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_true",
interp="log")
exposure = Map.create(
binsz=0.02,
map_type="wcs",
skydir=position,
width="2 deg",
axes=[energy_axis],
frame="galactic",
unit="cm2 s",
)
spatial_model = GaussianSpatialModel(lon_0="0 deg",
lat_0="0 deg",
sigma="0.2 deg",
frame="galactic")
spectral_model = PowerLawSpectralModel(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 evaluator, npred
def estimate_exposure_reco_energy(dataset, spectral_model=None):
"""Estimate an exposure map in reconstructed energy.
Parameters
----------
dataset:`~gammapy.datasets.MapDataset` or `~gammapy.datasets.MapDatasetOnOff`
the input dataset
spectral_model: `~gammapy.modeling.models.SpectralModel`
assumed spectral shape. If none, a Power Law of index 2 is assumed
Returns
-------
exposure : `Map`
Exposure map in reconstructed energy
"""
if spectral_model is None:
spectral_model = PowerLawSpectralModel()
model = SkyModel(spatial_model=ConstantFluxSpatialModel(),
spectral_model=spectral_model)
energy_axis = dataset._geom.axes["energy"]
edisp = None
if dataset.edisp is not None:
edisp = dataset.edisp.get_edisp_kernel(position=None,
energy_axis=energy_axis)
meval = MapEvaluator(model=model, exposure=dataset.exposure, edisp=edisp)
npred = meval.compute_npred()
ref_flux = spectral_model.integral(energy_axis.edges[:-1],
energy_axis.edges[1:])
reco_exposure = npred / ref_flux[:, np.newaxis, np.newaxis]
return reco_exposure
def test_compute_flux_spatial():
center = SkyCoord("0 deg", "0 deg", frame="galactic")
region = CircleSkyRegion(center=center, radius=0.1 * u.deg)
nbin = 2
energy_axis_true = MapAxis.from_energy_bounds(
".1 TeV", "10 TeV", nbin=nbin, name="energy_true"
)
spectral_model = ConstantSpectralModel()
spatial_model = PointSpatialModel(
lon_0=0 * u.deg, lat_0=0 * u.deg, frame="galactic"
)
models = SkyModel(spectral_model=spectral_model, spatial_model=spatial_model)
model = Models(models)
exposure_region = RegionNDMap.create(region, axes=[energy_axis_true])
exposure_region.data += 1.0
exposure_region.unit = "m2 s"
geom = RegionGeom(region, axes=[energy_axis_true])
psf = PSFKernel.from_gauss(geom.to_wcs_geom(), sigma="0.1 deg")
evaluator = MapEvaluator(model=model[0], exposure=exposure_region, psf=psf)
flux = evaluator.compute_flux_spatial()
assert_allclose(flux.value, [0.39677402, 0.39677402], atol=0.001)
def test_sky_point_source():
# Test special case of point source. Regression test for GH 2367.
energy_axis = MapAxis.from_edges(
[1, 10], unit="TeV", name="energy_true", interp="log"
)
exposure = Map.create(
skydir=(100, 70),
npix=(4, 4),
binsz=0.1,
proj="AIT",
unit="cm2 s",
axes=[energy_axis],
)
exposure.data = np.ones_like(exposure.data)
spatial_model = PointSpatialModel(
lon_0=100.06 * u.deg, lat_0=70.03 * u.deg, frame="icrs"
)
# Create a spectral model with integral flux of 1 cm-2 s-1 in this energy band
spectral_model = ConstantSpectralModel(const="1 cm-2 s-1 TeV-1")
spectral_model.const.value /= spectral_model.integral(1 * u.TeV, 10 * u.TeV).value
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
evaluator = MapEvaluator(model=model, exposure=exposure)
flux = evaluator.compute_flux().quantity.to_value("cm-2 s-1")[0]
expected = [
[0, 0, 0, 0],
[0, 0.140, 0.058, 0.0],
[0, 0.564, 0.236, 0],
[0, 0, 0, 0],
]
assert_allclose(flux, expected, atol=0.01)
assert_allclose(flux.sum(), 1)
def estimate_kernel(self, dataset):
"""Get the convolution kernel for the input dataset.
Convolves the model with the PSFKernel at the center of the dataset.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
Returns
-------
kernel : `Map`
Kernel map
"""
# TODO: further simplify the code below
geom = dataset.counts.geom
model = self.model.copy()
model.spatial_model.position = geom.center_skydir
binsz = np.mean(geom.pixel_scales)
width_pix = self.kernel_width / binsz
npix = round_up_to_odd(width_pix.to_value(""))
axis = dataset.exposure.geom.axes["energy_true"]
geom_kernel = WcsGeom.create(skydir=model.position,
proj="TAN",
npix=npix,
axes=[axis],
binsz=binsz)
exposure = Map.from_geom(geom_kernel, unit="cm2 s1")
exposure.data += 1.0
# We use global evaluation mode to not modify the geometry
evaluator = MapEvaluator(model, evaluation_mode="global")
evaluator.update(exposure, dataset.psf, dataset.edisp,
dataset.counts.geom)
kernel = evaluator.compute_npred()
kernel.data /= kernel.data.sum()
if (self.kernel_width + binsz >= geom.width).any():
raise ValueError(
"Kernel shape larger than map shape, please adjust"
" size of the kernel")
return kernel
def estimate_kernel(self, dataset):
"""Get the convolution kernel for the input dataset.
Convolves the model with the PSFKernel at the center of the dataset.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
Returns
-------
kernel : `Map`
Kernel map
"""
# TODO: further simplify the code below
geom = dataset.exposure.geom
model = self.model.copy()
model.spatial_model.position = geom.center_skydir
geom_kernel = geom.to_odd_npix(max_radius=self.kernel_width / 2)
# Creating exposure map with exposure at map center
exposure = Map.from_geom(geom_kernel, unit="cm2 s1")
coord = MapCoord.create(
dict(skycoord=geom.center_skydir,
energy_true=geom.axes["energy_true"].center))
exposure.data[...] = dataset.exposure.get_by_coord(coord)[:,
np.newaxis,
np.newaxis]
# We use global evaluation mode to not modify the geometry
evaluator = MapEvaluator(model, evaluation_mode="global")
evaluator.update(
exposure,
dataset.psf,
dataset.edisp,
dataset.counts.geom,
dataset.mask_fit,
)
kernel = evaluator.compute_npred()
kernel.data /= kernel.data.sum()
if (self.kernel_width >= geom.width).any():
raise ValueError(
"Kernel shape larger than map shape, please adjust"
" size of the kernel")
return kernel
def estimate_exposure_reco_energy(dataset,
spectral_model=None,
normalize=True):
"""Estimate an exposure map in reconstructed energy.
Parameters
----------
dataset:`~gammapy.datasets.MapDataset` or `~gammapy.datasets.MapDatasetOnOff`
the input dataset
spectral_model: `~gammapy.modeling.models.SpectralModel`
assumed spectral shape. If none, a Power Law of index 2 is assumed
normalize : bool
Normalize the exposure to the total integrated flux of the spectral model.
When not normalized it directly gives the predicted counts from the spectral
model.
Returns
-------
exposure : `Map`
Exposure map in reconstructed energy
"""
if spectral_model is None:
spectral_model = PowerLawSpectralModel()
model = SkyModel(spatial_model=ConstantFluxSpatialModel(),
spectral_model=spectral_model)
energy_axis = dataset._geom.axes["energy"]
if dataset.edisp is not None:
edisp = dataset.edisp.get_edisp_kernel(position=None,
energy_axis=energy_axis)
else:
edisp = None
eval = MapEvaluator(model=model, exposure=dataset.exposure, edisp=edisp)
reco_exposure = eval.compute_npred()
if normalize:
ref_flux = spectral_model.integral(energy_axis.edges[:-1],
energy_axis.edges[1:])
reco_exposure = reco_exposure / ref_flux[:, np.newaxis, np.newaxis]
return reco_exposure
def get_kernel(self, dataset):
"""Set the convolution kernel for the input dataset.
Convolves the model with the PSFKernel at the center of the dataset.
If no PSFMap or PSFKernel is found the dataset, the model is used without convolution.
"""
# TODO: further simplify the code below
geom = dataset.counts.geom
if self.downsampling_factor:
geom = geom.downsample(self.downsampling_factor)
model = self.model.copy()
model.spatial_model.position = geom.center_skydir
binsz = np.mean(geom.pixel_scales)
width_pix = self.kernel_width / binsz
npix = round_up_to_odd(width_pix.to_value(""))
axis = dataset.exposure.geom.get_axis_by_name("energy_true")
geom = WcsGeom.create(skydir=model.position,
proj="TAN",
npix=npix,
axes=[axis],
binsz=binsz)
exposure = Map.from_geom(geom, unit="cm2 s1")
exposure.data += 1.0
# We use global evaluation mode to not modify the geometry
evaluator = MapEvaluator(model, evaluation_mode="global")
evaluator.update(exposure, dataset.psf, dataset.edisp,
dataset.counts.geom)
kernel = evaluator.compute_npred().sum_over_axes()
kernel.data /= kernel.data.sum()
if (self.kernel_width > geom.width).any():
raise ValueError(
"Kernel shape larger than map shape, please adjust"
" size of the kernel")
return kernel
def estimate_kernel(self, dataset):
"""Get the convolution kernel for the input dataset.
Convolves the model with the PSFKernel at the center of the dataset.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
Returns
-------
kernel : `Map`
Kernel map
"""
geom = dataset.exposure.geom
if self.kernel_width is not None:
geom = geom.to_odd_npix(max_radius=self.kernel_width / 2)
model = self.model.copy()
model.spatial_model.position = geom.center_skydir
# Creating exposure map with exposure at map center
exposure = Map.from_geom(geom, unit="cm2 s1")
exposure_center = dataset.exposure.to_region_nd_map(geom.center_skydir)
exposure.data[...] = exposure_center.data
# We use global evaluation mode to not modify the geometry
evaluator = MapEvaluator(model=model)
evaluator.update(
exposure=exposure,
psf=dataset.psf,
edisp=dataset.edisp,
geom=dataset.counts.geom,
mask=dataset.mask_image,
)
kernel = evaluator.compute_npred()
kernel.data /= kernel.data.sum()
return kernel
def estimate_kernel(self, dataset):
"""Get the convolution kernel for the input dataset.
Convolves the model with the PSFKernel at the center of the dataset.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Input dataset.
Returns
-------
kernel : `Map`
Kernel map
"""
# TODO: further simplify the code below
geom = dataset.exposure.geom
model = self.model.copy()
model.spatial_model.position = geom.center_skydir
geom_kernel = geom.to_odd_npix(max_radius=self.kernel_width / 2)
exposure = Map.from_geom(geom_kernel, unit="cm2 s1")
exposure.data += 1.0
# We use global evaluation mode to not modify the geometry
evaluator = MapEvaluator(model, evaluation_mode="global")
evaluator.update(exposure, dataset.psf, dataset.edisp,
dataset.counts.geom)
kernel = evaluator.compute_npred()
kernel.data /= kernel.data.sum()
if (self.kernel_width >= geom.width).any():
raise ValueError(
"Kernel shape larger than map shape, please adjust"
" size of the kernel")
return kernel
def diffuse_evaluator(diffuse_model, exposure, psf, edisp):
return MapEvaluator(diffuse_model, exposure, psf=psf, edisp=edisp)
def evaluator(sky_model, exposure, psf, edisp, gti):
return MapEvaluator(sky_model, exposure, psf=psf, edisp=edisp, gti=gti)
def run_region(self, kr, lon, lat, radius):
# TODO: for now we have to read/create the allsky maps each in each job
# because we can't pickle <functools._lru_cache_wrapper object
# send this back to init when fixed
# exposure
exposure_hpx = Map.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz"
)
exposure_hpx.unit = "cm2 s"
# iem
iem_filepath = BASE_PATH / "data" / "gll_iem_v06_extrapolated.fits"
iem_fermi_extra = Map.read(iem_filepath)
# norm=1.1, tilt=0.03 see paper appendix A
model_iem = SkyDiffuseCube(
iem_fermi_extra, norm=1.1, tilt=0.03, name="iem_extrapolated"
)
# ROI
roi_time = time()
ROI_pos = SkyCoord(lon, lat, frame="galactic", unit="deg")
width = 2 * (radius + self.psf_margin)
# Counts
counts = Map.create(
skydir=ROI_pos,
width=width,
proj="CAR",
frame="galactic",
binsz=1 / 8.0,
axes=[self.energy_axis],
dtype=float,
)
counts.fill_by_coord(
{"skycoord": self.events.radec, "energy": self.events.energy}
)
axis = MapAxis.from_nodes(
counts.geom.axes[0].center, name="energy_true", unit="GeV", interp="log"
)
wcs = counts.geom.wcs
geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis])
coords = geom.get_coord()
# expo
data = exposure_hpx.interp_by_coord(coords)
exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit, dtype=float)
# read PSF
psf_kernel = PSFKernel.from_table_psf(
self.psf, geom, max_radius=self.psf_margin * u.deg
)
# Energy Dispersion
e_true = exposure.geom.axes[0].edges
e_reco = counts.geom.axes[0].edges
edisp = EDispKernel.from_diagonal_response(e_true=e_true, e_reco=e_reco)
# fit mask
if coords["lon"].min() < 90 * u.deg and coords["lon"].max() > 270 * u.deg:
coords["lon"][coords["lon"].value > 180] -= 360 * u.deg
mask = (
(coords["lon"] >= coords["lon"].min() + self.psf_margin * u.deg)
& (coords["lon"] <= coords["lon"].max() - self.psf_margin * u.deg)
& (coords["lat"] >= coords["lat"].min() + self.psf_margin * u.deg)
& (coords["lat"] <= coords["lat"].max() - self.psf_margin * u.deg)
)
mask_fermi = WcsNDMap(counts.geom, mask)
# IEM
eval_iem = MapEvaluator(
model=model_iem, exposure=exposure, psf=psf_kernel, edisp=edisp
)
bkg_iem = eval_iem.compute_npred()
# ISO
eval_iso = MapEvaluator(model=self.model_iso, exposure=exposure, edisp=edisp)
bkg_iso = eval_iso.compute_npred()
# merge iem and iso, only one local normalization is fitted
dataset_name = "3FHL_ROI_num" + str(kr)
background_total = bkg_iem + bkg_iso
background_model = BackgroundModel(
background_total, name="bkg_iem+iso", datasets_names=[dataset_name]
)
background_model.parameters["norm"].min = 0.0
# Sources model
in_roi = self.FHL3.positions.galactic.contained_by(wcs)
FHL3_roi = []
for ks in range(len(self.FHL3.table)):
if in_roi[ks] == True:
model = self.FHL3[ks].sky_model()
model.spatial_model.parameters.freeze_all() # freeze spatial
model.spectral_model.parameters["amplitude"].min = 0.0
if isinstance(model.spectral_model, PowerLawSpectralModel):
model.spectral_model.parameters["index"].min = 0.1
model.spectral_model.parameters["index"].max = 10.0
else:
model.spectral_model.parameters["alpha"].min = 0.1
model.spectral_model.parameters["alpha"].max = 10.0
FHL3_roi.append(model)
model_total = Models([background_model] + FHL3_roi)
# Dataset
dataset = MapDataset(
models=model_total,
counts=counts,
exposure=exposure,
psf=psf_kernel,
edisp=edisp,
mask_fit=mask_fermi,
name=dataset_name,
)
cat_stat = dataset.stat_sum()
datasets = Datasets([dataset])
fit = Fit(datasets)
results = fit.run(**self.optimize_opts)
print("ROI_num", str(kr), "\n", results)
fit_stat = datasets.stat_sum()
if results.message != "Optimization failed.":
datasets.write(path=Path(self.resdir), prefix=dataset.name, overwrite=True)
np.savez(
self.resdir / f"3FHL_ROI_num{kr}_fit_infos.npz",
message=results.message,
stat=[cat_stat, fit_stat],
)
exec_time = time() - roi_time
print("ROI", kr, " time (s): ", exec_time)
for model in FHL3_roi:
if (
self.FHL3[model.name].data["ROI_num"] == kr
and self.FHL3[model.name].data["Signif_Avg"] >= self.sig_cut
):
flux_points = FluxPointsEstimator(
e_edges=self.El_flux, source=model.name, n_sigma_ul=2,
).run(datasets=datasets)
filename = self.resdir / f"{model.name}_flux_points.fits"
flux_points.write(filename, overwrite=True)
exec_time = time() - roi_time - exec_time
print("ROI", kr, " Flux points time (s): ", exec_time)
def run_region(self, kr, lon, lat, radius):
# TODO: for now we have to read/create the allsky maps each in each job
# because we can't pickle <functools._lru_cache_wrapper object
# send this back to init when fixed
log.info(f"ROI {kr}: loading data")
# exposure
exposure_hpx = Map.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz")
exposure_hpx.unit = "cm2 s"
# psf
psf_map = PSFMap.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz",
format="gtpsf")
# reduce size of the PSF
axis = psf_map.psf_map.geom.axes["rad"].center.to_value(u.deg)
indmax = np.argmin(np.abs(self.psf_margin - axis))
psf_map = psf_map.slice_by_idx(slices={"rad": slice(0, indmax)})
# iem
iem_filepath = BASE_PATH / "data" / "gll_iem_v06_extrapolated.fits"
iem_fermi_extra = Map.read(iem_filepath)
# norm=1.1, tilt=0.03 see paper appendix A
model_iem = SkyModel(
PowerLawNormSpectralModel(norm=1.1, tilt=0.03),
TemplateSpatialModel(iem_fermi_extra, normalize=False),
name="iem_extrapolated",
)
# ROI
roi_time = time()
ROI_pos = SkyCoord(lon, lat, frame="galactic", unit="deg")
width = 2 * (radius + self.psf_margin)
# Counts
counts = Map.create(
skydir=ROI_pos,
width=width,
proj="CAR",
frame="galactic",
binsz=1 / 8.0,
axes=[self.energy_axis],
dtype=float,
)
counts.fill_by_coord({
"skycoord": self.events.radec,
"energy": self.events.energy
})
axis = MapAxis.from_nodes(counts.geom.axes[0].center,
name="energy_true",
unit="GeV",
interp="log")
wcs = counts.geom.wcs
geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis])
coords = geom.get_coord()
# expo
data = exposure_hpx.interp_by_coord(coords)
exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit, dtype=float)
# Energy Dispersion
edisp = EDispKernelMap.from_diagonal_response(
energy_axis_true=axis, energy_axis=self.energy_axis)
# fit mask
if coords["lon"].min() < 90 * u.deg and coords["lon"].max(
) > 270 * u.deg:
coords["lon"][coords["lon"].value > 180] -= 360 * u.deg
mask = (
(coords["lon"] >= coords["lon"].min() + self.psf_margin * u.deg)
& (coords["lon"] <= coords["lon"].max() - self.psf_margin * u.deg)
& (coords["lat"] >= coords["lat"].min() + self.psf_margin * u.deg)
& (coords["lat"] <= coords["lat"].max() - self.psf_margin * u.deg))
mask_fermi = WcsNDMap(counts.geom, mask)
mask_safe_fermi = WcsNDMap(counts.geom, np.ones(mask.shape,
dtype=bool))
log.info(f"ROI {kr}: pre-computing diffuse")
# IEM
eval_iem = MapEvaluator(
model=model_iem,
exposure=exposure,
psf=psf_map.get_psf_kernel(geom),
edisp=edisp.get_edisp_kernel(),
)
bkg_iem = eval_iem.compute_npred()
# ISO
eval_iso = MapEvaluator(model=self.model_iso,
exposure=exposure,
edisp=edisp.get_edisp_kernel())
bkg_iso = eval_iso.compute_npred()
# merge iem and iso, only one local normalization is fitted
dataset_name = "3FHL_ROI_num" + str(kr)
background_total = bkg_iem + bkg_iso
# Dataset
dataset = MapDataset(
counts=counts,
exposure=exposure,
background=background_total,
psf=psf_map,
edisp=edisp,
mask_fit=mask_fermi,
mask_safe=mask_safe_fermi,
name=dataset_name,
)
background_model = FoVBackgroundModel(dataset_name=dataset_name)
background_model.parameters["norm"].min = 0.0
# Sources model
in_roi = self.FHL3.positions.galactic.contained_by(wcs)
FHL3_roi = []
for ks in range(len(self.FHL3.table)):
if in_roi[ks] == True:
model = self.FHL3[ks].sky_model()
model.spatial_model.parameters.freeze_all() # freeze spatial
model.spectral_model.parameters["amplitude"].min = 0.0
if isinstance(model.spectral_model, PowerLawSpectralModel):
model.spectral_model.parameters["index"].min = 0.1
model.spectral_model.parameters["index"].max = 10.0
else:
model.spectral_model.parameters["alpha"].min = 0.1
model.spectral_model.parameters["alpha"].max = 10.0
FHL3_roi.append(model)
model_total = Models(FHL3_roi + [background_model])
dataset.models = model_total
cat_stat = dataset.stat_sum()
datasets = Datasets([dataset])
log.info(f"ROI {kr}: running fit")
fit = Fit(**self.fit_opts)
results = fit.run(datasets=datasets)
print("ROI_num", str(kr), "\n", results)
fit_stat = datasets.stat_sum()
if results.message != "Optimization failed.":
filedata = Path(self.resdir) / f"3FHL_ROI_num{kr}_datasets.yaml"
filemodel = Path(self.resdir) / f"3FHL_ROI_num{kr}_models.yaml"
datasets.write(filedata, filemodel, overwrite=True)
np.savez(
self.resdir / f"3FHL_ROI_num{kr}_fit_infos.npz",
message=results.message,
stat=[cat_stat, fit_stat],
)
exec_time = time() - roi_time
print("ROI", kr, " time (s): ", exec_time)
log.info(f"ROI {kr}: running flux points")
for model in FHL3_roi:
if (self.FHL3[model.name].data["ROI_num"] == kr
and self.FHL3[model.name].data["Signif_Avg"] >=
self.sig_cut):
print(model.name)
flux_points = FluxPointsEstimator(
energy_edges=self.El_flux,
source=model.name,
n_sigma_ul=2,
selection_optional=["ul"],
).run(datasets=datasets)
flux_points.meta["sqrt_ts_threshold_ul"] = 1
filename = self.resdir / f"{model.name}_flux_points.fits"
flux_points.write(filename, overwrite=True)
exec_time = time() - roi_time - exec_time
print("ROI", kr, " Flux points time (s): ", exec_time)
