Horne extraction fix (#256)

Author: hpparvi

CHANGES.rst

@@ -13,7 +13,18 @@ Bug Fixes
 Other changes
 ^^^^^^^^^^^^^
 
-1.5.0 (2024-01-16)
+
+1.5.1 (2024-03-08)
+------------------
+
+Bug Fixes
+^^^^^^^^^
+
+- Changed Horne extraction to behave as before when using an interpolated spatial profile
+  and not explicitly setting `bkgrd_prof` to `None`. The changed default behavior in 1.5.0
+  caused problems in codes using specreduce.
+
+1.5.0 (2024-03-06)
 ------------------
 
 New Features

specreduce/extract.py

@@ -299,9 +299,10 @@ class HorneExtract(SpecreduceOperation):
         The index of the image's cross-dispersion axis. [default: 0]
 
     bkgrd_prof : `~astropy.modeling.Model` or None, optional
-        A model for the image's background flux. If ``spatial_profile`` is set
-        to ``interpolated_profile``, then ``bkgrd_prof`` must be set to None.
-        [default: models.Polynomial1D(2)].
+        A model for the image's background flux when using the ``gaussian``
+        spatial profile. If ``spatial_profile`` is set to ``gaussian``, it defaults
+        to ``models.Polynomial1D(2)``. Note that the ``interpolated_profile`` option
+        does not support a background model, so ``bkgrd_prof`` must be left as ``None``.
 
     spatial_profile : str or dict, optional
         The shape of the object profile. The first option is 'gaussian' to fit
@@ -341,7 +342,7 @@ class HorneExtract(SpecreduceOperation):
 
     image: NDData
     trace_object: Trace
-    bkgrd_prof: Model = field(default=models.Polynomial1D(2))
+    bkgrd_prof: None | Model = None
     spatial_profile: str | dict = "gaussian"
     variance: np.ndarray = field(default=None)
     mask: np.ndarray = field(default=None)
@@ -599,7 +600,10 @@ def __call__(
             The index of the image's cross-dispersion axis.
 
         bkgrd_prof : `~astropy.modeling.Model`, optional
-            A model for the image's background flux.
+            A model for the image's background flux when using the ``gaussian``
+            spatial profile. If ``spatial_profile`` is set to ``gaussian``, it defaults
+            to ``models.Polynomial1D(2)``. Note that the ``interpolated_profile`` option
+            does not support a background model, so ``bkgrd_prof`` must be left as ``None``.
 
         spatial_profile : str or dict, optional
             The shape of the object profile. The first option is 'gaussian' to fit
@@ -665,6 +669,9 @@ def __call__(
         n_bins_interpolated_profile = profile.get("n_bins_interpolated_profile", 10)
         interp_degree_interpolated_profile = profile.get("interp_degree_interpolated_profile", 1)
 
+        if bkgrd_prof is None and profile_type == 'gaussian':
+            bkgrd_prof = models.Polynomial1D(2)
+
         self.image = self._parse_image(image, variance, mask, unit, disp_axis)
         variance = self.image.uncertainty.represent_as(VarianceUncertainty).array
         mask = self.image.mask.astype(bool) | (~np.isfinite(self.image.data))
@@ -690,16 +697,6 @@ def __call__(
             else:
                 mean_cross_pix = np.broadcast_to(ncross // 2, ndisp)
         else:  # interpolated_profile
-            # for now, bkgrd_prof must be None because a compound model can't
-            # be created with a interpolator + model. i think there is a way
-            # around this, but will follow up later
-            if bkgrd_prof is not None:
-                raise ValueError(
-                    "When `spatial_profile`is `interpolated_profile`,"
-                    "`bkgrd_prof` must be None. Background should"
-                    " be fit and subtracted from `img` beforehand."
-                )
-
             # determine interpolation degree from input and make tuple if int
             # this can also be moved to another method to parse the input
             # 'spatial_profile' arg, eventually

specreduce/tests/test_extract.py

@@ -314,14 +314,12 @@ def test_horne_interpolated_profile(mk_test_img):
 
     assert_quantity_allclose(horne_extract_gauss.spectrum.flux, horne_extract_self.spectrum.flux)
 
-    with pytest.raises(ValueError, match="When"):
-        HorneExtract(
-            image.data,
-            trace,
-            spatial_profile={"name": "interpolated_profile", "n_bins_interpolated_profile": 3},
-            bkgrd_prof=models.Polynomial1D(2),
-            variance=np.ones(image.data.shape),
-        ).spectrum
+    horne_extract_self = HorneExtract(
+        image.data,
+        trace,
+        spatial_profile={"name": "interpolated_profile", "n_bins_interpolated_profile": 3},
+        variance=np.ones(image.data.shape),
+    )
 
 
 def test_horne_interpolated_profile_norm(mk_test_img):