def _map_making(self):
"""Make maps and datasets for 3d analysis."""
log.info("Creating geometry.")
geom = self._create_geometry(self.settings["datasets"]["geom"])
geom_irf = dict(energy_axis_true=None, binsz_irf=None, margin_irf=None)
if "energy-axis-true" in self.settings["datasets"]:
axis_params = self.settings["datasets"]["energy-axis-true"]
geom_irf["energy_axis_true"] = MapAxis.from_bounds(**axis_params)
geom_irf["binsz_irf"] = self.settings["datasets"].get("binsz", None)
geom_irf["margin_irf"] = self.settings["datasets"].get("margin", None)
offset_max = Angle(self.settings["datasets"]["offset-max"])
log.info("Creating datasets.")
maker = MapDatasetMaker(geom=geom, offset_max=offset_max, **geom_irf)
if self.settings["datasets"]["stack-datasets"]:
stacked = MapDataset.create(geom=geom, name="stacked", **geom_irf)
for obs in self.observations:
dataset = maker.run(obs)
stacked.stack(dataset)
self._extract_irf_kernels(stacked)
datasets = [stacked]
else:
datasets = []
for obs in self.observations:
dataset = maker.run(obs)
self._extract_irf_kernels(dataset)
datasets.append(dataset)
self.datasets = Datasets(datasets)
def _map_making(self):
"""Make maps and datasets for 3d analysis."""
log.info("Creating geometry.")
geom = self._create_geometry(self.settings["datasets"]["geom"])
if "geom-irf" in self.settings["datasets"]:
geom_irf = self._create_geometry(self.settings["datasets"]["geom-irf"])
else:
geom_irf = geom.to_binsz(binsz=BINSZ_IRF)
offset_max = Angle(self.settings["datasets"]["offset-max"])
stack_datasets = self.settings["datasets"]["stack-datasets"]
log.info("Creating datasets.")
maker = MapDatasetMaker(
geom=geom,
geom_true=geom_irf,
offset_max=offset_max,
)
if stack_datasets:
stacked = MapDataset.create(geom=geom, geom_irf=geom_irf, name="stacked")
for obs in self.observations:
dataset = maker.run(obs)
stacked.stack(dataset)
self._extract_irf_kernels(stacked)
datasets = [stacked]
else:
datasets = []
for obs in self.observations:
dataset = maker.run(obs)
self._extract_irf_kernels(dataset)
datasets.append(dataset)
self.datasets = Datasets(datasets)
def test_map_maker_ring(observations):
geomd = geom(ebounds=[0.1, 10])
map_dataset_maker = MapDatasetMaker(offset_max="2 deg")
safe_mask_maker = SafeMaskMaker(methods=["offset-max"], offset_max="2 deg")
stacked = MapDatasetOnOff.create(geomd)
regions = CircleSkyRegion(SkyCoord(0, 0, unit="deg", frame="galactic"),
radius=0.5 * u.deg)
exclusion = Map.from_geom(geomd)
exclusion.data = exclusion.geom.region_mask([regions], inside=False)
ring_bkg = RingBackgroundMaker(r_in="0.5 deg",
width="0.4 deg",
exclusion_mask=exclusion)
for obs in observations:
dataset = map_dataset_maker.run(stacked, obs)
dataset = safe_mask_maker.run(dataset, obs)
dataset = dataset.to_image()
dataset_on_off = ring_bkg.run(dataset)
stacked.stack(dataset_on_off)
assert_allclose(np.nansum(stacked.counts.data), 34366, rtol=1e-2)
assert_allclose(np.nansum(stacked.acceptance_off.data), 434.36, rtol=1e-2)
def test_safe_mask_maker(observations):
obs = observations[0]
axis = MapAxis.from_edges([0.1, 1, 10],
name="energy",
interp="log",
unit="TeV")
geom = WcsGeom.create(npix=(11, 11),
axes=[axis],
skydir=obs.pointing_radec)
empty_dataset = MapDataset.create(geom=geom)
dataset_maker = MapDatasetMaker(offset_max="3 deg")
safe_mask_maker = SafeMaskMaker(offset_max="3 deg")
dataset = dataset_maker.run(empty_dataset, obs)
mask_offset = safe_mask_maker.make_mask_offset_max(dataset=dataset,
observation=obs)
assert_allclose(mask_offset.sum(), 109)
mask_energy_aeff_default = safe_mask_maker.make_mask_energy_aeff_default(
dataset=dataset, observation=obs)
assert_allclose(mask_energy_aeff_default.sum(), 242)
with pytest.raises(NotImplementedError) as excinfo:
safe_mask_maker.make_mask_energy_edisp_bias(dataset)
assert "only supported" in str(excinfo.value)
with pytest.raises(NotImplementedError) as excinfo:
safe_mask_maker.make_mask_energy_edisp_bias(dataset)
assert "only supported" in str(excinfo.value)
def prepare_dataset_simple(filename_dataset):
"""Prepare dataset for a given skymodel."""
log.info(f"Reading {IRF_FILE}")
irfs = load_cta_irfs(IRF_FILE)
edisp_gauss = EnergyDispersion2D.from_gauss(
e_true=ENERGY_AXIS_TRUE.edges,
migra=MIGRA_AXIS.edges,
sigma=0.1,
bias=0,
offset=[0, 2, 4, 6, 8] * u.deg,
)
irfs["edisp"] = edisp_gauss
# irfs["aeff"].data.data = np.ones_like(irfs["aeff"].data.data) * 1e6
observation = Observation.create(obs_id=1001,
pointing=POINTING,
livetime=LIVETIME,
irfs=irfs)
empty = MapDataset.create(WCS_GEOM,
energy_axis_true=ENERGY_AXIS_TRUE,
migra_axis=MIGRA_AXIS)
# maker = MapDatasetMaker(selection=["exposure", "edisp"])
# maker = MapDatasetMaker(selection=["exposure", "edisp", "background"])
maker = MapDatasetMaker(
selection=["exposure", "edisp", "psf", "background"])
dataset = maker.run(empty, observation)
filename_dataset.parent.mkdir(exist_ok=True, parents=True)
log.info(f"Writing {filename_dataset}")
dataset.write(filename_dataset, overwrite=True)
def empty_dataset(source_pos_radec, map_geom, e_reco_binning, livetime,
irf_file, offset):
source_pos_ra = source_pos_radec["ra"]
source_pos_dec = source_pos_radec["dec"]
source = SkyCoord(source_pos_ra,
source_pos_dec,
unit="deg",
frame="icrs")
e_reco_min = u.Quantity(e_reco_binning["e_reco_min"]).to("TeV")
e_reco_min = e_reco_min.value
e_reco_max = u.Quantity(e_reco_binning["e_reco_max"]).to("TeV")
e_reco_max = e_reco_max.value
n_e_reco = e_reco_binning["n_e_reco"]
energy_axis = MapAxis.from_edges(np.logspace(
np.log10(e_reco_min), np.log10(e_reco_max), n_e_reco),
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(
skydir=source,
binsz=u.Quantity(map_geom["binsize"]).to("deg").value,
width=(u.Quantity(map_geom["width"]).to("deg").value,
u.Quantity(map_geom["width"]).to("deg").value),
frame="icrs",
axes=[energy_axis])
energy_axis_true = MapAxis.from_edges(np.logspace(
np.log10(e_reco_min), np.log10(e_reco_max), n_e_reco),
unit="TeV",
name="energy",
interp="log")
pointing = SkyCoord(u.Quantity(source_pos_ra).to("deg"),
u.Quantity(source_pos_dec).to("deg") + offset,
frame="icrs",
unit="deg")
irfs = load_cta_irfs(irf_file)
obs = Observation.create(pointing=pointing,
livetime=livetime,
irfs=irfs)
empty = MapDataset.create(geom, energy_axis_true=energy_axis_true)
maker = MapDatasetMaker(
selection=["exposure", "background", "psf", "edisp"])
maker_safe_mask = SafeMaskMaker(
methods=["offset-max"],
offset_max=u.quantity.Quantity(map_geom["width"]) +
1.0 * u.deg)
dataset = maker.run(empty, obs)
dataset = maker_safe_mask.run(dataset, obs)
return dataset
def simulate_map_dataset(random_state=0):
irfs = load_cta_irfs(
"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits"
)
skydir = SkyCoord("0 deg", "0 deg", frame="galactic")
edges = np.logspace(-1, 2, 15) * u.TeV
energy_axis = MapAxis.from_edges(edges=edges, name="energy", interp="log")
geom = WcsGeom.create(skydir=skydir,
width=(4, 4),
binsz=0.1,
axes=[energy_axis],
frame="galactic")
gauss = GaussianSpatialModel(lon_0="0 deg",
lat_0="0 deg",
sigma="0.4 deg",
frame="galactic")
pwl = PowerLawSpectralModel(amplitude="1e-11 cm-2 s-1 TeV-1")
skymodel = SkyModel(spatial_model=gauss, spectral_model=pwl, name="source")
obs = Observation.create(pointing=skydir, livetime=1 * u.h, irfs=irfs)
empty = MapDataset.create(geom)
maker = MapDatasetMaker(
selection=["exposure", "background", "psf", "edisp"])
dataset = maker.run(empty, obs)
dataset.models = skymodel
dataset.fake(random_state=random_state)
return dataset
def test_map_maker_obs(observations):
# Test for different spatial geoms and etrue, ereco bins
geom_reco = geom(ebounds=[0.1, 1, 10])
e_true = MapAxis.from_edges([0.1, 0.5, 2.5, 10.0],
name="energy",
unit="TeV",
interp="log")
geom_exp = geom(ebounds=[0.1, 0.5, 2.5, 10.0])
maker_obs = MapDatasetMaker(
geom=geom_reco,
energy_axis_true=e_true,
binsz_irf=1.0,
margin_irf=1.0,
offset_max=2.0 * u.deg,
cutout=False,
)
map_dataset = maker_obs.run(observations[0])
assert map_dataset.counts.geom == geom_reco
assert map_dataset.background_model.map.geom == geom_reco
assert map_dataset.exposure.geom == geom_exp
assert map_dataset.edisp.edisp_map.data.shape == (3, 48, 6, 11)
assert map_dataset.edisp.exposure_map.data.shape == (3, 1, 6, 11)
assert map_dataset.psf.psf_map.data.shape == (3, 66, 6, 11)
assert map_dataset.psf.exposure_map.data.shape == (3, 1, 6, 11)
assert_allclose(map_dataset.gti.time_delta, 1800.0 * u.s)
assert map_dataset.name == "obs_110380"
def _map_making(self):
"""Make maps and datasets for 3d analysis."""
log.info("Creating geometry.")
geom = self._create_geometry(self.settings["datasets"]["geom"])
geom_irf = dict(energy_axis_true=None, binsz_irf=None, margin_irf=None)
if "energy-axis-true" in self.settings["datasets"]:
axis_params = self.settings["datasets"]["energy-axis-true"]
geom_irf["energy_axis_true"] = MapAxis.from_bounds(**axis_params)
geom_irf["binsz_irf"] = self.settings["datasets"].get("binsz", None)
geom_irf["margin_irf"] = self.settings["datasets"].get("margin", None)
offset_max = Angle(self.settings["datasets"]["offset-max"])
log.info("Creating datasets.")
maker = MapDatasetMaker(offset_max=offset_max)
maker_safe_mask = SafeMaskMaker(methods=["offset-max"], offset_max=offset_max)
stacked = MapDataset.create(geom=geom, name="stacked", **geom_irf)
if self.settings["datasets"]["stack-datasets"]:
for obs in self.observations:
log.info(f"Processing observation {obs.obs_id}")
dataset = maker.run(stacked, obs)
dataset = maker_safe_mask.run(dataset, obs)
dataset.background_model.name = f"bkg_{dataset.name}"
# TODO remove this once dataset and model have unique identifiers
log.debug(dataset)
stacked.stack(dataset)
self._extract_irf_kernels(stacked)
datasets = [stacked]
else:
datasets = []
for obs in self.observations:
log.info(f"Processing observation {obs.obs_id}")
dataset = maker.run(stacked, obs)
dataset = maker_safe_mask.run(dataset, obs)
dataset.background_model.name = f"bkg_{dataset.name}"
# TODO remove this once dataset and model have unique identifiers
self._extract_irf_kernels(dataset)
log.debug(dataset)
datasets.append(dataset)
self.datasets = Datasets(datasets)
def _map_making(self):
"""Make maps and datasets for 3d analysis."""
log.info("Creating geometry.")
geom = self._create_geometry()
geom_settings = self.config.datasets.geom
geom_irf = dict(energy_axis_true=None, binsz_irf=None)
if geom_settings.axes.energy_true.min is not None:
geom_irf["energy_axis_true"] = self._make_energy_axis(
geom_settings.axes.energy_true)
geom_irf["binsz_irf"] = geom_settings.wcs.binsize_irf.to("deg").value
offset_max = geom_settings.selection.offset_max
log.info("Creating datasets.")
maker = MapDatasetMaker(selection=self.config.datasets.map_selection)
safe_mask_selection = self.config.datasets.safe_mask.methods
safe_mask_settings = self.config.datasets.safe_mask.settings
maker_safe_mask = SafeMaskMaker(methods=safe_mask_selection,
**safe_mask_settings)
stacked = MapDataset.create(geom=geom, name="stacked", **geom_irf)
if self.config.datasets.stack:
for obs in self.observations:
log.info(f"Processing observation {obs.obs_id}")
cutout = stacked.cutout(obs.pointing_radec,
width=2 * offset_max)
dataset = maker.run(cutout, obs)
dataset = maker_safe_mask.run(dataset, obs)
log.debug(dataset)
stacked.stack(dataset)
datasets = [stacked]
else:
datasets = []
for obs in self.observations:
log.info(f"Processing observation {obs.obs_id}")
cutout = stacked.cutout(obs.pointing_radec,
width=2 * offset_max)
dataset = maker.run(cutout, obs)
dataset = maker_safe_mask.run(dataset, obs)
log.debug(dataset)
datasets.append(dataset)
self.datasets = Datasets(datasets)
def data_prep():
data_store = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
OBS_ID = 110380
obs_ids = OBS_ID * np.ones(N_OBS)
observations = data_store.get_observations(obs_ids)
energy_axis = MapAxis.from_bounds(0.1,
10,
nbin=10,
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(
skydir=(0, 0),
binsz=0.02,
width=(10, 8),
frame="galactic",
proj="CAR",
axes=[energy_axis],
)
offset_max = 4 * u.deg
maker = MapDatasetMaker()
safe_mask_maker = SafeMaskMaker(methods=["offset-max"],
offset_max=offset_max)
stacked = MapDataset.create(geom=geom)
spatial_model = PointSpatialModel(lon_0="-0.05 deg",
lat_0="-0.05 deg",
frame="galactic")
spectral_model = ExpCutoffPowerLawSpectralModel(
index=2,
amplitude=3e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1.0 * u.TeV,
lambda_=0.1 / u.TeV,
)
model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model,
name="gc-source")
datasets = Datasets([])
for idx, obs in enumerate(observations):
cutout = stacked.cutout(obs.pointing_radec,
width=2 * offset_max,
name=f"dataset{idx}")
dataset = maker.run(cutout, obs)
dataset = safe_mask_maker.run(dataset, obs)
dataset.models = model
datasets.append(dataset)
return datasets
def test_map_maker(pars, observations):
stacked = MapDataset.create(
geom=pars["geom"],
energy_axis_true=pars["e_true"],
binsz_irf=pars["binsz_irf"],
margin_irf=pars["margin_irf"],
)
maker = MapDatasetMaker(
geom=pars["geom"],
energy_axis_true=pars["e_true"],
offset_max="2 deg",
background_oversampling=pars.get("background_oversampling"),
binsz_irf=pars["binsz_irf"],
margin_irf=pars["margin_irf"],
)
safe_mask_maker = SafeMaskMaker(methods=["offset-max"], offset_max="2 deg")
for obs in observations:
dataset = maker.run(obs)
dataset = safe_mask_maker.run(dataset, obs)
stacked.stack(dataset)
counts = stacked.counts
assert counts.unit == ""
assert_allclose(counts.data.sum(), pars["counts"], rtol=1e-5)
exposure = stacked.exposure
assert exposure.unit == "m2 s"
assert_allclose(exposure.data.mean(), pars["exposure"], rtol=3e-3)
background = stacked.background_model.map
assert background.unit == ""
assert_allclose(background.data.sum(), pars["background"], rtol=1e-4)
image_dataset = stacked.to_image()
counts = image_dataset.counts
assert counts.unit == ""
assert_allclose(counts.data.sum(), pars["counts"], rtol=1e-4)
exposure = image_dataset.exposure
assert exposure.unit == "m2 s"
assert_allclose(exposure.data.sum(), pars["exposure_image"], rtol=1e-3)
background = image_dataset.background_model.map
assert background.unit == ""
assert_allclose(background.data.sum(), pars["background"], rtol=1e-4)
def data_prep():
data_store = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
OBS_ID = 110380
obs_ids = OBS_ID * np.ones(N_OBS)
observations = data_store.get_observations(obs_ids)
energy_axis = MapAxis.from_bounds(0.1,
10,
nbin=10,
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(
skydir=(0, 0),
binsz=0.05,
width=(10, 8),
coordsys="GAL",
proj="CAR",
axes=[energy_axis],
)
stacked = MapDataset.create(geom)
maker = MapDatasetMaker()
safe_mask_maker = SafeMaskMaker(methods=["offset-max"], offset_max="4 deg")
for obs in observations:
dataset = maker.run(stacked, obs)
dataset = safe_mask_maker.run(dataset, obs)
stacked.stack(dataset)
spatial_model = PointSpatialModel(lon_0="0.01 deg",
lat_0="0.01 deg",
frame="galactic")
spectral_model = ExpCutoffPowerLawSpectralModel(
index=2,
amplitude=3e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1.0 * u.TeV,
lambda_=0.1 / u.TeV,
)
model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model,
name="gc-source")
stacked.models = model
stacked.name = "stacked_ds"
return Datasets([stacked])
def prepare_dataset(filename_dataset):
"""Prepare dataset for a given skymodel."""
log.info(f"Reading {IRF_FILE}")
irfs = load_cta_irfs(IRF_FILE)
observation = Observation.create(obs_id=1001,
pointing=POINTING,
livetime=LIVETIME,
irfs=irfs)
empty = MapDataset.create(WCS_GEOM,
energy_axis_true=ENERGY_AXIS_TRUE,
migra_axis=MIGRA_AXIS)
maker = MapDatasetMaker(
selection=["exposure", "background", "psf", "edisp"])
dataset = maker.run(empty, observation)
filename_dataset.parent.mkdir(exist_ok=True, parents=True)
log.info(f"Writing {filename_dataset}")
dataset.write(filename_dataset, overwrite=True)
def test_map_maker_obs(observations):
# Test for different spatial geoms and etrue, ereco bins
geom_reco = geom(ebounds=[0.1, 1, 10])
geom_true = geom(ebounds=[0.1, 0.5, 2.5, 10.0], binsz=1.0)
geom_exp = geom(ebounds=[0.1, 0.5, 2.5, 10.0])
maker_obs = MapDatasetMaker(geom=geom_reco,
geom_true=geom_true,
offset_max=2.0 * u.deg,
cutout=False)
map_dataset = maker_obs.run(observations[0])
assert map_dataset.counts.geom == geom_reco
assert map_dataset.background_model.map.geom == geom_reco
assert map_dataset.exposure.geom == geom_exp
assert map_dataset.edisp.edisp_map.data.shape == (3, 48, 5, 10)
assert map_dataset.edisp.exposure_map.data.shape == (3, 1, 5, 10)
assert map_dataset.psf.psf_map.data.shape == (3, 66, 5, 10)
assert map_dataset.psf.exposure_map.data.shape == (3, 1, 5, 10)
assert_allclose(map_dataset.gti.time_delta, 1800.0 * u.s)
assert map_dataset.name == "obs_110380"
def data_prep():
data_store = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
OBS_ID = 110380
obs_ids = OBS_ID * np.ones(N_OBS)
observations = data_store.get_observations(obs_ids)
energy_axis = MapAxis.from_bounds(0.1,
10,
nbin=10,
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(
skydir=(0, 0),
binsz=0.02,
width=(10, 8),
coordsys="GAL",
proj="CAR",
axes=[energy_axis],
)
src_pos = SkyCoord(0, 0, unit="deg", frame="galactic")
offset_max = 4 * u.deg
maker = MapDatasetMaker(offset_max=offset_max)
safe_mask_maker = SafeMaskMaker(methods=["offset-max"], offset_max="4 deg")
stacked = MapDataset.create(geom=geom)
datasets = []
for obs in observations:
dataset = maker.run(stacked, obs)
dataset = safe_mask_maker.run(dataset, obs)
dataset.edisp = dataset.edisp.get_energy_dispersion(
position=src_pos, e_reco=energy_axis.edges)
dataset.psf = dataset.psf.get_psf_kernel(position=src_pos,
geom=geom,
max_radius="0.3 deg")
datasets.append(dataset)
return datasets
def test_map_maker(pars, observations, keepdims):
stacked = MapDataset.create(geom=pars["geom"], geom_irf=pars["geom_true"])
for obs in observations:
maker = MapDatasetMaker(
geom=pars["geom"],
geom_true=pars["geom_true"],
offset_max="2 deg",
background_oversampling=pars.get("background_oversampling"),
)
dataset = maker.run(obs)
stacked.stack(dataset)
counts = stacked.counts
assert counts.unit == ""
assert_allclose(counts.data.sum(), pars["counts"], rtol=1e-5)
exposure = stacked.exposure
assert exposure.unit == "m2 s"
assert_allclose(exposure.data.mean(), pars["exposure"], rtol=3e-3)
background = stacked.background_model.map
assert background.unit == ""
assert_allclose(background.data.sum(), pars["background"], rtol=1e-5)
image_dataset = stacked.to_image()
counts = image_dataset.counts
assert counts.unit == ""
assert_allclose(counts.data.sum(), pars["counts"], rtol=1e-5)
exposure = image_dataset.exposure
assert exposure.unit == "m2 s"
assert_allclose(exposure.data.sum(), pars["exposure_image"], rtol=3e-3)
background = image_dataset.background_model.map
assert background.unit == ""
assert_allclose(background.data.sum(), pars["background"], rtol=1e-5)
def data_prep():
data_store = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1/")
OBS_ID = 23523
obs_ids = OBS_ID * np.ones(N_OBS)
observations = data_store.get_observations(obs_ids)
time_intervals = [(obs.tstart, obs.tstop) for obs in observations]
target_position = SkyCoord(ra=83.63308, dec=22.01450, unit="deg")
emin, emax = [0.7, 10] * u.TeV
energy_axis = MapAxis.from_bounds(emin.value,
emax.value,
10,
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(
skydir=target_position,
binsz=0.02,
width=(2, 2),
coordsys="CEL",
proj="CAR",
axes=[energy_axis],
)
energy_axis_true = MapAxis.from_bounds(0.1,
20,
20,
unit="TeV",
name="energy",
interp="log")
offset_max = 2 * u.deg
datasets = []
maker = MapDatasetMaker(offset_max=offset_max)
safe_mask_maker = SafeMaskMaker(methods=["offset-max"],
offset_max=offset_max)
for time_interval in time_intervals:
observations = observations.select_time(time_interval)
# Proceed with further analysis only if there are observations
# in the selected time window
if len(observations) == 0:
log.warning(f"No observations in time interval: {time_interval}")
continue
stacked = MapDataset.create(geom=geom,
energy_axis_true=energy_axis_true)
for obs in observations:
dataset = maker.run(stacked, obs)
dataset = safe_mask_maker.run(dataset, obs)
stacked.stack(dataset)
stacked.edisp = stacked.edisp.get_energy_dispersion(
position=target_position, e_reco=energy_axis.edges)
stacked.psf = stacked.psf.get_psf_kernel(position=target_position,
geom=stacked.exposure.geom,
max_radius="0.3 deg")
datasets.append(stacked)
spatial_model = PointSpatialModel(lon_0=target_position.ra,
lat_0=target_position.dec,
frame="icrs")
spatial_model.lon_0.frozen = True
spatial_model.lat_0.frozen = True
spectral_model = PowerLawSpectralModel(index=2.6,
amplitude=2.0e-11 *
u.Unit("1 / (cm2 s TeV)"),
reference=1 * u.TeV)
spectral_model.index.frozen = False
sky_model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model,
name="")
for dataset in datasets:
model = sky_model.copy(name="crab")
dataset.model = model
return datasets
def make_datasets_example():
# Define which data to use and print some information
energy_axis = MapAxis.from_edges(
np.logspace(-1.0, 1.0, 4), unit="TeV", name="energy", interp="log"
)
geom0 = WcsGeom.create(
skydir=(0, 0),
binsz=0.1,
width=(1, 1),
coordsys="GAL",
proj="CAR",
axes=[energy_axis],
)
geom1 = WcsGeom.create(
skydir=(1, 0),
binsz=0.1,
width=(1, 1),
coordsys="GAL",
proj="CAR",
axes=[energy_axis],
)
geoms = [geom0, geom1]
sources_coords = [(0, 0), (0.9, 0.1)]
names = ["gc", "g09"]
models = []
for ind, (lon, lat) in enumerate(sources_coords):
spatial_model = PointSpatialModel(
lon_0=lon * u.deg, lat_0=lat * u.deg, frame="galactic"
)
spectral_model = ExpCutoffPowerLawSpectralModel(
index=2 * u.Unit(""),
amplitude=3e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1.0 * u.TeV,
lambda_=0.1 / u.TeV,
)
model_ecpl = SkyModel(
spatial_model=spatial_model, spectral_model=spectral_model, name=names[ind]
)
models.append(model_ecpl)
# test to link a spectral parameter
params0 = models[0].spectral_model.parameters
params1 = models[1].spectral_model.parameters
ind = params0.parameters.index(params0["reference"])
params0.parameters[ind] = params1["reference"]
# update the sky model
ind = models[0].parameters.parameters.index(models[0].parameters["reference"])
models[0].parameters.parameters[ind] = params1["reference"]
obs_ids = [110380, 111140, 111159]
data_store = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
diffuse_model = SkyDiffuseCube.read(
"$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits"
)
datasets_list = []
for idx, geom in enumerate(geoms):
observations = data_store.get_observations(obs_ids)
stacked = MapDataset.create(geom=geom)
stacked.background_model.name = "background_irf_" + names[idx]
maker = MapDatasetMaker(geom=geom, offset_max=4.0 * u.deg)
for obs in observations:
dataset = maker.run(obs)
stacked.stack(dataset)
stacked.psf = stacked.psf.get_psf_kernel(position=geom.center_skydir, geom=geom, max_radius="0.3 deg")
stacked.edisp = stacked.edisp.get_energy_dispersion(position=geom.center_skydir, e_reco=energy_axis.edges)
stacked.name = names[idx]
stacked.model = models[idx] + diffuse_model
datasets_list.append(stacked)
datasets = Datasets(datasets_list)
dataset0 = datasets.datasets[0]
print("dataset0")
print("counts sum : ", dataset0.counts.data.sum())
print("expo sum : ", dataset0.exposure.data.sum())
print("bkg0 sum : ", dataset0.background_model.evaluate().data.sum())
path = "$GAMMAPY_DATA/tests/models/gc_example_"
datasets.to_yaml(path, overwrite=True)
name="model_simu", ) print(model_simu) # Now, comes the main part of dataset simulation. We create an in-memory observation and an empty dataset. We then predict the number of counts for the given model, and Poission fluctuate it using `fake()` to make a simulated counts maps. Keep in mind that it is important to specify the `selection` of the maps that you want to produce # In[ ]: # Create an in-memory observation obs = Observation.create(pointing=pointing, livetime=livetime, irfs=irfs) print(obs) # Make the MapDataset empty = MapDataset.create(geom) maker = MapDatasetMaker(selection=["exposure", "background", "psf", "edisp"]) maker_safe_mask = SafeMaskMaker(methods=["offset-max"], offset_max=4.0 * u.deg) dataset = maker.run(empty, obs) dataset = maker_safe_mask.run(dataset, obs) print(dataset) # In[ ]: # Add the model on the dataset and Poission fluctuate dataset.models = model_simu dataset.fake() # Do a print on the dataset - there is now a counts maps print(dataset) # Now use this dataset as you would in all standard analysis. You can plot the maps, or proceed with your custom analysis. # In the next section, we show the standard 3D fitting as in [analysis_3d](analysis_3d.ipynb). # In[ ]: