Fix NaN handling in cube fitting

CHANGES.rst

@@ -142,7 +142,6 @@ Cubeviz
 
 - No longer incorrectly swap RA and Dec axes when loading Spectrum1D objects. [#3133]
 
-
 Imviz
 ^^^^^
 
@@ -239,6 +238,8 @@ Bug Fixes
 Cubeviz
 ^^^^^^^
 
+- Fixed fitting a model to the entire cube when NaNs are present. [#3191]
+
 Imviz
 ^^^^^
 

jdaviz/app.py

@@ -735,8 +735,9 @@ def _link_new_data(self, reference_data=None, data_to_be_linked=None):
             return
 
         elif self.config == 'cubeviz' and linked_data.ndim == 1:
-            ref_wavelength_component = dc[0].components[-2]
-            ref_flux_component = dc[0].components[-1]
+            # Don't want to use negative indices in case there are extra components like a mask
+            ref_wavelength_component = dc[0].components[5]
+            ref_flux_component = dc[0].components[6]
             linked_wavelength_component = dc[-1].components[1]
             linked_flux_component = dc[-1].components[-1]
 

jdaviz/configs/cubeviz/plugins/spectral_extraction/spectral_extraction.py

@@ -471,6 +471,9 @@ def _extract_from_aperture(self, cube, uncert_cube, aperture,
             wcs = cube.meta['_orig_spec'].wcs.spectral
         elif hasattr(cube.coords, 'spectral'):
             wcs = cube.coords.spectral
+        elif hasattr(cube.coords, 'spectral_wcs'):
+            # This is the attribute for a PaddedSpectrumWCS in the 3D case
+            wcs = cube.coords.spectral_wcs
         else:
             wcs = None
 

jdaviz/configs/default/plugins/model_fitting/initializers.py

@@ -119,7 +119,7 @@ def initialize(self, instance, x, y):
         instance: `~astropy.modeling.Model`
             The initialized model.
         """
-        y_mean = np.mean(y)
+        y_mean = np.nanmean(y)
         x_range = x[-1] - x[0]
         position = x_range / 2.0 + x[0]
 
@@ -190,7 +190,7 @@ def initialize(self, instance, x, y):
 
         # width can be estimated by the weighted
         # 2nd moment of the X coordinate.
-        dx = x - np.mean(x)
+        dx = x - np.nanmean(x)
         fwhm = 2 * np.sqrt(np.sum((dx * dx) * y) / np.sum(y))
 
         # amplitude is derived from area.

jdaviz/configs/default/plugins/model_fitting/model_fitting.py

@@ -493,7 +493,7 @@ def _initialize_model_component(self, model_comp, comp_label, poly_order=None):
             masked_spectrum.flux[~mask] if mask is not None else masked_spectrum.flux)
 
         # need to loop over parameters again as the initializer may have overridden
-        # the original default value
+        # the original default value.
         for param_name in get_model_parameters(model_cls, new_model["model_kwargs"]):
             param_quant = getattr(initialized_model, param_name)
             new_model["parameters"].append({"name": param_name,
@@ -917,6 +917,12 @@ def _fit_model_to_cube(self, add_data):
 
         # Apply masks from selected spectral subset
         spec = self._apply_subset_masks(spec, self.spectral_subset)
+        # Also mask out NaNs for fitting. Simply adding filter_non_finite to the cube fit
+        # didn't work out of the box, so doing this for now.
+        if spec.mask is None:
+            spec.mask = np.isnan(spec.flux)
+        else:
+            spec.mask = spec.mask | np.isnan(spec.flux)
 
         try:
             fitted_model, fitted_spectrum = fit_model_to_spectrum(

jdaviz/configs/default/plugins/model_fitting/tests/test_fitting.py

@@ -8,6 +8,7 @@
 from astropy.nddata import StdDevUncertainty
 from astropy.tests.helper import assert_quantity_allclose
 from astropy.wcs import WCS
+from glue.core.roi import XRangeROI
 from numpy.testing import assert_allclose, assert_array_equal
 from specutils.spectra import Spectrum1D
 
@@ -378,3 +379,41 @@ def test_incompatible_units(specviz_helper, spectrum1d):
     with warnings.catch_warnings():
         warnings.filterwarnings('ignore', message='Model is linear in parameters.*')
         mf.calculate_fit(add_data=True)
+
+
+def test_cube_fit_with_nans(cubeviz_helper):
+    flux = np.ones((7, 8, 9)) * u.nJy
+    flux[:, :, 0] = np.nan
+    spec = Spectrum1D(flux=flux)
+    cubeviz_helper.load_data(spec, data_label="test")
+
+    mf = cubeviz_helper.plugins["Model Fitting"]
+    mf.cube_fit = True
+    mf.create_model_component("Const1D")
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', message='Model is linear in parameters.*')
+        mf.calculate_fit()
+    result = cubeviz_helper.app.data_collection['model']
+    assert np.all(result.get_component("flux").data == 1)
+
+
+def test_cube_fit_with_subset_and_nans(cubeviz_helper):
+    # Also test with existing mask
+    flux = np.ones((7, 8, 9)) * u.nJy
+    flux[:, :, 0] = np.nan
+    spec = Spectrum1D(flux=flux)
+    spec.flux[5, 5, 7] = 10 * u.nJy
+    cubeviz_helper.load_data(spec, data_label="test")
+
+    sv = cubeviz_helper.app.get_viewer('spectrum-viewer')
+    sv.apply_roi(XRangeROI(0, 5))
+
+    mf = cubeviz_helper.plugins["Model Fitting"]
+    mf.cube_fit = True
+    mf.spectral_subset = 'Subset 1'
+    mf.create_model_component("Const1D")
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', message='Model is linear in parameters.*')
+        mf.calculate_fit()
+    result = cubeviz_helper.app.data_collection['model']
+    assert np.all(result.get_component("flux").data == 1)