def test_sky_model_init():
with pytest.raises(ValueError) as excinfo:
spatial_model = SkyGaussian("0 deg", "0 deg", "0.1 deg")
_ = SkyModel(spectral_model=1234, spatial_model=spatial_model)
assert "Spectral model" in str(excinfo.value)
with pytest.raises(ValueError) as excinfo:
_ = SkyModel(spectral_model=PowerLaw(), spatial_model=1234)
assert "Spatial model" in str(excinfo.value)
def get_npred_map():
position = SkyCoord(0.0, 0.0, frame="galactic", unit="deg")
energy_axis = MapAxis.from_bounds(1,
100,
nbin=30,
unit="TeV",
name="energy",
interp="log")
exposure = Map.create(
binsz=0.02,
map_type="wcs",
skydir=position,
width="5 deg",
axes=[energy_axis],
coordsys="GAL",
unit="cm2 s",
)
spatial_model = SkyGaussian("0 deg", "0 deg", sigma="0.2 deg")
spectral_model = PowerLaw(amplitude="1e-11 cm-2 s-1 TeV-1")
skymodel = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model)
exposure.data = 1e14 * np.ones(exposure.data.shape)
evaluator = MapEvaluator(model=skymodel, exposure=exposure)
npred = evaluator.compute_npred()
return npred
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")
geom = WcsGeom.create(skydir=skydir,
width=(4, 4),
binsz=0.1,
axes=[energy_axis],
coordsys="GAL")
gauss = SkyGaussian("0 deg", "0 deg", "0.4 deg", frame="galactic")
pwl = PowerLaw(amplitude="1e-11 cm-2 s-1 TeV-1")
skymodel = SkyModel(spatial_model=gauss, spectral_model=pwl, name="source")
dataset = simulate_dataset(
skymodel=skymodel,
geom=geom,
pointing=skydir,
irfs=irfs,
random_state=random_state,
)
return dataset
def sky_model():
spatial_model = SkyGaussian(lon_0="0.2 deg",
lat_0="0.1 deg",
sigma="0.2 deg")
spectral_model = PowerLaw(index=3,
amplitude="1e-11 cm-2 s-1 TeV-1",
reference="1 TeV")
return SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
def calc_bk(self, lon0, lat0, sig, amp):
"""
returns the computed b_k and the diffuse model template.
"""
# Define sky model to fit the data
ind = 2.0
spatial_model = SkyGaussian(lon_0=lon0, lat_0=lat0, sigma=sig)
spectral_model = PowerLaw(index=ind, amplitude=amp, reference="1 TeV")
model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model)
# For simulations, we can have the same npred map
b_k = []
Sk_list = []
for count, bkg, exp in zip(self.count_list, self.background_list,
self.exposure_list):
evaluator = MapEvaluator(model=model, exposure=exp)
npred = evaluator.compute_npred()
geom = exp.geom
diffuse_map = WcsNDMap(geom, npred) #This is Sk
Bk = bkg.data
Sk = diffuse_map.data
Nk = count.data
not_has_exposure = ~(exp.data > 0)
not_has_bkg = ~(Bk > 0)
S_B = np.divide(Sk, Bk)
S_B[not_has_exposure] = 0.0
S_B[not_has_bkg] = 0.0
#Sk is nan for large sep.. to be investigated. temp soln
#if np.isnan(np.sum(S_B)):
# S_B=np.zeros(S_B.shape)
delta = np.power(np.sum(Nk) / np.sum(Bk),
2.0) - 4.0 * np.sum(S_B) / np.sum(Bk)
#print(np.sum(Nk),np.sum(Bk),np.sum(Sk),np.sum(S_B), delta)
#print("delta is %f for obs no %s",delta,k)
#bk1=(np.sum(Nk)/np.sum(Bk) - np.sqrt(delta))/2.0
bk2 = (np.sum(Nk) / np.sum(Bk) + np.sqrt(delta)) / 2.0
b_k.append(bk2)
Sk_list.append(diffuse_map)
return Sk_list, b_k
def test_simulate():
irfs = load_cta_irfs(
"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits"
)
# Define sky model to simulate the data
spatial_model = SkyGaussian(lon_0="0 deg", lat_0="0 deg", sigma="0.2 deg")
spectral_model = PowerLaw(index=2,
amplitude="1e-11 cm-2 s-1 TeV-1",
reference="1 TeV")
sky_model_simu = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model)
# Define map geometry
axis = MapAxis.from_edges(np.logspace(-1, 1.0, 20),
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(skydir=(0, 0),
binsz=0.025,
width=(1, 1),
coordsys="GAL",
axes=[axis])
# Define some observation parameters
pointing = SkyCoord(0 * u.deg, 0 * u.deg, frame="galactic")
dataset = simulate_dataset(sky_model_simu,
geom,
pointing,
irfs,
livetime=10 * u.h,
random_state=42)
assert isinstance(dataset, MapDataset)
assert isinstance(dataset.model, SkyModels)
assert dataset.counts.data.dtype is np.dtype("int")
assert_allclose(dataset.counts.data[5, 20, 20], 5)
assert_allclose(dataset.exposure.data[5, 20, 20], 16122681639.856329)
assert_allclose(dataset.background_model.map.data[5, 20, 20],
0.976554,
rtol=1e-5)
assert_allclose(dataset.psf.data[5, 32, 32], 0.044402823)
assert_allclose(dataset.edisp.data.data[10, 10], 0.662208, rtol=1e-5)
def make_example_2():
spatial = SkyGaussian("0 deg", "0 deg", "1 deg")
model = SkyModel(spatial, PowerLaw())
models = SkyModels([model])
models.to_yaml(DATA_PATH / "example2.yaml")
def sky_model(self):
"""Source sky model (`~gammapy.cube.models.SkyModel`)."""
spatial_model = self.spatial_model
spectral_model = self.spectral_model
return SkyModel(spatial_model, spectral_model, name=self.name)
irfs = load_cta_irfs(
"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits")
# In[ ]:
# Define sky model to simulate the data
spatial_model = SkyGaussian(lon_0="0 deg", lat_0="0 deg", sigma="0.2 deg")
spectral_model = ExponentialCutoffPowerLaw(
index=2,
amplitude="3e-12 cm-2 s-1 TeV-1",
reference="1 TeV",
lambda_="0.05 TeV-1",
)
sky_model_simu = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model)
print(sky_model_simu)
# In[ ]:
# Define map geometry
axis = MapAxis.from_edges(np.logspace(-1, 2, 30),
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(skydir=(0, 0),
binsz=0.05,
width=(2, 2),
coordsys="GAL",
axes=[axis])
# ## Simulate
# In[ ]:
filename = (
"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits")
irfs = load_cta_irfs(filename)
# In[ ]:
# Define sky model to simulate the data
spatial_model = SkyGaussian(lon_0="0.2 deg", lat_0="0.1 deg", sigma="0.3 deg")
spectral_model = PowerLaw(index=3,
amplitude="1e-11 cm-2 s-1 TeV-1",
reference="1 TeV")
sky_model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model)
print(sky_model)
# In[ ]:
# Define map geometry
axis = MapAxis.from_edges(np.logspace(-1.0, 1.0, 10),
unit="TeV",
name="energy",
interp="log")
geom = WcsGeom.create(skydir=(0, 0),
binsz=0.02,
width=(5, 4),
coordsys="GAL",
axes=[axis])
def sky_model():
spatial_model = SkyGaussian(lon_0="3 deg", lat_0="4 deg", sigma="3 deg")
spectral_model = PowerLaw(index=2,
amplitude="1e-11 cm-2 s-1 TeV-1",
reference="1 TeV")
return SkyModel(spatial_model, spectral_model, name="source-1")
# In[ ]:
model = SkyDiffuseCube(diffuse_galactic)
evaluator = MapEvaluator(model=model, exposure=exposure, psf=psf_kernel)
background_gal = counts.copy(data=evaluator.compute_npred())
background_gal.sum_over_axes().plot()
print("Background counts from Galactic diffuse: ", background_gal.data.sum())
# In[ ]:
model = SkyModel(SkyDiffuseConstant(), diffuse_iso)
evaluator = MapEvaluator(model=model, exposure=exposure, psf=psf_kernel)
background_iso = counts.copy(data=evaluator.compute_npred())
background_iso.sum_over_axes().plot()
print("Background counts from isotropic diffuse: ", background_iso.data.sum())
# In[ ]:
background = background_gal.copy()
background.data += background_iso.data
spatial_model_1 = SkyGaussian(
lon_0=lon_0_1 * u.deg, lat_0=lat_0_1 * u.deg, sigma="0.3 deg"
)
spatial_model_2 = SkyGaussian(
lon_0=lon_0_2 * u.deg, lat_0=lat_0_2 * u.deg, sigma="0.2 deg"
)
spectral_model_1 = PowerLaw(
index=3, amplitude="1e-11 cm-2 s-1 TeV-1", reference="1 TeV"
)
spectral_model_2 = PowerLaw(
index=3, amplitude="1e-11 cm-2 s-1 TeV-1", reference="1 TeV"
)
sky_model_1 = SkyModel(spatial_model=spatial_model_1, spectral_model=spectral_model_1)
sky_model_2 = SkyModel(spatial_model=spatial_model_2, spectral_model=spectral_model_2)
compound_model = sky_model_1 + sky_model_2
# Define map geometry
axis = MapAxis.from_edges(np.logspace(-1.0, 1.0, 10), unit="TeV")
geom = WcsGeom.create(
skydir=(0, 0), binsz=0.02, width=(2, 2), coordsys="GAL", axes=[axis]
)
# Define some observation parameters
# we are not simulating many pointings / observations
pointing = SkyCoord(0.2, 0.5, unit="deg", frame="galactic")
mask = Map.from_geom(maps["counts"].geom)
coords = mask.geom.get_coord()
mask.data = coords["energy"] > 0.3
# ### Model fit
#
# No we are ready for the actual likelihood fit. We first define the model as a combination of a point source with a powerlaw:
# In[ ]:
spatial_model = SkyPointSource(lon_0="0.01 deg", lat_0="0.01 deg")
spectral_model = PowerLaw(index=2.2,
amplitude="3e-12 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
# Often, it is useful to fit the normalisation (and also the tilt) of the background. To do so, we have to define the background as a model. In this example, we will keep the tilt fixed and the norm free.
# In[ ]:
background_model = BackgroundModel(maps["background"], norm=1.1, tilt=0.0)
background_model.parameters["norm"].frozen = False
background_model.parameters["tilt"].frozen = True
# Now we set up the `MapDataset` object by passing the prepared maps, IRFs as well as the model:
# In[ ]:
dataset = MapDataset(
model=model,
# In[ ]:
coords = maps["counts"].geom.get_coord()
mask = coords["energy"] > 0.3
# ### Model fit
#
# No we are ready for the actual likelihood fit. We first define the model as a combination of a point source with a powerlaw:
# In[ ]:
spatial_model = SkyPointSource(lon_0="0.01 deg", lat_0="0.01 deg")
spectral_model = PowerLaw(index=2.2,
amplitude="3e-12 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
# Often, it is useful to fit the normalisation (and also the tilt) of the background. To do so, we have to define the background as a model. In this example, we will keep the tilt fixed and the norm free.
# In[ ]:
background_model = BackgroundModel(maps["background"], norm=1.1, tilt=0.0)
background_model.parameters["norm"].frozen = False
background_model.parameters["tilt"].frozen = True
# Now we set up the `MapDataset` object by passing the prepared maps, IRFs as well as the model:
# In[ ]:
dataset = MapDataset(
model=model,
