def test_write(self):
# Without GTI
self.events.write("test.fits", overwrite=True)
read_again = EventList.read("test.fits")
# the meta dictionaries match because the input one
# already has the EXTNAME keyword
assert self.events.table.meta == read_again.table.meta
assert (self.events.table == read_again.table).all()
dummy_events = EventList(Table())
dummy_events.write("test.fits", overwrite=True)
read_again = EventList.read("test.fits")
assert read_again.table.meta['EXTNAME'] == "EVENTS"
assert read_again.table.meta['HDUCLASS'] == "GADF"
assert read_again.table.meta['HDUCLAS1'] == "EVENTS"
# With GTI
gti = GTI.read(
"$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_020136.fits.gz"
)
self.events.write("test.fits", overwrite=True, gti=gti)
read_again_ev = EventList.read("test.fits")
read_again_gti = GTI.read("test.fits")
assert self.events.table.meta == read_again_ev.table.meta
assert (self.events.table == read_again_ev.table).all()
assert gti.table.meta == read_again_gti.table.meta
assert (gti.table == read_again_gti.table).all()
# test that it won't work if gti is not a GTI
with pytest.raises(TypeError):
self.events.write("test.fits", overwrite=True, gti=gti.table)
# test that it won't work if format is not "gadf"
with pytest.raises(ValueError):
self.events.write("test.fits", overwrite=True, format='something')
# test that it won't work if the basic headers are wrong
with pytest.raises(ValueError):
dummy_events = EventList(Table())
dummy_events.table.meta['HDUCLAS1'] = 'response'
dummy_events.write("test.fits", overwrite=True)
with pytest.raises(ValueError):
dummy_events = EventList(Table())
dummy_events.table.meta['HDUCLASS'] = "ogip"
dummy_events.write("test.fits", overwrite=True)
# test that it works when the srings are right but lowercase
dummy_events = EventList(Table())
dummy_events.table.meta['HDUCLASS'] = "gadf"
dummy_events.table.meta['HDUCLAS1'] = "events"
dummy_events.write("test.fits", overwrite=True)
def sample_background(self, dataset):
"""Sample background
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset
Returns
-------
events : `gammapy.data.EventList`
Background events
"""
background = dataset.npred_background()
temporal_model = ConstantTemporalModel()
table = self._sample_coord_time(background, temporal_model, dataset.gti)
table["MC_ID"] = 0
table["ENERGY"] = table["ENERGY_TRUE"]
table["RA"] = table["RA_TRUE"]
table["DEC"] = table["DEC_TRUE"]
return EventList(table)
def sample_background(self, dataset):
"""Sample background
Parameters
----------
dataset : `~gammapy.cube.MapDataset`
Map dataset
Returns
-------
events : `gammapy.data.EventList`
Background events
"""
background = dataset.background_model.evaluate()
temporal_model = ConstantTemporalModel()
table = self._sample_coord_time(background, temporal_model,
dataset.gti)
table["MC_ID"] = 0
table.rename_column("ENERGY_TRUE", "ENERGY")
table.rename_column("RA_TRUE", "RA")
table.rename_column("DEC_TRUE", "DEC")
return EventList(table)
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.cube.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, evaluator in enumerate(dataset._evaluators):
evaluator = copy.deepcopy(evaluator)
evaluator.edisp = None
evaluator.psf = None
npred = evaluator.compute_npred()
temporal_model = ConstantTemporalModel()
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
table["MC_ID"] = idx + 1
events_all.append(EventList(table))
return EventList.stack(events_all)
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.cube.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, model in enumerate(dataset.models):
if isinstance(model, BackgroundModel):
continue
evaluator = dataset.evaluators.get(model.name)
evaluator = copy.deepcopy(evaluator)
evaluator.model.apply_irf["psf"] = False
evaluator.model.apply_irf["edisp"] = False
npred = evaluator.compute_npred()
temporal_model = ConstantTemporalModel()
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
table["MC_ID"] = idx + 1
events_all.append(EventList(table))
return EventList.stack(events_all)
def events():
t = Table()
t["EVENT_ID"] = np.array([1, 5], dtype=np.uint16)
t["RA"] = [5, 11] * u.deg
t["DEC"] = [0, 0] * u.deg
t["ENERGY"] = [10, 12] * u.TeV
t["TIME"] = [3, 4] * u.s
return EventList(t)
def setup_class(self):
table = Table()
table["RA"] = [0.0, 0.0, 0.0, 10.0] * u.deg
table["DEC"] = [0.0, 0.9, 10.0, 10.0] * u.deg
table["ENERGY"] = [1.0, 1.5, 1.5, 10.0] * u.TeV
table["OFFSET"] = [0.1, 0.5, 1.0, 1.5] * u.deg
self.events = EventList(table)
center1 = SkyCoord(0.0, 0.0, frame="icrs", unit="deg")
on_region1 = CircleSkyRegion(center1, radius=1.0 * u.deg)
center2 = SkyCoord(0.0, 10.0, frame="icrs", unit="deg")
on_region2 = RectangleSkyRegion(center2, width=0.5 * u.deg, height=0.3 * u.deg)
self.on_regions = [on_region1, on_region2]
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.cube.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, model in enumerate(dataset.models):
if isinstance(model, BackgroundModel):
continue
evaluator = dataset.evaluators.get(model.name)
evaluator = copy.deepcopy(evaluator)
evaluator.model.apply_irf["psf"] = False
evaluator.model.apply_irf["edisp"] = False
npred = evaluator.compute_npred()
if hasattr(model, "temporal_model"):
if getattr(model, "temporal_model") is None:
temporal_model = ConstantTemporalModel()
else:
temporal_model = model.temporal_model
else:
temporal_model = ConstantTemporalModel()
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
if len(table) > 0:
table["MC_ID"] = idx + 1
else:
mcid = table.Column(name="MC_ID", length=0, dtype=int)
table.add_column(mcid)
events_all.append(EventList(table))
return EventList.stack(events_all)
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, evaluator in enumerate(dataset.evaluators.values()):
if evaluator.needs_update:
evaluator.update(
dataset.exposure,
dataset.psf,
dataset.edisp,
dataset._geom,
dataset.mask,
)
flux = evaluator.compute_flux()
npred = evaluator.apply_exposure(flux)
if evaluator.model.temporal_model is None:
temporal_model = ConstantTemporalModel()
else:
temporal_model = evaluator.model.temporal_model
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
if len(table) > 0:
table["MC_ID"] = idx + 1
else:
mcid = table.Column(name="MC_ID", length=0, dtype=int)
table.add_column(mcid)
events_all.append(EventList(table))
return EventList.from_stack(events_all)
def setup_class(self):
table = Table()
table["RA"] = [0.0, 0.0, 0.0, 0.0, 10.0] * u.deg
table["DEC"] = [0.0, 0.05, 0.9, 10.0, 10.0] * u.deg
table["ENERGY"] = [1.0, 1.0, 1.5, 1.5, 10.0] * u.TeV
table["OFFSET"] = [0.1, 0.1, 0.5, 1.0, 1.5] * u.deg
table.meta["RA_PNT"] = 0 * u.deg
table.meta["DEC_PNT"] = 0.5 * u.deg
meta_obs = dict()
meta_obs["RA_PNT"] = 0 * u.deg
meta_obs["DEC_PNT"] = 0.5 * u.deg
meta_obs["DEADC"] = 1
meta = time_ref_to_dict("2010-01-01")
gti_table = Table({"START": [1], "STOP": [3]}, meta=meta)
gti = GTI(gti_table)
self.observation = Observation(
events=EventList(table), obs_info=meta_obs, gti=gti
)
def setup_class(self):
self.observations = []
for sign in [-1, 1]:
events = Table()
events["RA"] = [0.0, 0.0, 0.0, 0.0, 10.0] * u.deg
events["DEC"] = sign * ([0.0, 0.05, 0.9, 10.0, 10.0] * u.deg)
events["ENERGY"] = [1.0, 1.0, 1.5, 1.5, 10.0] * u.TeV
events["OFFSET"] = [0.1, 0.1, 0.5, 1.0, 1.5] * u.deg
obs_info = dict(
RA_PNT=0 * u.deg,
DEC_PNT=sign * 0.5 * u.deg,
DEADC=1,
)
events.meta.update(obs_info)
meta = time_ref_to_dict("2010-01-01")
gti_table = Table({"START": [1], "STOP": [3]}, meta=meta)
gti = GTI(gti_table)
self.observations.append(Observation(
events=EventList(events), obs_info=obs_info, gti=gti
))
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, model in enumerate(dataset.models):
if isinstance(model, BackgroundModel):
continue
evaluator = dataset.evaluators.get(model)
flux = evaluator.compute_flux()
npred = evaluator.apply_exposure(flux)
if model.temporal_model is None:
temporal_model = ConstantTemporalModel()
else:
temporal_model = model.temporal_model
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
if len(table) > 0:
table["MC_ID"] = idx + 1
else:
mcid = table.Column(name="MC_ID", length=0, dtype=int)
table.add_column(mcid)
events_all.append(EventList(table))
return EventList.from_stack(events_all)
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)
