class TestSpectrumOnOff:
""" Test ON OFF SpectrumDataset"""
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_on_off_create(self):
e_reco = MapAxis.from_edges(u.Quantity([0.1, 1, 10.0], "TeV"),
name="energy")
e_true = MapAxis.from_edges(u.Quantity([0.05, 0.5, 5, 20.0], "TeV"),
name="energy_true")
geom = RegionGeom(region=None, axes=[e_reco])
empty_dataset = SpectrumDatasetOnOff.create(geom=geom,
energy_axis_true=e_true)
assert empty_dataset.counts.data.sum() == 0
assert empty_dataset.data_shape[0] == 2
assert empty_dataset.counts_off.data.sum() == 0
assert empty_dataset.counts_off.geom.axes[0].nbin == 2
assert_allclose(empty_dataset.acceptance_off, 0)
assert_allclose(empty_dataset.acceptance, 0)
assert empty_dataset.acceptance.data.shape[0] == 2
assert empty_dataset.acceptance_off.data.shape[0] == 2
assert empty_dataset.gti.time_sum.value == 0
assert len(empty_dataset.gti.table) == 0
assert empty_dataset.energy_range[0] is None
def test_create_stack(self):
geom = RegionGeom(region=None, axes=[self.e_reco])
stacked = SpectrumDatasetOnOff.create(geom=geom,
energy_axis_true=self.e_true)
stacked.mask_safe.data += True
stacked.stack(self.dataset)
assert_allclose(stacked.energy_range.value,
self.dataset.energy_range.value)
def test_alpha(self):
assert self.dataset.alpha.data.shape == (4, 1, 1)
assert_allclose(self.dataset.alpha.data, 0.1)
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,
exposure=aeff * livetime,
models=model,
)
energy = aeff.geom.axes[0].edges
expected = aeff.data[0] * (energy[-1] - energy[0]) * const * livetime
assert_allclose(dataset.npred_signal().data.sum(), expected.value)
def test_to_spectrum_dataset(self):
ds = self.dataset.to_spectrum_dataset()
assert isinstance(ds, SpectrumDataset)
assert_allclose(ds.background.data.sum(), 4)
@requires_dependency("matplotlib")
def test_peek(self):
dataset = self.dataset.copy()
dataset.models = SkyModel(spectral_model=PowerLawSpectralModel())
with mpl_plot_check():
dataset.peek()
@requires_dependency("matplotlib")
def test_plot_fit(self):
dataset = self.dataset.copy()
dataset.models = SkyModel(spectral_model=PowerLawSpectralModel())
with mpl_plot_check():
dataset.plot_fit()
@requires_dependency("matplotlib")
def test_plot_off_regions(self):
from gammapy.visualization import plot_spectrum_datasets_off_regions
with mpl_plot_check():
plot_spectrum_datasets_off_regions([self.dataset])
def test_to_from_ogip_files(self, tmp_path):
dataset = self.dataset.copy(name="test")
dataset.write(tmp_path / "test.fits")
newdataset = SpectrumDatasetOnOff.read(tmp_path / "test.fits")
expected_regions = compound_region_to_list(self.off_counts.geom.region)
regions = compound_region_to_list(newdataset.counts_off.geom.region)
assert newdataset.counts.meta["RESPFILE"] == "test_rmf.fits"
assert newdataset.counts.meta["BACKFILE"] == "test_bkg.fits"
assert newdataset.counts.meta["ANCRFILE"] == "test_arf.fits"
assert_allclose(self.on_counts.data, newdataset.counts.data)
assert_allclose(self.off_counts.data, newdataset.counts_off.data)
assert_allclose(self.edisp.edisp_map.data,
newdataset.edisp.edisp_map.data)
assert_time_allclose(newdataset.gti.time_start, dataset.gti.time_start)
assert len(regions) == len(expected_regions)
assert regions[0].center.is_equivalent_frame(
expected_regions[0].center)
assert_allclose(regions[1].angle, expected_regions[1].angle)
def test_to_from_ogip_files_no_mask(self, tmp_path):
dataset = self.dataset.copy(name="test")
dataset.mask_safe = None
dataset.write(tmp_path / "test.fits")
newdataset = SpectrumDatasetOnOff.read(tmp_path / "test.fits")
assert_allclose(newdataset.mask_safe.data, True)
def test_to_from_ogip_files_zip(self, tmp_path):
dataset = self.dataset.copy(name="test")
dataset.write(tmp_path / "test.fits.gz")
newdataset = SpectrumDatasetOnOff.read(tmp_path / "test.fits.gz")
assert newdataset.counts.meta["RESPFILE"] == "test_rmf.fits.gz"
assert newdataset.counts.meta["BACKFILE"] == "test_bkg.fits.gz"
assert newdataset.counts.meta["ANCRFILE"] == "test_arf.fits.gz"
def test_to_from_ogip_files_no_edisp(self, tmp_path):
mask_safe = RegionNDMap.from_geom(self.on_counts.geom, dtype=bool)
mask_safe.data += True
acceptance = RegionNDMap.from_geom(self.on_counts.geom, data=1.0)
exposure = self.aeff * self.livetime
exposure.meta["livetime"] = self.livetime
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
exposure=exposure,
mask_safe=mask_safe,
acceptance=acceptance,
name="test",
)
dataset.write(tmp_path / "pha_obstest.fits")
newdataset = SpectrumDatasetOnOff.read(tmp_path / "pha_obstest.fits")
assert_allclose(self.on_counts.data, newdataset.counts.data)
assert newdataset.counts_off is None
assert newdataset.edisp is None
assert newdataset.gti is None
def test_energy_mask(self):
mask = self.dataset.counts.geom.energy_mask(energy_min=0.3 * u.TeV,
energy_max=6 * u.TeV)
desired = [False, True, True, False]
assert_allclose(mask.data[:, 0, 0], desired)
mask = self.dataset.counts.geom.energy_mask(energy_max=6 * u.TeV)
desired = [True, True, True, False]
assert_allclose(mask.data[:, 0, 0], desired)
mask = self.dataset.counts.geom.energy_mask(energy_min=1 * u.TeV)
desired = [False, False, True, True]
assert_allclose(mask.data[:, 0, 0], desired)
def test_str(self):
model = SkyModel(spectral_model=PowerLawSpectralModel())
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
models=model,
exposure=self.aeff * self.livetime,
edisp=self.edisp,
acceptance=RegionNDMap.from_geom(geom=self.on_counts.geom, data=1),
acceptance_off=RegionNDMap.from_geom(geom=self.off_counts.geom,
data=10),
)
assert "SpectrumDatasetOnOff" in str(dataset)
assert "wstat" in str(dataset)
def test_fake(self):
"""Test the fake dataset"""
source_model = SkyModel(spectral_model=PowerLawSpectralModel())
dataset = SpectrumDatasetOnOff(
name="test",
counts=self.on_counts,
counts_off=self.off_counts,
models=source_model,
exposure=self.aeff * self.livetime,
edisp=self.edisp,
acceptance=RegionNDMap.from_geom(geom=self.on_counts.geom, data=1),
acceptance_off=RegionNDMap.from_geom(geom=self.off_counts.geom,
data=10),
)
real_dataset = dataset.copy()
background = RegionNDMap.from_geom(dataset.counts.geom)
background.data += 1
dataset.fake(npred_background=background, random_state=314)
assert real_dataset.counts.data.shape == dataset.counts.data.shape
assert real_dataset.counts_off.data.shape == dataset.counts_off.data.shape
assert dataset.counts_off.data.sum() == 39
assert dataset.counts.data.sum() == 5
def test_info_dict(self):
info_dict = self.dataset.info_dict()
assert_allclose(info_dict["counts"], 3)
assert_allclose(info_dict["counts_off"], 40)
assert_allclose(info_dict["acceptance"], 4)
assert_allclose(info_dict["acceptance_off"], 40)
assert_allclose(info_dict["alpha"], 0.1)
assert_allclose(info_dict["excess"], -1, rtol=1e-2)
assert_allclose(info_dict["ontime"].value, 1e3)
assert_allclose(info_dict["sqrt_ts"], -0.501005, rtol=1e-2)
assert info_dict["name"] == "test"
def test_resample_energy_axis(self):
axis = MapAxis.from_edges([0.1, 1, 10] * u.TeV,
name="energy",
interp="log")
grouped = self.dataset.resample_energy_axis(energy_axis=axis)
assert grouped.counts.data.shape == (2, 1, 1)
# exposure should be untouched
assert_allclose(grouped.exposure.data, 1000)
assert_allclose(np.squeeze(grouped.counts), [2, 1])
assert_allclose(np.squeeze(grouped.counts_off), [20, 20])
assert grouped.edisp.edisp_map.data.shape == (9, 2, 1, 1)
assert_allclose(np.squeeze(grouped.acceptance), [2, 2])
assert_allclose(np.squeeze(grouped.acceptance_off), [20, 20])
def test_to_image(self):
grouped = self.dataset.to_image()
assert grouped.counts.data.shape == (1, 1, 1)
# exposure should be untouched
assert_allclose(grouped.exposure.data, 1000)
assert_allclose(np.squeeze(grouped.counts), 3)
assert_allclose(np.squeeze(grouped.counts_off), 40)
assert grouped.edisp.edisp_map.data.shape == (9, 1, 1, 1)
assert_allclose(np.squeeze(grouped.acceptance), 4)
assert_allclose(np.squeeze(grouped.acceptance_off), 40)
class TestSpectrumOnOff:
""" Test ON OFF SpectrumDataset"""
def setup(self):
etrue = np.logspace(-1, 1, 10) * u.TeV
self.e_true = etrue
ereco = np.logspace(-1, 1, 5) * u.TeV
elo = ereco[:-1]
ehi = ereco[1:]
self.e_reco = ereco
self.aeff = EffectiveAreaTable(etrue[:-1], etrue[1:], np.ones(9) * u.cm ** 2)
self.edisp = EDispKernel.from_diagonal_response(etrue, ereco)
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.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 test_spectrumdatasetonoff_create(self):
e_reco = u.Quantity([0.1, 1, 10.0], "TeV")
e_true = u.Quantity([0.05, 0.5, 5, 20.0], "TeV")
empty_dataset = SpectrumDatasetOnOff.create(e_reco, e_true)
assert empty_dataset.counts.data.sum() == 0
assert empty_dataset.data_shape[0] == 2
assert empty_dataset.counts_off.data.sum() == 0
assert empty_dataset.counts_off.geom.axes[0].nbin == 2
assert_allclose(empty_dataset.acceptance_off, 1)
assert_allclose(empty_dataset.acceptance, 1)
assert empty_dataset.acceptance.data.shape[0] == 2
assert empty_dataset.acceptance_off.data.shape[0] == 2
assert empty_dataset.livetime.value == 0
assert len(empty_dataset.gti.table) == 0
assert empty_dataset.energy_range[0] is None
def test_create_stack(self):
stacked = SpectrumDatasetOnOff.create(self.e_reco, self.e_true)
stacked.stack(self.dataset)
assert_allclose(stacked.energy_range.value, self.dataset.energy_range.value)
def test_alpha(self):
assert self.dataset.alpha.data.shape == (4, 1, 1)
assert_allclose(self.dataset.alpha.data, 0.1)
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)
@requires_dependency("matplotlib")
def test_peek(self):
dataset = self.dataset.copy()
dataset.models = SkyModel(spectral_model=PowerLawSpectralModel())
with mpl_plot_check():
dataset.peek()
@requires_dependency("matplotlib")
def test_plot_fit(self):
dataset = self.dataset.copy()
dataset.models = SkyModel(spectral_model=PowerLawSpectralModel())
with mpl_plot_check():
dataset.plot_fit()
def test_to_from_ogip_files(self, tmp_path):
dataset = self.dataset.copy(name="test")
dataset.to_ogip_files(outdir=tmp_path)
newdataset = SpectrumDatasetOnOff.from_ogip_files(tmp_path / "pha_obstest.fits")
expected_regions = compound_region_to_list(self.off_counts.geom.region)
regions = compound_region_to_list(newdataset.counts_off.geom.region)
assert_allclose(self.on_counts.data, newdataset.counts.data)
assert_allclose(self.off_counts.data, newdataset.counts_off.data)
assert_allclose(self.edisp.pdf_matrix, newdataset.edisp.pdf_matrix)
assert_time_allclose(newdataset.gti.time_start, dataset.gti.time_start)
assert len(regions) == len(expected_regions)
assert regions[0].center.is_equivalent_frame(expected_regions[0].center)
assert_allclose(regions[1].angle, expected_regions[1].angle)
def test_to_from_ogip_files_no_edisp(self, tmp_path):
mask_safe = RegionNDMap.from_geom(self.on_counts.geom, dtype=bool)
mask_safe.data += True
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
aeff=self.aeff,
livetime=self.livetime,
mask_safe=mask_safe,
acceptance=1,
name="test",
)
dataset.to_ogip_files(outdir=tmp_path)
newdataset = SpectrumDatasetOnOff.from_ogip_files(tmp_path / "pha_obstest.fits")
assert_allclose(self.on_counts.data, newdataset.counts.data)
assert newdataset.counts_off is None
assert newdataset.edisp is None
assert newdataset.gti is None
def test_energy_mask(self):
mask = self.dataset.counts.geom.energy_mask(emin=0.3 * u.TeV, emax=6 * u.TeV)
desired = [False, True, True, False]
assert_allclose(mask[:, 0, 0], desired)
mask = self.dataset.counts.geom.energy_mask(emax=6 * u.TeV)
desired = [True, True, True, False]
assert_allclose(mask[:, 0, 0], desired)
mask = self.dataset.counts.geom.energy_mask(emin=1 * u.TeV)
desired = [False, False, True, True]
assert_allclose(mask[:, 0, 0], desired)
def test_str(self):
model = SkyModel(spectral_model=PowerLawSpectralModel())
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
models=model,
aeff=self.aeff,
livetime=self.livetime,
edisp=self.edisp,
acceptance=1,
acceptance_off=10,
)
assert "SpectrumDatasetOnOff" in str(dataset)
assert "wstat" in str(dataset)
def test_fake(self):
"""Test the fake dataset"""
source_model = SkyModel(spectral_model=PowerLawSpectralModel())
dataset = SpectrumDatasetOnOff(
counts=self.on_counts,
counts_off=self.off_counts,
models=source_model,
aeff=self.aeff,
livetime=self.livetime,
edisp=self.edisp,
acceptance=1,
acceptance_off=10,
)
real_dataset = dataset.copy()
background = RegionNDMap.from_geom(dataset.counts.geom)
background.data += 1
dataset.fake(background_model=background, random_state=314)
assert real_dataset.counts.data.shape == dataset.counts.data.shape
assert real_dataset.counts_off.data.shape == dataset.counts_off.data.shape
assert dataset.counts_off.data.sum() == 39
assert dataset.counts.data.sum() == 5
def test_info_dict(self):
info_dict = self.dataset.info_dict()
assert_allclose(info_dict["n_on"], 3)
assert_allclose(info_dict["n_off"], 40)
assert_allclose(info_dict["a_on"], 1)
assert_allclose(info_dict["a_off"], 10)
assert_allclose(info_dict["alpha"], 0.1)
assert_allclose(info_dict["excess"], -1)
assert_allclose(info_dict["livetime"].value, 1e3)
assert info_dict["name"] == "test"