Make WCSLink.as_affine_link more robust to NaN values and raise an error if no overlap

glue/plugins/wcs_autolinking/tests/test_wcs_autolinking.py

@@ -591,3 +591,29 @@ def test_wcs_no_approximation():
 
     with pytest.raises(NoAffineApproximation):
         link.as_affine_link(tolerance=0.1)
+
+
+def test_no_wcs_overlap():
+
+    wcs1 = WCS(naxis=2)
+    wcs1.wcs.ctype = 'RA---TAN', 'DEC--TAN'
+    wcs1.wcs.crval = 10, 20
+    wcs1.wcs.set()
+
+    data1 = Data(label='Data 1')
+    data1.coords = wcs1
+    data1['x'] = np.ones((2, 3))
+
+    wcs2 = WCS(naxis=2)
+    wcs2.wcs.ctype = 'RA---TAN', 'DEC--TAN'
+    wcs2.wcs.crval = 190, -20
+    wcs2.wcs.set()
+
+    data2 = Data(label='Data 2')
+    data2.coords = wcs2
+    data2['x'] = np.ones((2, 3))
+
+    link = WCSLink(data1, data2)
+
+    with pytest.raises(NoAffineApproximation, match='no overlap'):
+        link.as_affine_link()

glue/plugins/wcs_autolinking/wcs_autolinking.py

@@ -260,13 +260,28 @@ def as_affine_link(self, n_samples=1000, tolerance=1):
         # Convert to pixel positions in data2
         pixel2 = self.forwards(*pixel1)
 
+        keep = np.ones(n_samples, dtype=bool)
+        for p in pixel1 + pixel2:
+            keep[np.isnan(p)] = False
+
+        if not np.any(keep):
+            raise NoAffineApproximation(f'Could not find a good affine approximation to '
+                                        f'WCSLink with tolerance={tolerance}, as no overlap')
+
+        pixel1 = [p[keep] for p in pixel1]
+        pixel2 = [p[keep] for p in pixel2]
+
         # First try simple offset
 
         def transform_offset(offsets):
             pixel1_tr = pixel1[0] - offsets[0], pixel1[1] - offsets[1]
             return np.hypot(pixel2[0] - pixel1_tr[0], pixel2[1] - pixel1_tr[1])
 
-        best, _ = leastsq(transform_offset, (0, 0))
+        best, status = leastsq(transform_offset, (0, 0))
+
+        if status not in [1, 2, 3, 4]:
+            raise NoAffineApproximation(f'Could not find a good affine approximation to '
+                                        f'WCSLink with tolerance={tolerance}, as fitting failed')
 
         max_deviation = np.max(transform_offset(best))