Merge branch 'main' into info_fermi_catalog

examples/tutorials/analysis-1d/cta_sensitivity.py

@@ -21,6 +21,7 @@
 -  `~gammapy.estimators.SensitivityEstimator`
 
 """
+
 from cycler import cycler
 import numpy as np
 import astropy.units as u
@@ -182,13 +183,11 @@
 #
 
 
-
 fig, ax = plt.subplots()
 
 ax.set_prop_cycle(cycler("marker", "s*v") + cycler("color", "rgb"))
 
 for criterion in ("significance", "gamma", "bkg"):
-
     mask = sensitivity_table["criterion"] == criterion
     t = sensitivity_table[mask]
 
@@ -264,7 +263,9 @@
 #
 
 flux_points = FluxPoints.from_table(
-    sensitivity_table, sed_type="e2dnde", reference_model=sensitivity_estimator.spectrum
+    sensitivity_table,
+    sed_type="e2dnde",
+    reference_model=sensitivity_estimator.spectral_model,
 )
 print(
     f"Integral sensitivity in {livetime:.2f} above {energy_axis.edges[0]:.2e} "

gammapy/estimators/map/asmooth.py

@@ -12,6 +12,7 @@
 from gammapy.utils.pbar import progress_bar
 from ..core import Estimator
 from ..utils import estimate_exposure_reco_energy
+from gammapy.utils.deprecation import deprecated_renamed_argument
 
 __all__ = ["ASmoothMapEstimator"]
 
@@ -37,8 +38,8 @@ class ASmoothMapEstimator(Estimator):
         Smoothing scales.
     kernel : `astropy.convolution.Kernel`
         Smoothing kernel.
-    spectrum : `~gammapy.modeling.models.SpectralModel`
-        Spectral model assumption.
+    spectral_model : `~gammapy.modeling.models.SpectralModel`, optional
+        Spectral model assumption. Default is power-law with spectral index of 2.
     method : {'lima', 'asmooth'}
         Significance estimation method. Default is 'lima'.
     threshold : float
@@ -63,19 +64,20 @@ class ASmoothMapEstimator(Estimator):
 
     tag = "ASmoothMapEstimator"
 
+    @deprecated_renamed_argument("spectrum", "spectral_model", "v1.3")
     def __init__(
         self,
         scales=None,
         kernel=Gaussian2DKernel,
-        spectrum=None,
+        spectral_model=None,
         method="lima",
         threshold=5,
         energy_edges=None,
     ):
-        if spectrum is None:
-            spectrum = PowerLawSpectralModel()
+        if spectral_model is None:
+            spectral_model = PowerLawSpectralModel(index=2)
 
-        self.spectrum = spectrum
+        self.spectral_model = spectral_model
 
         if scales is None:
             scales = self.get_scales(n_scales=9, kernel=kernel)
@@ -231,7 +233,7 @@ def estimate_maps(self, dataset):
             background = dataset_image.background.data[0]
 
         if dataset_image.exposure is not None:
-            exposure = estimate_exposure_reco_energy(dataset_image, self.spectrum)
+            exposure = estimate_exposure_reco_energy(dataset_image, self.spectral_model)
         else:
             exposure = None
 

gammapy/estimators/map/tests/test_asmooth.py

@@ -7,6 +7,8 @@
 from gammapy.estimators import ASmoothMapEstimator
 from gammapy.maps import Map, MapAxis, WcsNDMap
 from gammapy.utils.testing import requires_data
+from gammapy.modeling.models import PowerLawSpectralModel
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 
 @pytest.fixture(scope="session")
@@ -39,6 +41,9 @@ def test_asmooth(input_dataset_simple):
     kernel = Tophat2DKernel
     scales = ASmoothMapEstimator.get_scales(3, factor=2, kernel=kernel) * 0.1 * u.deg
 
+    with pytest.warns(GammapyDeprecationWarning):
+        ASmoothMapEstimator(scales=scales, spectrum=PowerLawSpectralModel())
+
     asmooth = ASmoothMapEstimator(
         scales=scales, kernel=kernel, method="lima", threshold=2.5
     )

gammapy/estimators/points/profile.py

@@ -11,6 +11,7 @@
 from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
 from .core import FluxPoints
 from .sed import FluxPointsEstimator
+from gammapy.utils.deprecation import deprecated_renamed_argument
 
 __all__ = ["FluxProfileEstimator"]
 
@@ -22,7 +23,7 @@ class FluxProfileEstimator(FluxPointsEstimator):
     ----------
     regions : list of `~regions.SkyRegion`
         Regions to use.
-    spectrum : `~gammapy.modeling.models.SpectralModel`, optional
+    spectral_model : `~gammapy.modeling.models.SpectralModel`, optional
         Spectral model to compute the fluxes or brightness.
         Default is power-law with spectral index of 2.
     n_jobs : int, optional
@@ -89,18 +90,19 @@ class FluxProfileEstimator(FluxPointsEstimator):
 
     tag = "FluxProfileEstimator"
 
-    def __init__(self, regions, spectrum=None, **kwargs):
+    @deprecated_renamed_argument("spectrum", "spectral_model", "v1.3")
+    def __init__(self, regions, spectral_model=None, **kwargs):
         if len(regions) <= 1:
             raise ValueError(
                 "Please provide at least two regions for flux profile estimation."
             )
 
         self.regions = regions
 
-        if spectrum is None:
-            spectrum = PowerLawSpectralModel()
+        if spectral_model is None:
+            spectral_model = PowerLawSpectralModel()
 
-        self.spectrum = spectrum
+        self.spectral_model = spectral_model
         super().__init__(**kwargs)
 
     @property
@@ -170,7 +172,7 @@ def _run_region(self, datasets, region):
                 .to_region_nd_map(region, func=np.sum, weights=dataset_map.mask_safe)
                 .data
             )
-        datasets_to_fit.models = SkyModel(self.spectrum, name="test-source")
+        datasets_to_fit.models = SkyModel(self.spectral_model, name="test-source")
         estimator = self.copy()
         estimator.n_jobs = self._n_child_jobs
         return estimator._run_flux_points(datasets_to_fit)

gammapy/estimators/points/sensitivity.py

@@ -5,6 +5,7 @@
 from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
 from gammapy.stats import WStatCountsStatistic
 from ..core import Estimator
+from gammapy.utils.deprecation import deprecated_renamed_argument
 
 __all__ = ["SensitivityEstimator"]
 
@@ -20,8 +21,8 @@ class SensitivityEstimator(Estimator):
 
     Parameters
     ----------
-    spectrum : `SpectralModel`
-        Spectral model assumption. Default is Power Law with index 2.
+    spectral_model : `~gammapy.modeling.models.SpectralModel`, optional
+        Spectral model assumption. Default is power-law with spectral index of 2.
     n_sigma : float, optional
         Minimum significance. Default is 5.
     gamma_min : float, optional
@@ -37,18 +38,20 @@ class SensitivityEstimator(Estimator):
 
     tag = "SensitivityEstimator"
 
+    @deprecated_renamed_argument("spectrum", "spectral_model", "v1.3")
     def __init__(
         self,
-        spectrum=None,
+        spectral_model=None,
         n_sigma=5.0,
         gamma_min=10,
         bkg_syst_fraction=0.05,
     ):
+        if spectral_model is None:
+            spectral_model = PowerLawSpectralModel(
+                index=2, amplitude="1 cm-2 s-1 TeV-1"
+            )
 
-        if spectrum is None:
-            spectrum = PowerLawSpectralModel(index=2, amplitude="1 cm-2 s-1 TeV-1")
-
-        self.spectrum = spectrum
+        self.spectral_model = spectral_model
         self.n_sigma = n_sigma
         self.gamma_min = gamma_min
         self.bkg_syst_fraction = bkg_syst_fraction
@@ -100,12 +103,12 @@ def estimate_min_e2dnde(self, excess, dataset):
         """
         energy = dataset._geom.axes["energy"].center
 
-        dataset.models = SkyModel(spectral_model=self.spectrum)
+        dataset.models = SkyModel(spectral_model=self.spectral_model)
         npred = dataset.npred_signal()
 
         phi_0 = excess / npred
 
-        dnde_model = self.spectrum(energy=energy)
+        dnde_model = self.spectral_model(energy=energy)
         dnde = phi_0.data[:, 0, 0] * dnde_model * energy**2
         return dnde.to("erg / (cm2 s)")
 

gammapy/estimators/points/tests/test_profile.py

@@ -17,6 +17,7 @@
     make_orthogonal_rectangle_sky_regions,
 )
 from gammapy.utils.testing import requires_data, requires_dependency
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 
 def get_simple_dataset_on_off():
@@ -54,13 +55,21 @@ def test_profile_content():
     wcs = mapdataset_onoff.counts.geom.wcs
     boxes = make_horizontal_boxes(wcs)
 
+    with pytest.warns(GammapyDeprecationWarning):
+        FluxProfileEstimator(
+            regions=boxes,
+            energy_edges=[0.1, 1, 10] * u.TeV,
+            spectrum=PowerLawSpectralModel(),
+        )
+
     prof_maker = FluxProfileEstimator(
         regions=boxes,
         energy_edges=[0.1, 1, 10] * u.TeV,
         selection_optional="all",
         n_sigma=1,
         n_sigma_ul=3,
     )
+
     result = prof_maker.run(mapdataset_onoff)
 
     imp_prof = result.to_table(sed_type="flux")

gammapy/estimators/points/tests/test_sensitivity.py

@@ -5,6 +5,8 @@
 from gammapy.estimators import FluxPoints, SensitivityEstimator
 from gammapy.irf import EDispKernelMap
 from gammapy.maps import MapAxis, RegionNDMap
+from gammapy.modeling.models import PowerLawSpectralModel
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 
 @pytest.fixture()
@@ -37,6 +39,10 @@ def test_cta_sensitivity_estimator(spectrum_dataset, caplog):
         acceptance=RegionNDMap.from_geom(geom=geom, data=1),
         acceptance_off=RegionNDMap.from_geom(geom=geom, data=5),
     )
+    with pytest.warns(GammapyDeprecationWarning):
+        SensitivityEstimator(
+            gamma_min=25, bkg_syst_fraction=0.075, spectrum=PowerLawSpectralModel()
+        )
 
     sens = SensitivityEstimator(gamma_min=25, bkg_syst_fraction=0.075)
     table = sens.run(dataset_on_off)
@@ -89,7 +95,7 @@ def test_integral_estimation(spectrum_dataset, caplog):
     sens = SensitivityEstimator(gamma_min=25, bkg_syst_fraction=0.075)
     table = sens.run(dataset_on_off)
     flux_points = FluxPoints.from_table(
-        table, sed_type="e2dnde", reference_model=sens.spectrum
+        table, sed_type="e2dnde", reference_model=sens.spectral_model
     )
 
     assert_allclose(table["excess"].data.squeeze(), 270540, rtol=1e-3)

gammapy/irf/psf/kernel.py

@@ -5,6 +5,7 @@
 import matplotlib.pyplot as plt
 from gammapy.maps import Map
 from gammapy.modeling.models import PowerLawSpectralModel
+from gammapy.utils.deprecation import deprecated_renamed_argument
 
 __all__ = ["PSFKernel"]
 
@@ -161,12 +162,13 @@ def write(self, *args, **kwargs):
         """Write the Map object which contains the PSF kernel to file."""
         self.psf_kernel_map.write(*args, **kwargs)
 
-    def to_image(self, spectrum=None, exposure=None, keepdims=True):
+    @deprecated_renamed_argument("spectrum", "spectral_model", "v1.3")
+    def to_image(self, spectral_model=None, exposure=None, keepdims=True):
         """Transform 3D PSFKernel into a 2D PSFKernel.
 
         Parameters
         ----------
-        spectrum : `~gammapy.modeling.models.SpectralModel`, optional
+        spectral_model : `~gammapy.modeling.models.SpectralModel`, optional
             Spectral model to compute the weights.
             Default is power-law with spectral index of 2.
         exposure : `~astropy.units.Quantity` or `~numpy.ndarray`, optional
@@ -184,8 +186,8 @@ def to_image(self, spectrum=None, exposure=None, keepdims=True):
         """
         map = self.psf_kernel_map
 
-        if spectrum is None:
-            spectrum = PowerLawSpectralModel(index=2.0)
+        if spectral_model is None:
+            spectral_model = PowerLawSpectralModel(index=2.0)
 
         if exposure is None:
             exposure = np.ones(map.geom.axes[0].center.shape)
@@ -200,7 +202,7 @@ def to_image(self, spectrum=None, exposure=None, keepdims=True):
             if "energy" in name:
                 energy_name = name
         energy_edges = map.geom.axes[energy_name].edges
-        weights = spectrum.integral(
+        weights = spectral_model.integral(
             energy_min=energy_edges[:-1], energy_max=energy_edges[1:], intervals=True
         )
         weights *= exposure

gammapy/irf/psf/map.py

@@ -12,6 +12,7 @@
 from ..core import IRFMap
 from .core import PSF
 from .kernel import PSFKernel
+from gammapy.utils.deprecation import deprecated_renamed_argument
 
 __all__ = ["PSFMap", "RecoPSFMap"]
 
@@ -447,12 +448,13 @@ def from_gauss(cls, energy_axis_true, rad_axis=None, sigma=0.1 * u.deg, geom=Non
         )
         return cls(psf_map=psf_map, exposure_map=exposure_map)
 
-    def to_image(self, spectrum=None, keepdims=True):
+    @deprecated_renamed_argument("spectrum", "spectral_model", "v1.3")
+    def to_image(self, spectral_model=None, keepdims=True):
         """Reduce to a 2D map after weighing with the associated exposure and a spectrum.
 
         Parameters
         ----------
-        spectrum : `~gammapy.modeling.models.SpectralModel`, optional
+        spectral_model : `~gammapy.modeling.models.SpectralModel`, optional
             Spectral model to compute the weights.
             Default is power-law with spectral index of 2.
         keepdims : bool, optional
@@ -466,10 +468,10 @@ def to_image(self, spectrum=None, keepdims=True):
         """
         from gammapy.makers.utils import _map_spectrum_weight
 
-        if spectrum is None:
-            spectrum = PowerLawSpectralModel(index=2.0)
+        if spectral_model is None:
+            spectral_model = PowerLawSpectralModel(index=2.0)
 
-        exp_weighed = _map_spectrum_weight(self.exposure_map, spectrum)
+        exp_weighed = _map_spectrum_weight(self.exposure_map, spectral_model)
         exposure = exp_weighed.sum_over_axes(
             axes_names=[self.energy_name], keepdims=keepdims
         )

gammapy/irf/psf/tests/test_kernel.py

@@ -5,8 +5,9 @@
 import astropy.units as u
 from gammapy.irf import PSFKernel
 from gammapy.maps import MapAxis, WcsGeom
-from gammapy.modeling.models import DiskSpatialModel
+from gammapy.modeling.models import DiskSpatialModel, PowerLawSpectralModel
 from gammapy.utils.testing import mpl_plot_check
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 
 @pytest.fixture
@@ -51,6 +52,9 @@ def test_psf_kernel_to_image():
     kernel1 = PSFKernel.from_spatial_model(disk_1, geom, max_radius=rad_max, factor=4)
     kernel2 = PSFKernel.from_spatial_model(disk_2, geom, max_radius=rad_max, factor=4)
 
+    with pytest.warns(GammapyDeprecationWarning):
+        kernel1.to_image(spectrum=PowerLawSpectralModel())
+
     kernel1.psf_kernel_map.data[1, :, :] = kernel2.psf_kernel_map.data[1, :, :]
 
     kernel_image_1 = kernel1.to_image()

gammapy/irf/psf/tests/test_map.py

@@ -10,6 +10,8 @@
 from gammapy.makers.utils import make_map_exposure_true_energy, make_psf_map
 from gammapy.maps import Map, MapAxis, MapCoord, RegionGeom, WcsGeom
 from gammapy.utils.testing import mpl_plot_check, requires_data
+from gammapy.modeling.models import PowerLawSpectralModel
+from gammapy.utils.deprecation import GammapyDeprecationWarning
 
 
 @pytest.fixture(scope="session")
@@ -423,6 +425,9 @@ def test_psf_map_write_gtpsf(tmpdir):
 def test_to_image():
     psfmap = make_test_psfmap(0.15 * u.deg)
 
+    with pytest.warns(GammapyDeprecationWarning):
+        psfmap.to_image(spectrum=PowerLawSpectralModel())
+
     psf2D = psfmap.to_image()
     assert_allclose(psf2D.psf_map.geom.data_shape, (1, 100, 25, 25))
     assert_allclose(psf2D.exposure_map.geom.data_shape, (1, 1, 25, 25))