def test_sky_point_source():
# Test special case of point source. Regression test for GH 2367.
energy_axis = MapAxis.from_edges(
[1, 10], unit="TeV", name="energy_true", interp="log"
)
exposure = Map.create(
skydir=(100, 70),
npix=(4, 4),
binsz=0.1,
proj="AIT",
unit="cm2 s",
axes=[energy_axis],
)
exposure.data = np.ones_like(exposure.data)
spatial_model = PointSpatialModel(
lon_0=100.06 * u.deg, lat_0=70.03 * u.deg, frame="icrs"
)
# Create a spectral model with integral flux of 1 cm-2 s-1 in this energy band
spectral_model = ConstantSpectralModel(const="1 cm-2 s-1 TeV-1")
spectral_model.const.value /= spectral_model.integral(1 * u.TeV, 10 * u.TeV).value
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
evaluator = MapEvaluator(model=model, exposure=exposure)
flux = evaluator.compute_flux().quantity.to_value("cm-2 s-1")[0]
expected = [
[0, 0, 0, 0],
[0, 0.140, 0.058, 0.0],
[0, 0.564, 0.236, 0],
[0, 0, 0, 0],
]
assert_allclose(flux, expected, atol=0.01)
assert_allclose(flux.sum(), 1)
def test_compute_flux_spatial_no_psf():
# check that spatial integration is not performed in the absence of a psf
center = SkyCoord("0 deg", "0 deg", frame="galactic")
region = CircleSkyRegion(center=center, radius=0.1 * u.deg)
nbin = 2
energy_axis_true = MapAxis.from_energy_bounds(".1 TeV",
"10 TeV",
nbin=nbin,
name="energy_true")
spectral_model = ConstantSpectralModel()
spatial_model = GaussianSpatialModel(lon_0=0 * u.deg,
lat_0=0 * u.deg,
frame="galactic",
sigma="0.1 deg")
models = SkyModel(spectral_model=spectral_model,
spatial_model=spatial_model)
model = Models(models)
exposure_region = RegionNDMap.create(region, axes=[energy_axis_true])
exposure_region.data += 1.0
exposure_region.unit = "m2 s"
evaluator = MapEvaluator(model=model[0], exposure=exposure_region)
flux = evaluator.compute_flux_spatial()
assert_allclose(flux, 1.0)
def test_compute_flux_spatial():
center = SkyCoord("0 deg", "0 deg", frame="galactic")
region = CircleSkyRegion(center=center, radius=0.1 * u.deg)
nbin = 2
energy_axis_true = MapAxis.from_energy_bounds(".1 TeV",
"10 TeV",
nbin=nbin,
name="energy_true")
spectral_model = ConstantSpectralModel()
spatial_model = PointSpatialModel(lon_0=0 * u.deg,
lat_0=0 * u.deg,
frame="galactic")
models = SkyModel(spectral_model=spectral_model,
spatial_model=spatial_model)
model = Models(models)
exposure_region = RegionNDMap.create(region,
axes=[energy_axis_true],
binsz_wcs="0.01deg")
exposure_region.data += 1.0
exposure_region.unit = "m2 s"
geom = RegionGeom(region, axes=[energy_axis_true], binsz_wcs="0.01deg")
psf = PSFKernel.from_gauss(geom.to_wcs_geom(), sigma="0.1 deg")
evaluator = MapEvaluator(model=model[0], exposure=exposure_region, psf=psf)
flux = evaluator.compute_flux_spatial()
g = Gauss2DPDF(0.1)
reference = g.containment_fraction(0.1)
assert_allclose(flux.value, reference, rtol=0.003)
def test_map_spectrum_weight():
axis = MapAxis.from_edges([0.1, 10, 1000], unit="TeV", name="energy")
expo_map = WcsNDMap.create(npix=10, binsz=1, axes=[axis], unit="m2 s")
expo_map.data += 1
spectrum = ConstantSpectralModel(const="42 cm-2 s-1 TeV-1")
weighted_expo = _map_spectrum_weight(expo_map, spectrum)
assert weighted_expo.data.shape == (2, 10, 10)
assert weighted_expo.unit == "m2 s"
assert_allclose(weighted_expo.data.sum(), 100)
def test_npred_no_edisp(self):
const = 1 / u.TeV / u.cm**2 / u.s
model = ConstantSpectralModel(const)
livetime = 1 * u.s
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
aeff=self.aeff,
model=model,
livetime=livetime,
)
energy = self.aeff.energy.edges * self.aeff.energy.unit
expected = self.aeff.data.data[0] * (energy[-1] -
energy[0]) * const * livetime
assert_allclose(dataset.npred_sig().data.sum(), expected.value)
def test_npred_no_edisp(self):
const = 1 * u.Unit("cm-2 s-1 TeV-1")
model = SkyModel(spectral_model=ConstantSpectralModel(const=const))
livetime = 1 * u.s
e_reco = self.on_counts.geom.axes[0].edges
aeff = EffectiveAreaTable(e_reco[:-1], e_reco[1:], np.ones(4) * u.cm ** 2)
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
aeff=aeff,
models=model,
livetime=livetime,
)
energy = aeff.energy.edges
expected = aeff.data.data[0] * (energy[-1] - energy[0]) * const * livetime
assert_allclose(dataset.npred_sig().data.sum(), expected.value)
def test_npred_no_edisp(self):
const = 1 * u.Unit("cm-2 s-1 TeV-1")
model = SkyModel(spectral_model=ConstantSpectralModel(const=const))
livetime = 1 * u.s
aeff = RegionNDMap.create(region=self.on_region,
unit="cm2",
axes=[self.e_reco.copy(name="energy_true")])
aeff.data += 1
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
aeff=aeff,
models=model,
livetime=livetime,
)
energy = aeff.geom.axes[0].edges
expected = aeff.data[0] * (energy[-1] - energy[0]) * const * livetime
assert_allclose(dataset.npred_sig().data.sum(), expected.value)
def test_large_oversampling():
nbin = 2
energy_axis_true = MapAxis.from_energy_bounds(".1 TeV",
"10 TeV",
nbin=nbin,
name="energy_true")
geom = WcsGeom.create(width=1, binsz=0.02, axes=[energy_axis_true])
spectral_model = ConstantSpectralModel()
spatial_model = GaussianSpatialModel(lon_0=0 * u.deg,
lat_0=0 * u.deg,
sigma=1e-4 * u.deg,
frame="icrs")
models = SkyModel(spectral_model=spectral_model,
spatial_model=spatial_model)
model = Models(models)
exposure = Map.from_geom(geom, unit="m2 s")
exposure.data += 1.0
psf = PSFKernel.from_gauss(geom, sigma="0.1 deg")
evaluator = MapEvaluator(model=model[0], exposure=exposure, psf=psf)
flux_1 = evaluator.compute_flux_spatial()
spatial_model.sigma.value = 0.001
flux_2 = evaluator.compute_flux_spatial()
spatial_model.sigma.value = 0.01
flux_3 = evaluator.compute_flux_spatial()
spatial_model.sigma.value = 0.03
flux_4 = evaluator.compute_flux_spatial()
assert_allclose(flux_1.data.sum(), nbin, rtol=1e-4)
assert_allclose(flux_2.data.sum(), nbin, rtol=1e-4)
assert_allclose(flux_3.data.sum(), nbin, rtol=1e-4)
assert_allclose(flux_4.data.sum(), nbin, rtol=1e-4)
dict(
name="logpar10",
model=LogParabolaSpectralModel.from_log10(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=1.151292546497023 * u.Unit(""),
),
val_at_2TeV=u.Quantity(0.6387956571420305, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.255689748270628, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.9586515834989267, "TeV cm-2 s-1"),
e_peak=0.74082 * u.TeV,
),
dict(
name="constant",
model=ConstantSpectralModel(const=4 / u.cm ** 2 / u.s / u.TeV),
val_at_2TeV=u.Quantity(4, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(35.9999999999999, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(198.00000000000006, "TeV cm-2 s-1"),
),
dict(
name="powerlaw_index1",
model=PowerLawSpectralModel(
index=1 * u.Unit(""),
amplitude=2 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
),
val_at_2TeV=u.Quantity(1.0, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(4.605170185, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(18.0, "TeV cm-2 s-1"),
),
=======================
This model takes a constant value along the spectral range.
.. math:: \phi(E) = k
"""
# %%
# Example plot
# ------------
# Here is an example plot of the model:
from astropy import units as u
import matplotlib.pyplot as plt
from gammapy.modeling.models import ConstantSpectralModel, Models, SkyModel
energy_bounds = [0.1, 100] * u.TeV
model = ConstantSpectralModel(const="1 / (cm2 s TeV)")
model.plot(energy_bounds)
plt.grid(which="both")
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
model = SkyModel(spectral_model=model, name="constant-model")
models = Models([model])
print(models.to_yaml())
=======================
This model takes a constant value along the spectral range.
.. math:: \phi(E) = k
"""
# %%
# Example plot
# ------------
# Here is an example plot of the model:
from astropy import units as u
import matplotlib.pyplot as plt
from gammapy.modeling.models import ConstantSpectralModel, Models, SkyModel
energy_range = [0.1, 100] * u.TeV
model = ConstantSpectralModel(const="1 / (cm2 s TeV)")
model.plot(energy_range)
plt.grid(which="both")
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
model = SkyModel(spectral_model=model, name="constant-model")
models = Models([model])
print(models.to_yaml())
