 = SpectrumDataset(
counts=spectrum_dataset.counts.copy(),
livetime=livetime2,
aeff=aeff2,
edisp=edisp2,
)
spectrum_dataset1.stack(spectrum_dataset2)
assert spectrum_dataset1.background is None
assert spectrum_dataset1.livetime == 2 * livetime
assert_allclose(spectrum_dataset1.aeff.data.data.to_value("m2"),
aeff.data.data.to_value("m2"))
kernel = edisp1.get_edisp_kernel()
assert_allclose(kernel.get_bias(1 * u.TeV), 0.0, atol=1.2e-3)
assert_allclose(kernel.get_resolution(1 * u.TeV), 0.1581, atol=1e-2)
def test_spectrum_dataset_stack_nondiagonal_no_bkg(spectrum_dataset):
energy = spectrum_dataset.counts.geom.axes[0]
geom = spectrum_dataset.counts.geom.to_image()
edisp1 = EDispKernelMap.from_gauss(energy, energy, 0.1, 0, geom=geom)
edisp1.exposure_map.data += 1
aeff = EffectiveAreaTable.from_parametrization(
energy.edges, "HESS").to_region_map(geom.region)
livetime = 100 * u.s
spectrum_dataset1 = SpectrumDataset(counts=spectrum_dataset.counts.copy(),
livetime=livetime,
aeff=aeff,
edisp=edisp1,
meta_table=Table({"OBS_ID": [0]}))
livetime2 = livetime
aeff2 = aeff.copy()
edisp2 = EDispKernelMap.from_gauss(energy, energy, 0.2, 0.0, geom=geom)
edisp2.exposure_map.data += 1
spectrum_dataset2 = SpectrumDataset(counts=spectrum_dataset.counts.copy(),
livetime=livetime2,
aeff=aeff2,
edisp=edisp2,
meta_table=Table({"OBS_ID": [1]}))
spectrum_dataset1.stack(spectrum_dataset2)
assert_allclose(spectrum_dataset1.meta_table["OBS_ID"][0], [0, 1])
assert spectrum_dataset1.background is None
assert spectrum_dataset1.livetime == 2 * livetime
assert_allclose(spectrum_dataset1.aeff.quantity.to_value("m2"),
aeff.quantity.to_value("m2"))
kernel = edisp1.get_edisp_kernel()
assert_allclose(kernel.get_bias(1 * u.TeV), 0.0, atol=1.2e-3)
assert_allclose(kernel.get_resolution(1 * u.TeV), 0.1581, atol=1e-2)
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_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)
