def test_spectrum_dataset_stack_nondiagonal_no_bkg(self):
aeff = EffectiveAreaTable.from_parametrization(self.src.energy.edges,
"HESS")
edisp1 = EDispKernel.from_gauss(self.src.energy.edges,
self.src.energy.edges, 0.1, 0.0)
livetime = self.livetime
dataset1 = SpectrumDataset(counts=None,
livetime=livetime,
aeff=aeff,
edisp=edisp1,
background=None)
livetime2 = livetime
aeff2 = EffectiveAreaTable(self.src.energy.edges[:-1],
self.src.energy.edges[1:], aeff.data.data)
edisp2 = EDispKernel.from_gauss(self.src.energy.edges,
self.src.energy.edges, 0.2, 0.0)
dataset2 = SpectrumDataset(
counts=self.src.copy(),
livetime=livetime2,
aeff=aeff2,
edisp=edisp2,
background=None,
)
dataset1.stack(dataset2)
assert dataset1.counts is None
assert dataset1.background is None
assert dataset1.livetime == 2 * self.livetime
assert_allclose(dataset1.aeff.data.data.to_value("m2"),
aeff.data.data.to_value("m2"))
assert_allclose(dataset1.edisp.get_bias(1 * u.TeV), 0.0, atol=1.2e-3)
assert_allclose(dataset1.edisp.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].edges
aeff = EffectiveAreaTable.from_parametrization(energy, "HESS")
edisp1 = EDispKernel.from_gauss(energy, energy, 0.1, 0)
livetime = 100 * u.s
spectrum_dataset1 = SpectrumDataset(
counts=spectrum_dataset.counts.copy(),
livetime=livetime, aeff=aeff, edisp=edisp1,
)
livetime2 = livetime
aeff2 = EffectiveAreaTable(
energy[:-1], energy[1:], aeff.data.data
)
edisp2 = EDispKernel.from_gauss(energy, energy, 0.2, 0.0)
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")
)
assert_allclose(spectrum_dataset1.edisp.get_bias(1 * u.TeV), 0.0, atol=1.2e-3)
assert_allclose(spectrum_dataset1.edisp.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
on_vector = CountsSpectrum(energy_lo=energy[:-1],
energy_hi=energy[1:],
data=data_on)
off_vector1 = CountsSpectrum(energy_lo=energy[:-1],
energy_hi=energy[1:],
data=dataoff_1)
off_vector2 = CountsSpectrum(energy_lo=energy[:-1],
energy_hi=energy[1:],
data=dataoff_2)
aeff = EffectiveAreaTable.from_constant(energy, "1 cm2")
edisp = EDispKernel.from_gauss(e_true=energy,
e_reco=energy,
sigma=0.2,
bias=0)
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)
obs1 = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=off_vector1,
aeff=aeff,
edisp=edisp,
livetime=livetime,
mask_safe=np.ones(on_vector.energy.nbin, dtype=bool),
acceptance=1,
acceptance_off=2,
name="1",
gti=gti1,
)
obs2 = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=off_vector2,
aeff=aeff,
edisp=edisp,
livetime=livetime,
mask_safe=np.ones(on_vector.energy.nbin, dtype=bool),
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"""
dataset = MapDataset.create(geom, binsz_irf=1.0)
dataset.counts.fill_events(self.events)
dataset.gti = self._make_gti()
self._fill_psfmap(self.psf, dataset)
# recompute exposure on geom
coords = geom.get_coord()
# this is to change the axis name. Can we avoid this?
coords = MapCoord.create(
dict(skycoord=coords.skycoord, energy_true=coords['energy']))
values = self.exposure.interp_by_coord(coords)
dataset.exposure = Map.from_geom(geom,
data=values,
unit=self.exposure.unit)
# Not the real Fermi-LAT EDISP: Use 5% energy resolution as approximation
energy = geom.axes[0]
edisp = EDispKernel.from_gauss(e_true=energy.edges,
e_reco=energy.edges,
sigma=0.05,
bias=0)
dataset.edisp = edisp
return dataset
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")
geom = RegionGeom(region=None, axes=[axis])
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 = EffectiveAreaTable.from_constant(energy, "1 cm2")
edisp = EDispKernel.from_gauss(e_true=energy, e_reco=energy, sigma=0.2, bias=0)
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)
obs1 = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=off_vector1,
aeff=aeff,
edisp=edisp,
livetime=livetime,
mask_safe=mask_safe,
acceptance=1,
acceptance_off=2,
name="1",
gti=gti1,
)
obs2 = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=off_vector2,
aeff=aeff,
edisp=edisp,
livetime=livetime,
mask_safe=mask_safe,
acceptance=1,
acceptance_off=4,
name="2",
gti=gti2,
)
obs_list = [obs1, obs2]
return obs_list
def setup(self):
self.e_true = np.logspace(0, 1, 101) * u.TeV
self.e_reco = self.e_true
self.resolution = 0.1
self.bias = 0
self.edisp = EDispKernel.from_gauss(
e_true=self.e_true,
e_reco=self.e_reco,
pdf_threshold=1e-7,
sigma=self.resolution,
bias=self.bias,
)
def setup(self):
energy_axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=100)
energy_axis_true = energy_axis.copy(name="energy_true")
self.resolution = 0.1
self.bias = 0
self.edisp = EDispKernel.from_gauss(
energy_axis_true=energy_axis_true,
energy_axis=energy_axis,
pdf_threshold=1e-7,
sigma=self.resolution,
bias=self.bias,
)
def test_to_image(self):
e_reco = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=3)
e_true = MapAxis.from_energy_bounds(
"0.08 TeV", "20 TeV", nbin=5, name="energy_true"
)
edisp = EDispKernel.from_gauss(
energy=e_reco.edges, energy_true=e_true.edges, sigma=0.2, bias=0.1
)
im = edisp.to_image()
assert im.pdf_matrix.shape == (5, 1)
assert_allclose(
im.pdf_matrix, [[0.97142], [1.0], [1.0], [1.0], [0.12349]], rtol=1e-3
)
assert_allclose(im.energy_axis.edges, [0.1, 10] * u.TeV)
def test_edispkernel_from_1D():
energy_axis_true = MapAxis.from_energy_bounds(
"0.5 TeV", "5 TeV", nbin=31, name="energy_true"
)
energy_axis = MapAxis.from_energy_bounds(
"0.1 TeV", "10 TeV", nbin=11, name="energy"
)
edisp = EDispKernel.from_gauss(energy_axis_true.edges, energy_axis.edges, 0.1, 0.0)
region = make_region("fk5;circle(0.,0., 10.")
geom = RegionGeom(region)
region_edisp = EDispKernelMap.from_edisp_kernel(edisp, geom=geom)
sum_kernel = np.sum(region_edisp.edisp_map.data[..., 0, 0], axis=1)
assert_allclose(sum_kernel, 1, rtol=1e-5)
allsky_edisp = EDispKernelMap.from_edisp_kernel(edisp)
sum_kernel = np.sum(allsky_edisp.edisp_map.data[..., 0, 0], axis=1)
assert allsky_edisp.edisp_map.data.shape == (31, 11, 1, 2)
assert_allclose(sum_kernel, 1, rtol=1e-5)
def get_test_cases():
e_true = Quantity(np.logspace(-1, 2, 120), "TeV")
e_reco = Quantity(np.logspace(-1, 2, 100), "TeV")
return [
dict(
model=SkyModel(spectral_model=PowerLawSpectralModel(
amplitude="1e-11 TeV-1 cm-2 s-1")),
aeff=EffectiveAreaTable.from_parametrization(e_true),
livetime="10 h",
npred=1448.05960,
),
dict(
model=SkyModel(spectral_model=PowerLawSpectralModel(
reference="1 GeV", amplitude="1e-11 GeV-1 cm-2 s-1")),
aeff=EffectiveAreaTable.from_parametrization(e_true),
livetime="30 h",
npred=4.34417881,
),
dict(
model=SkyModel(spectral_model=PowerLawSpectralModel(
amplitude="1e-11 TeV-1 cm-2 s-1")),
aeff=EffectiveAreaTable.from_parametrization(e_true),
edisp=EDispKernel.from_gauss(e_reco=e_reco,
e_true=e_true,
bias=0,
sigma=0.2),
livetime="10 h",
npred=1437.494815,
),
dict(
model=SkyModel(spectral_model=TemplateSpectralModel(
energy=[0.1, 0.2, 0.3, 0.4] * u.TeV,
values=[4.0, 3.0, 1.0, 0.1] * u.Unit("TeV-1"),
)),
aeff=EffectiveAreaTable.from_constant([0.1, 0.2, 0.3, 0.4] * u.TeV,
1),
npred=0.554513062,
),
]
"""Plot an energy dispersion using a gaussian parametrisation"""
import matplotlib.pyplot as plt
from gammapy.irf import EDispKernel
from gammapy.maps import MapAxis
energy_axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=10)
energy_axis_true = MapAxis.from_energy_bounds(
"0.5 TeV", "30 TeV", nbin=10, per_decade=True, name="energy_true"
)
edisp = EDispKernel.from_gauss(
energy_axis=energy_axis, energy_axis_true=energy_axis_true, sigma=0.1, bias=0
)
edisp.peek()
plt.show()
"""Plot energy dispersion example."""
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
from gammapy.irf import EDispKernel
ebounds = np.logspace(-1, 2, 101) * u.TeV
edisp = EDispKernel.from_gauss(e_true=ebounds,
e_reco=ebounds,
bias=0,
sigma=0.3)
edisp.peek()
plt.show()
"""Plot an energy dispersion using a gaussian parametrisation"""
import numpy as np
import astropy.units as u
from gammapy.irf import EDispKernel
import matplotlib.pyplot as plt
energy = np.logspace(0, 1, 101) * u.TeV
edisp = EDispKernel.from_gauss(
e_true=energy,
e_reco=energy,
sigma=0.1,
bias=0,
)
edisp.peek()
plt.show()
"""Plot an energy dispersion using a gaussian parametrisation"""
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
from gammapy.irf import EDispKernel
energy = np.logspace(0, 1, 101) * u.TeV
edisp = EDispKernel.from_gauss(
energy=energy,
energy_true=energy,
sigma=0.1,
bias=0,
)
edisp.peek()
plt.show()
