feat: add ability to compute sim inpainting on nonavg data

hera_cal/lst_stack/averaging.py

@@ -300,7 +300,7 @@ def reduce_lst_bins(
         LST bin, in addition to the mean and standard deviation.
     get_std
         Whether to compute the standard deviation of the data in each LST bin.
-    fill_value
+    mean_fill_value
         The value to use for the mean when there are no samples. This must be either
         nan, zero or inf.
 
@@ -703,6 +703,13 @@ def average_and_inpaint_simultaneously(
     lstavg = reduce_lst_bins(
         stack, get_std=False, get_mad=False, inpainted_mode=False, mean_fill_value=0.0
     )
+    # Trick the LST-binner into performing the average over autopairs instead of LSTs
+    auto_redavg = reduce_lst_bins(
+        data=auto_stack.data.transpose((1, 0, 2, 3)),
+        flags=auto_stack.flags.transpose((1, 0, 2, 3)),
+        nsamples=auto_stack.nsamples.transpose((1, 0, 2, 3)),
+        get_std=False, get_mad=False, inpainted_mode=False, mean_fill_value=0.0
+    )
 
     # Map antenna polarizations to visibility pol indices for correct noise variance computation
     # even for cross-polarized visibilities, which use the auto-polarized autocorrelations.
@@ -712,12 +719,6 @@ def average_and_inpaint_simultaneously(
         if antpol1 == antpol2:
             antpol_to_vispol_idx[antpol1] = polidx
 
-    # Compute noise variance
-    if auto_stack.data.shape[1] != 1:
-        raise NotImplementedError(
-            "This code only works with redundantly averaged data, which has only one unique auto per polarization"
-        )
-
     for iap, antpair in enumerate(stack.antpairs):
         # Get the baseline vector and length
         bl_vec = (antpos[antpair[1]] - antpos[antpair[0]])[:]
@@ -742,11 +743,13 @@ def average_and_inpaint_simultaneously(
             avg_flgs = lstavg["flags"][iap, :, polidx]
 
             antpol1, antpol2 = utils.split_pol(pol)
+
             # Compute noise variance for all days in stack
             base_noise_var = (
-                np.abs(auto_stack.data[:, 0, :, antpol_to_vispol_idx[antpol1]] *
-                       auto_stack.data[:, 0, :, antpol_to_vispol_idx[antpol2]])
-                / (stack.dt * stack.df).value
+                np.abs(
+                    auto_redavg['data'][..., antpol_to_vispol_idx[antpol1]] *
+                    auto_redavg['data'][..., antpol_to_vispol_idx[antpol2]]
+                ) / (stack.dt * stack.df).value
             )
 
             # Shortcut early if there are no flags in the stack. In that case,

hera_cal/lst_stack/tests/test_averaging.py

@@ -828,9 +828,6 @@ def test_nonred_data(self):
             time_axis_faster_than_bls=False,
         )
         auto_stack = LSTStack(auto_uvd)
-
-        with pytest.raises(
-            NotImplementedError,
-            match="This code only works with redundantly averaged data",
-        ):
-            avg.average_and_inpaint_simultaneously(self.stack, auto_stack)
+        lstavg, _ = avg.average_and_inpaint_simultaneously(self.stack, auto_stack)
+        assert not np.any(np.isnan(lstavg['data']))
+        np.testing.assert_allclose(lstavg["data"], self.stack.data[0])