fix: include resolution in stitched curve if present

src/ess/reflectometry/tools.py

@@ -229,7 +229,7 @@ def cost(scaling_factors):
 
 def combine_curves(
     curves: Sequence[sc.DataArray],
-    q_bin_edges: sc.Variable | None = None,
+    q_bin_edges: sc.Variable,
 ) -> sc.DataArray:
     '''Combines the given curves by interpolating them
     on a 1d grid defined by :code:`q_bin_edges` and averaging
@@ -261,22 +261,42 @@ def combine_curves(
     if len({c.coords['Q'].unit for c in curves}) != 1:
         raise ValueError('The Q-coordinates must have the same unit for each curve')
 
-    r = _interpolate_on_qgrid(map(sc.values, curves), q_bin_edges).values
-    v = _interpolate_on_qgrid(map(sc.variances, curves), q_bin_edges).values
+    r = _interpolate_on_qgrid(map(sc.values, curves), q_bin_edges)
+    v = _interpolate_on_qgrid(map(sc.variances, curves), q_bin_edges)
 
-    v[v == 0] = np.nan
+    v = sc.where(v == 0, sc.scalar(np.nan, unit=v.unit), v)
     inv_v = 1.0 / v
-    r_avg = np.nansum(r * inv_v, axis=0) / np.nansum(inv_v, axis=0)
-    v_avg = 1 / np.nansum(inv_v, axis=0)
-    return sc.DataArray(
+    r_avg = sc.nansum(r * inv_v, dim='curves') / sc.nansum(inv_v, dim='curves')
+    v_avg = 1 / sc.nansum(inv_v, dim='curves')
+
+    out = sc.DataArray(
         data=sc.array(
             dims='Q',
-            values=r_avg,
-            variances=v_avg,
+            values=r_avg.values,
+            variances=v_avg.values,
             unit=next(iter(curves)).data.unit,
         ),
         coords={'Q': q_bin_edges},
     )
+    if any('Q_resolution' in c.coords for c in curves):
+        # This might need to be revisited. The question about how to combine curves
+        # with different Q-resolution is not completely resolved.
+        # However, in practice the difference in Q-resolution between different curves
+        # is small so it's not likely to make a big difference.
+        q_res = (
+            sc.DataArray(
+                data=c.coords.get(
+                    'Q_resolution', sc.full_like(c.coords['Q'], value=np.nan)
+                ),
+                coords={'Q': c.coords['Q']},
+            )
+            for c in curves
+        )
+        qs = _interpolate_on_qgrid(q_res, q_bin_edges)
+        out.coords['Q_resolution'] = sc.nansum(qs * inv_v, dim='curves') / sc.nansum(
+            sc.where(sc.isnan(qs), sc.scalar(0.0, unit=inv_v.unit), inv_v), dim='curves'
+        )
+    return out
 
 
 def orso_datasets_from_measurements(

tests/tools_test.py

@@ -146,6 +146,29 @@ def test_combined_curves():
     )
 
 
+@pytest.mark.filterwarnings("ignore:invalid value encountered in divide")
+def test_combined_curves_resolution():
+    qgrid = sc.linspace('Q', 0, 1, 26)
+    data = sc.concat(
+        (
+            sc.ones(dims=['Q'], shape=[10], with_variances=True),
+            0.5 * sc.ones(dims=['Q'], shape=[15], with_variances=True),
+        ),
+        dim='Q',
+    )
+    data.variances[:] = 0.1
+    curves = (
+        curve(data, 0, 0.3),
+        curve(0.5 * data, 0.2, 0.7),
+        curve(0.25 * data, 0.6, 1.0),
+    )
+    curves[0].coords['Q_resolution'] = sc.midpoints(curves[0].coords['Q']) / 5
+    combined = combine_curves(curves, qgrid)
+    assert 'Q_resolution' in combined.coords
+    assert combined.coords['Q_resolution'][0] == curves[0].coords['Q_resolution'][1]
+    assert sc.isnan(combined.coords['Q_resolution'][-1])
+
+
 def test_linlogspace_linear():
     q_lin = linlogspace(
         dim='qz', edges=[0.008, 0.08], scale='linear', num=50, unit='1/angstrom'