Propagate covariance matrix to the spatial and spectral nodel in SkyM…

…odel

gammapy/cube/models.py

@@ -152,11 +152,19 @@ def __init__(self, spatial_model, spectral_model, name="SkyModel"):
     @property
     def spatial_model(self):
         """`~gammapy.image.models.SkySpatialModel`"""
+        # propagate sub-covariance
+        if self.parameters.covariance is not None:
+            idx = len(self._spatial_model.parameters.parameters)
+            self._spatial_model.parameters.covariance = self.parameters.covariance[:idx, :idx]
         return self._spatial_model
 
     @property
     def spectral_model(self):
         """`~gammapy.spectrum.models.SpectralModel`"""
+        # propagate sub-covariance
+        if self.parameters.covariance is not None:
+            idx = len(self._spatial_model.parameters.parameters)
+            self._spectral_model.parameters.covariance = self.parameters.covariance[idx:, idx:]
         return self._spectral_model
 
     @property

gammapy/cube/tests/test_fit.py

@@ -125,3 +125,6 @@ def test_cube_fit(sky_model, counts, exposure, psf, background, mask, edisp):
 
     assert_allclose(pars["amplitude"].value, 1e-11, rtol=1e-2)
     assert_allclose(pars.error("amplitude"), 3.936e-13, rtol=1e-2)
+
+    assert result.model.spectral_model.covariance is not None
+    assert result.model.spatial_model.covariance is not None

gammapy/utils/fitting/parameter.py

@@ -309,6 +309,7 @@ def covariance_to_table(self):
         for idx, par in enumerate(self.parameters):
             vals = self.covariance[idx]
             table[par.name] = vals
+            table[par.name].format = ".3e"
         return table
 
     @property