def simulate_spectrum_dataset(model, random_state=0):
energy_edges = np.logspace(-0.5, 1.5, 21) * u.TeV
energy_axis = MapAxis.from_edges(energy_edges, interp="log", name="energy")
aeff = EffectiveAreaTable.from_parametrization(
energy=energy_edges).to_region_map()
bkg_model = SkyModel(
spectral_model=PowerLawSpectralModel(index=2.5,
amplitude="1e-12 cm-2 s-1 TeV-1"),
name="background",
)
bkg_model.spectral_model.amplitude.frozen = True
bkg_model.spectral_model.index.frozen = True
geom = RegionGeom(region=None, axes=[energy_axis])
acceptance = RegionNDMap.from_geom(geom=geom, data=1)
edisp = EDispKernelMap.from_diagonal_response(
energy_axis=energy_axis,
energy_axis_true=energy_axis.copy(name="energy_true"),
geom=geom,
)
livetime = 100 * u.h
exposure = aeff * livetime
mask_safe = RegionNDMap.from_geom(geom=geom, dtype=bool)
mask_safe.data += True
dataset = SpectrumDatasetOnOff(name="test_onoff",
exposure=exposure,
acceptance=acceptance,
acceptance_off=5,
edisp=edisp,
mask_safe=mask_safe)
dataset.models = bkg_model
bkg_npred = dataset.npred_signal()
dataset.models = model
dataset.fake(
random_state=random_state,
npred_background=bkg_npred,
)
return dataset
def get_map_dataset(sky_model,
geom,
geom_etrue,
edisp="edispmap",
name="test",
**kwargs):
"""Returns a MapDatasets"""
# define background model
m = Map.from_geom(geom)
m.quantity = 0.2 * np.ones(m.data.shape)
background_model = BackgroundModel(m, datasets_names=[name])
psf = get_psf()
exposure = get_exposure(geom_etrue)
e_reco = geom.get_axis_by_name("energy")
e_true = geom_etrue.get_axis_by_name("energy_true")
if edisp == "edispmap":
edisp = EDispMap.from_diagonal_response(energy_axis_true=e_true)
elif edisp == "edispkernelmap":
edisp = EDispKernelMap.from_diagonal_response(energy_axis=e_reco,
energy_axis_true=e_true)
elif edisp == "edispkernel":
edisp = EDispKernel.from_diagonal_response(e_true=e_true.edges,
e_reco=e_reco.edges)
else:
edisp = None
# define fit mask
center = sky_model.spatial_model.position
circle = CircleSkyRegion(center=center, radius=1 * u.deg)
mask_fit = background_model.map.geom.region_mask([circle])
mask_fit = Map.from_geom(geom, data=mask_fit)
return MapDataset(models=[sky_model, background_model],
exposure=exposure,
psf=psf,
edisp=edisp,
mask_fit=mask_fit,
name=name,
**kwargs)
def test_edispkernel_from_diagonal_response():
energy_axis_true = MapAxis.from_energy_bounds("0.3 TeV",
"10 TeV",
nbin=11,
name="energy_true")
energy_axis = MapAxis.from_energy_bounds("0.3 TeV",
"10 TeV",
nbin=11,
name="energy")
region = make_region("fk5;circle(0.,0., 10.")
geom = RegionGeom(region)
region_edisp = EDispKernelMap.from_diagonal_response(energy_axis,
energy_axis_true,
geom=geom)
sum_kernel = np.sum(region_edisp.edisp_map.data[..., 0, 0], axis=1)
# We exclude the first and last bin, where there is no
# e_reco to contribute to
assert_allclose(sum_kernel[1:-1], 1)
def spectrum_dataset():
e_true = MapAxis.from_energy_bounds("1 TeV",
"10 TeV",
nbin=20,
name="energy_true")
e_reco = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=4)
aeff = EffectiveAreaTable.from_constant(value=1e6 * u.m**2,
energy=e_true.edges)
background = RegionNDMap.create(region="icrs;circle(0, 0, 0.1)",
axes=[e_reco])
background.data += 3600
background.data[-1] *= 1e-3
edisp = EDispKernelMap.from_diagonal_response(energy_axis_true=e_true,
energy_axis=e_reco,
geom=background.geom)
return SpectrumDataset(aeff=aeff,
livetime="1h",
edisp=edisp,
background=background)
def spectrum_dataset():
e_true = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=20, name="energy_true")
e_reco = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=4)
background = RegionNDMap.create(region="icrs;circle(0, 0, 0.1)", axes=[e_reco])
background.data += 3600
background.data[0] *= 1e3
background.data[-1] *= 1e-3
edisp = EDispKernelMap.from_diagonal_response(
energy_axis_true=e_true, energy_axis=e_reco, geom=background.geom
)
aeff = RegionNDMap.create(region="icrs;circle(0, 0, 0.1)", axes=[e_true], unit="m2")
aeff.data += 1e6
livetime = 1 * u.h
exposure = aeff * livetime
return SpectrumDataset(
name="test", exposure=exposure, edisp=edisp, background=background
)
def get_map_dataset(geom, geom_etrue, edisp="edispmap", name="test", **kwargs):
"""Returns a MapDatasets"""
# define background model
background = Map.from_geom(geom)
background.data += 0.2
psf = get_psf()
exposure = get_exposure(geom_etrue)
e_reco = geom.axes["energy"]
e_true = geom_etrue.axes["energy_true"]
if edisp == "edispmap":
edisp = EDispMap.from_diagonal_response(energy_axis_true=e_true)
elif edisp == "edispkernelmap":
edisp = EDispKernelMap.from_diagonal_response(energy_axis=e_reco,
energy_axis_true=e_true)
elif edisp == "edispkernel":
edisp = EDispKernel.from_diagonal_response(energy_true=e_true.edges,
energy=e_reco.edges)
else:
edisp = None
# define fit mask
center = SkyCoord("0.2 deg", "0.1 deg", frame="galactic")
circle = CircleSkyRegion(center=center, radius=1 * u.deg)
mask_fit = geom.region_mask([circle])
mask_fit = Map.from_geom(geom, data=mask_fit)
models = FoVBackgroundModel(dataset_name=name)
return MapDataset(
models=models,
exposure=exposure,
background=background,
psf=psf,
edisp=edisp,
mask_fit=mask_fit,
name=name,
**kwargs,
)
def fermi_dataset():
size = Angle("3 deg", "3.5 deg")
counts = Map.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts-cube.fits.gz")
counts = counts.cutout(counts.geom.center_skydir, size)
background = Map.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-background-cube.fits.gz"
)
background = background.cutout(background.geom.center_skydir, size)
background = BackgroundModel(background, datasets_names=["fermi-3fhl-gc"])
exposure = Map.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-exposure-cube.fits.gz"
)
exposure = exposure.cutout(exposure.geom.center_skydir, size)
exposure.unit = "cm2 s"
mask_safe = counts.copy(data=np.ones_like(counts.data).astype("bool"))
psf = EnergyDependentTablePSF.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-psf-cube.fits.gz"
)
psfmap = PSFMap.from_energy_dependent_table_psf(psf)
edisp = EDispKernelMap.from_diagonal_response(
energy_axis=counts.geom.get_axis_by_name("energy"),
energy_axis_true=exposure.geom.get_axis_by_name("energy_true"),
)
dataset = MapDataset(
counts=counts,
models=[background],
exposure=exposure,
mask_safe=mask_safe,
psf=psfmap,
name="fermi-3fhl-gc",
edisp=edisp
)
return dataset.to_image()
def setup(self):
etrue = np.logspace(-1, 1, 10) * u.TeV
self.e_true = MapAxis.from_energy_edges(etrue, name="energy_true")
ereco = np.logspace(-1, 1, 5) * u.TeV
elo = ereco[:-1]
ehi = ereco[1:]
self.e_reco = MapAxis.from_energy_edges(ereco, name="energy")
start = u.Quantity([0], "s")
stop = u.Quantity([1000], "s")
time_ref = Time("2010-01-01 00:00:00.0")
self.gti = GTI.create(start, stop, time_ref)
self.livetime = self.gti.time_sum
self.on_region = make_region("icrs;circle(0.,1.,0.1)")
off_region = make_region("icrs;box(0.,1.,0.1, 0.2,30)")
self.off_region = off_region.union(
make_region("icrs;box(-1.,-1.,0.1, 0.2,150)"))
self.wcs = WcsGeom.create(npix=300, binsz=0.01, frame="icrs").wcs
self.aeff = RegionNDMap.create(region=self.on_region,
wcs=self.wcs,
axes=[self.e_true],
unit="cm2")
self.aeff.data += 1
data = np.ones(elo.shape)
data[-1] = 0 # to test stats calculation with empty bins
axis = MapAxis.from_edges(ereco, name="energy", interp="log")
self.on_counts = RegionNDMap.create(
region=self.on_region,
wcs=self.wcs,
axes=[axis],
meta={"EXPOSURE": self.livetime.to_value("s")},
)
self.on_counts.data += 1
self.on_counts.data[-1] = 0
self.off_counts = RegionNDMap.create(region=self.off_region,
wcs=self.wcs,
axes=[axis])
self.off_counts.data += 10
acceptance = RegionNDMap.from_geom(self.on_counts.geom)
acceptance.data += 1
data = np.ones(elo.shape)
data[-1] = 0
acceptance_off = RegionNDMap.from_geom(self.off_counts.geom)
acceptance_off.data += 10
self.edisp = EDispKernelMap.from_diagonal_response(
self.e_reco, self.e_true, self.on_counts.geom.to_image())
exposure = self.aeff * self.livetime
exposure.meta["livetime"] = self.livetime
mask_safe = RegionNDMap.from_geom(self.on_counts.geom, dtype=bool)
mask_safe.data += True
self.dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
exposure=exposure,
edisp=self.edisp,
acceptance=acceptance,
acceptance_off=acceptance_off,
name="test",
gti=self.gti,
mask_safe=mask_safe,
)
def test_spectrum_dataset_stack_diagonal_safe_mask(spectrum_dataset):
geom = spectrum_dataset.counts.geom
energy = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=30)
energy_true = MapAxis.from_energy_bounds("0.1 TeV",
"10 TeV",
nbin=30,
name="energy_true")
aeff = EffectiveAreaTable2D.from_parametrization(
energy_axis_true=energy_true, instrument="HESS")
livetime = 100 * u.s
gti = GTI.create(start=0 * u.s, stop=livetime)
geom_true = geom.as_energy_true
exposure = make_map_exposure_true_energy(geom=geom_true,
livetime=livetime,
pointing=geom_true.center_skydir,
aeff=aeff)
edisp = EDispKernelMap.from_diagonal_response(energy,
energy_true,
geom=geom.to_image())
edisp.exposure_map.data = exposure.data[:, :, np.newaxis, :]
background = spectrum_dataset.background
mask_safe = RegionNDMap.from_geom(geom=geom, dtype=bool)
mask_safe.data += True
spectrum_dataset1 = SpectrumDataset(
name="ds1",
counts=spectrum_dataset.counts.copy(),
exposure=exposure.copy(),
edisp=edisp.copy(),
background=background.copy(),
gti=gti.copy(),
mask_safe=mask_safe,
)
livetime2 = 0.5 * livetime
gti2 = GTI.create(start=200 * u.s, stop=200 * u.s + livetime2)
bkg2 = RegionNDMap.from_geom(geom=geom, data=2 * background.data)
geom = spectrum_dataset.counts.geom
data = np.ones(spectrum_dataset.data_shape, dtype="bool")
data[0] = False
safe_mask2 = RegionNDMap.from_geom(geom=geom, data=data)
exposure2 = exposure.copy()
edisp = edisp.copy()
edisp.exposure_map.data = exposure2.data[:, :, np.newaxis, :]
spectrum_dataset2 = SpectrumDataset(
name="ds2",
counts=spectrum_dataset.counts.copy(),
exposure=exposure2,
edisp=edisp,
background=bkg2,
mask_safe=safe_mask2,
gti=gti2,
)
spectrum_dataset1.stack(spectrum_dataset2)
reference = spectrum_dataset.counts.data
assert_allclose(spectrum_dataset1.counts.data[1:], reference[1:] * 2)
assert_allclose(spectrum_dataset1.counts.data[0], 141363)
assert_allclose(spectrum_dataset1.exposure.quantity[0],
4.755644e09 * u.Unit("cm2 s"))
assert_allclose(spectrum_dataset1.background.data[1:],
3 * background.data[1:])
assert_allclose(spectrum_dataset1.background.data[0], background.data[0])
kernel = edisp.get_edisp_kernel()
kernel_stacked = spectrum_dataset1.edisp.get_edisp_kernel()
assert_allclose(kernel_stacked.pdf_matrix[1:], kernel.pdf_matrix[1:])
assert_allclose(kernel_stacked.pdf_matrix[0], 0.5 * kernel.pdf_matrix[0])
def test_spectrum_dataset_stack_diagonal_safe_mask(spectrum_dataset):
geom = spectrum_dataset.counts.geom
energy = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=30)
energy_true = MapAxis.from_energy_bounds("0.1 TeV",
"10 TeV",
nbin=30,
name="energy_true")
aeff = EffectiveAreaTable.from_parametrization(
energy.edges, "HESS").to_region_map(geom.region)
livetime = 100 * u.s
gti = GTI.create(start=0 * u.s, stop=livetime)
exposure = aeff * livetime
edisp = EDispKernelMap.from_diagonal_response(energy,
energy_true,
geom=geom.to_image())
edisp.exposure_map.data = exposure.data[:, :, np.newaxis, :]
background = spectrum_dataset.background_model.map.copy()
spectrum_dataset1 = SpectrumDataset(name="ds1",
counts=spectrum_dataset.counts.copy(),
exposure=exposure.copy(),
edisp=edisp.copy(),
models=BackgroundModel(
background,
name="ds1-bkg",
datasets_names=["ds1"]),
gti=gti.copy())
livetime2 = 0.5 * livetime
gti2 = GTI.create(start=200 * u.s, stop=200 * u.s + livetime2)
aeff2 = aeff * 2
bkg2 = RegionNDMap.from_geom(geom=geom, data=2 * background.data)
geom = spectrum_dataset.counts.geom
data = np.ones(spectrum_dataset.data_shape, dtype="bool")
data[0] = False
safe_mask2 = RegionNDMap.from_geom(geom=geom, data=data)
exposure2 = aeff2 * livetime2
edisp = edisp.copy()
edisp.exposure_map.data = exposure2.data[:, :, np.newaxis, :]
spectrum_dataset2 = SpectrumDataset(name="ds2",
counts=spectrum_dataset.counts.copy(),
exposure=exposure2,
edisp=edisp,
models=BackgroundModel(
bkg2,
name="ds2-bkg",
datasets_names=["ds2"]),
mask_safe=safe_mask2,
gti=gti2)
spectrum_dataset1.stack(spectrum_dataset2)
reference = spectrum_dataset.counts.data
assert_allclose(spectrum_dataset1.counts.data[1:], reference[1:] * 2)
assert_allclose(spectrum_dataset1.counts.data[0], 141363)
assert_allclose(spectrum_dataset1.exposure.data[0], 4.755644e+09)
assert_allclose(spectrum_dataset1.background_model.map.data[1:],
3 * background.data[1:])
assert_allclose(spectrum_dataset1.background_model.map.data[0],
background.data[0])
assert_allclose(
spectrum_dataset1.exposure.quantity.to_value("m2s"),
2 * (aeff * livetime).quantity.to_value("m2s"),
)
kernel = edisp.get_edisp_kernel()
kernel_stacked = spectrum_dataset1.edisp.get_edisp_kernel()
assert_allclose(kernel_stacked.pdf_matrix[1:], kernel.pdf_matrix[1:])
assert_allclose(kernel_stacked.pdf_matrix[0], 0.5 * kernel.pdf_matrix[0])
def test_stack(sky_model):
axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=3)
geom = WcsGeom.create(
skydir=(266.40498829, -28.93617776),
binsz=0.05,
width=(2, 2),
frame="icrs",
axes=[axis],
)
axis_etrue = MapAxis.from_energy_bounds(
"0.1 TeV", "10 TeV", nbin=5, name="energy_true"
)
geom_etrue = WcsGeom.create(
skydir=(266.40498829, -28.93617776),
binsz=0.05,
width=(2, 2),
frame="icrs",
axes=[axis_etrue],
)
edisp = EDispKernelMap.from_diagonal_response(
energy_axis=axis, energy_axis_true=axis_etrue, geom=geom
)
edisp.exposure_map.quantity = (
1e0 * u.m ** 2 * u.s * np.ones(edisp.exposure_map.data.shape)
)
bkg1 = Map.from_geom(geom)
bkg1.data += 0.2
cnt1 = Map.from_geom(geom)
cnt1.data = 1.0 * np.ones(cnt1.data.shape)
exp1 = Map.from_geom(geom_etrue)
exp1.quantity = 1e7 * u.m ** 2 * u.s * np.ones(exp1.data.shape)
mask1 = Map.from_geom(geom)
mask1.data = np.ones(mask1.data.shape, dtype=bool)
mask1.data[0][:][5:10] = False
dataset1 = MapDataset(
counts=cnt1,
background=bkg1,
exposure=exp1,
mask_safe=mask1,
name="dataset-1",
edisp=edisp,
meta_table=Table({"OBS_ID": [0]}),
)
bkg2 = Map.from_geom(geom)
bkg2.data = 0.1 * np.ones(bkg2.data.shape)
cnt2 = Map.from_geom(geom)
cnt2.data = 1.0 * np.ones(cnt2.data.shape)
exp2 = Map.from_geom(geom_etrue)
exp2.quantity = 1e7 * u.m ** 2 * u.s * np.ones(exp2.data.shape)
mask2 = Map.from_geom(geom)
mask2.data = np.ones(mask2.data.shape, dtype=bool)
mask2.data[0][:][5:10] = False
mask2.data[1][:][10:15] = False
dataset2 = MapDataset(
counts=cnt2,
background=bkg2,
exposure=exp2,
mask_safe=mask2,
name="dataset-2",
edisp=edisp,
meta_table=Table({"OBS_ID": [1]}),
)
background_model2 = FoVBackgroundModel(dataset_name="dataset-2")
background_model1 = FoVBackgroundModel(dataset_name="dataset-1")
dataset1.models = [background_model1, sky_model]
dataset2.models = [background_model2, sky_model]
dataset1.stack(dataset2)
dataset1.models = [sky_model]
npred_b = dataset1.npred()
assert_allclose(npred_b.data.sum(), 1459.985035, 1e-5)
assert_allclose(dataset1.npred_background().data.sum(), 1360.00, 1e-5)
assert_allclose(dataset1.counts.data.sum(), 9000, 1e-5)
assert_allclose(dataset1.mask_safe.data.sum(), 4600)
assert_allclose(dataset1.exposure.data.sum(), 1.6e11)
assert_allclose(dataset1.meta_table["OBS_ID"][0], [0, 1])
def create(
cls,
e_reco,
e_true=None,
region=None,
reference_time="2000-01-01",
name=None,
meta_table=None,
):
"""Creates empty spectrum dataset.
Empty containers are created with the correct geometry.
counts, background and aeff are zero and edisp is diagonal.
The safe_mask is set to False in every bin.
Parameters
----------
e_reco : `~gammapy.maps.MapAxis`
counts energy axis. Its name must be "energy".
e_true : `~gammapy.maps.MapAxis`
effective area table energy axis. Its name must be "energy-true".
If not set use reco energy values. Default : None
region : `~regions.SkyRegion`
Region to define the dataset for.
reference_time : `~astropy.time.Time`
reference time of the dataset, Default is "2000-01-01"
meta_table : `~astropy.table.Table`
Table listing informations on observations used to create the dataset.
One line per observation for stacked datasets.
"""
if e_true is None:
e_true = e_reco.copy(name="energy_true")
if region is None:
region = "icrs;circle(0, 0, 1)"
name = make_name(name)
counts = RegionNDMap.create(region=region, axes=[e_reco])
background = RegionNDMap.create(region=region, axes=[e_reco])
models = Models([
BackgroundModel(background,
name=name + "-bkg",
datasets_names=[name])
])
exposure = RegionNDMap.create(region=region,
axes=[e_true],
unit="cm2 s")
edisp = EDispKernelMap.from_diagonal_response(e_reco,
e_true,
geom=counts.geom)
mask_safe = RegionNDMap.from_geom(counts.geom, dtype="bool")
gti = GTI.create(u.Quantity([], "s"), u.Quantity([], "s"),
reference_time)
return SpectrumDataset(
counts=counts,
exposure=exposure,
edisp=edisp,
mask_safe=mask_safe,
gti=gti,
models=models,
name=name,
)
def setup(self):
etrue = np.logspace(-1, 1, 10) * u.TeV
self.e_true = MapAxis.from_edges(etrue, name="energy_true")
ereco = np.logspace(-1, 1, 5) * u.TeV
elo = ereco[:-1]
ehi = ereco[1:]
self.e_reco = MapAxis.from_edges(ereco, name="energy")
self.aeff = EffectiveAreaTable(etrue[:-1], etrue[1:],
np.ones(9) * u.cm**2)
start = u.Quantity([0], "s")
stop = u.Quantity([1000], "s")
time_ref = Time("2010-01-01 00:00:00.0")
self.gti = GTI.create(start, stop, time_ref)
self.livetime = self.gti.time_sum
self.on_region = make_region("icrs;circle(0.,1.,0.1)")
off_region = make_region("icrs;box(0.,1.,0.1, 0.2,30)")
self.off_region = off_region.union(
make_region("icrs;box(-1.,-1.,0.1, 0.2,150)"))
self.wcs = WcsGeom.create(npix=300, binsz=0.01, frame="icrs").wcs
data = np.ones(elo.shape)
data[-1] = 0 # to test stats calculation with empty bins
axis = MapAxis.from_edges(ereco, name="energy", interp="log")
self.on_counts = RegionNDMap.create(region=self.on_region,
wcs=self.wcs,
axes=[axis])
self.on_counts.data += 1
self.on_counts.data[-1] = 0
self.off_counts = RegionNDMap.create(region=self.off_region,
wcs=self.wcs,
axes=[axis])
self.off_counts.data += 10
acceptance = RegionNDMap.from_geom(self.on_counts.geom)
acceptance.data += 1
data = np.ones(elo.shape)
data[-1] = 0
acceptance_off = RegionNDMap.from_geom(self.off_counts.geom)
acceptance_off.data += 10
self.edisp = EDispKernelMap.from_diagonal_response(
self.e_reco, self.aeff.data.axis("energy_true"),
self.on_counts.geom)
self.dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
aeff=self.aeff,
edisp=self.edisp,
livetime=self.livetime,
acceptance=acceptance,
acceptance_off=acceptance_off,
name="test",
gti=self.gti,
)
def create(
cls,
e_reco,
e_true=None,
region=None,
reference_time="2000-01-01",
name=None,
meta_table=None,
):
"""Creates empty spectrum dataset.
Empty containers are created with the correct geometry.
counts, background and aeff are zero and edisp is diagonal.
The safe_mask is set to False in every bin.
Parameters
----------
e_reco : `~gammapy.maps.MapAxis`
counts energy axis. Its name must be "energy".
e_true : `~gammapy.maps.MapAxis`
effective area table energy axis. Its name must be "energy-true".
If not set use reco energy values. Default : None
region : `~regions.SkyRegion`
Region to define the dataset for.
reference_time : `~astropy.time.Time`
reference time of the dataset, Default is "2000-01-01"
meta_table : `~astropy.table.Table`
Table listing informations on observations used to create the dataset.
One line per observation for stacked datasets.
"""
if e_true is None:
e_true = e_reco.copy(name="energy_true")
if region is None:
region = "icrs;circle(0, 0, 1)"
counts = RegionNDMap.create(region=region, axes=[e_reco])
background = RegionNDMap.create(region=region, axes=[e_reco])
aeff = EffectiveAreaTable(
e_true.edges[:-1],
e_true.edges[1:],
np.zeros(e_true.edges[:-1].shape) * u.m**2,
)
edisp = EDispKernelMap.from_diagonal_response(e_reco, e_true,
counts.geom)
mask_safe = RegionNDMap.from_geom(counts.geom, dtype="bool")
gti = GTI.create(u.Quantity([], "s"), u.Quantity([], "s"),
reference_time)
livetime = gti.time_sum
return SpectrumDataset(
counts=counts,
aeff=aeff,
edisp=edisp,
mask_safe=mask_safe,
background=background,
livetime=livetime,
gti=gti,
name=name,
)
def run_region(self, kr, lon, lat, radius):
# TODO: for now we have to read/create the allsky maps each in each job
# because we can't pickle <functools._lru_cache_wrapper object
# send this back to init when fixed
log.info(f"ROI {kr}: loading data")
# exposure
exposure_hpx = Map.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz")
exposure_hpx.unit = "cm2 s"
# psf
psf_map = PSFMap.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz",
format="gtpsf")
# reduce size of the PSF
axis = psf_map.psf_map.geom.axes["rad"].center.to_value(u.deg)
indmax = np.argmin(np.abs(self.psf_margin - axis))
psf_map = psf_map.slice_by_idx(slices={"rad": slice(0, indmax)})
# iem
iem_filepath = BASE_PATH / "data" / "gll_iem_v06_extrapolated.fits"
iem_fermi_extra = Map.read(iem_filepath)
# norm=1.1, tilt=0.03 see paper appendix A
model_iem = SkyModel(
PowerLawNormSpectralModel(norm=1.1, tilt=0.03),
TemplateSpatialModel(iem_fermi_extra, normalize=False),
name="iem_extrapolated",
)
# ROI
roi_time = time()
ROI_pos = SkyCoord(lon, lat, frame="galactic", unit="deg")
width = 2 * (radius + self.psf_margin)
# Counts
counts = Map.create(
skydir=ROI_pos,
width=width,
proj="CAR",
frame="galactic",
binsz=1 / 8.0,
axes=[self.energy_axis],
dtype=float,
)
counts.fill_by_coord({
"skycoord": self.events.radec,
"energy": self.events.energy
})
axis = MapAxis.from_nodes(counts.geom.axes[0].center,
name="energy_true",
unit="GeV",
interp="log")
wcs = counts.geom.wcs
geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis])
coords = geom.get_coord()
# expo
data = exposure_hpx.interp_by_coord(coords)
exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit, dtype=float)
# Energy Dispersion
edisp = EDispKernelMap.from_diagonal_response(
energy_axis_true=axis, energy_axis=self.energy_axis)
# fit mask
if coords["lon"].min() < 90 * u.deg and coords["lon"].max(
) > 270 * u.deg:
coords["lon"][coords["lon"].value > 180] -= 360 * u.deg
mask = (
(coords["lon"] >= coords["lon"].min() + self.psf_margin * u.deg)
& (coords["lon"] <= coords["lon"].max() - self.psf_margin * u.deg)
& (coords["lat"] >= coords["lat"].min() + self.psf_margin * u.deg)
& (coords["lat"] <= coords["lat"].max() - self.psf_margin * u.deg))
mask_fermi = WcsNDMap(counts.geom, mask)
mask_safe_fermi = WcsNDMap(counts.geom, np.ones(mask.shape,
dtype=bool))
log.info(f"ROI {kr}: pre-computing diffuse")
# IEM
eval_iem = MapEvaluator(
model=model_iem,
exposure=exposure,
psf=psf_map.get_psf_kernel(geom),
edisp=edisp.get_edisp_kernel(),
)
bkg_iem = eval_iem.compute_npred()
# ISO
eval_iso = MapEvaluator(model=self.model_iso,
exposure=exposure,
edisp=edisp.get_edisp_kernel())
bkg_iso = eval_iso.compute_npred()
# merge iem and iso, only one local normalization is fitted
dataset_name = "3FHL_ROI_num" + str(kr)
background_total = bkg_iem + bkg_iso
# Dataset
dataset = MapDataset(
counts=counts,
exposure=exposure,
background=background_total,
psf=psf_map,
edisp=edisp,
mask_fit=mask_fermi,
mask_safe=mask_safe_fermi,
name=dataset_name,
)
background_model = FoVBackgroundModel(dataset_name=dataset_name)
background_model.parameters["norm"].min = 0.0
# Sources model
in_roi = self.FHL3.positions.galactic.contained_by(wcs)
FHL3_roi = []
for ks in range(len(self.FHL3.table)):
if in_roi[ks] == True:
model = self.FHL3[ks].sky_model()
model.spatial_model.parameters.freeze_all() # freeze spatial
model.spectral_model.parameters["amplitude"].min = 0.0
if isinstance(model.spectral_model, PowerLawSpectralModel):
model.spectral_model.parameters["index"].min = 0.1
model.spectral_model.parameters["index"].max = 10.0
else:
model.spectral_model.parameters["alpha"].min = 0.1
model.spectral_model.parameters["alpha"].max = 10.0
FHL3_roi.append(model)
model_total = Models(FHL3_roi + [background_model])
dataset.models = model_total
cat_stat = dataset.stat_sum()
datasets = Datasets([dataset])
log.info(f"ROI {kr}: running fit")
fit = Fit(**self.fit_opts)
results = fit.run(datasets=datasets)
print("ROI_num", str(kr), "\n", results)
fit_stat = datasets.stat_sum()
if results.message != "Optimization failed.":
filedata = Path(self.resdir) / f"3FHL_ROI_num{kr}_datasets.yaml"
filemodel = Path(self.resdir) / f"3FHL_ROI_num{kr}_models.yaml"
datasets.write(filedata, filemodel, overwrite=True)
np.savez(
self.resdir / f"3FHL_ROI_num{kr}_fit_infos.npz",
message=results.message,
stat=[cat_stat, fit_stat],
)
exec_time = time() - roi_time
print("ROI", kr, " time (s): ", exec_time)
log.info(f"ROI {kr}: running flux points")
for model in FHL3_roi:
if (self.FHL3[model.name].data["ROI_num"] == kr
and self.FHL3[model.name].data["Signif_Avg"] >=
self.sig_cut):
print(model.name)
flux_points = FluxPointsEstimator(
energy_edges=self.El_flux,
source=model.name,
n_sigma_ul=2,
selection_optional=["ul"],
).run(datasets=datasets)
flux_points.meta["sqrt_ts_threshold_ul"] = 1
filename = self.resdir / f"{model.name}_flux_points.fits"
flux_points.write(filename, overwrite=True)
exec_time = time() - roi_time - exec_time
print("ROI", kr, " Flux points time (s): ", exec_time)