refactor(delay): use flatten_axes routine

draco/analysis/delay.py

@@ -641,41 +641,19 @@ def _process_data(self, ss):
             )
 
         # Find the relevant axis positions
-        data_axes = ss.datasets[self.dataset].attrs["axis"]
-        freq_axis_pos = list(data_axes).index("freq")
-        average_axis_pos = list(data_axes).index(self.average_axis)
-
-        # Create a view of the dataset with the relevant axes at the back,
-        # and all other axes compressed. End result is packed as
-        # [baseline_axis, average_axis, freq_axis].
-        data_view = np.moveaxis(
-            ss.datasets[self.dataset][:].local_array,
-            [average_axis_pos, freq_axis_pos],
-            [-2, -1],
+        data_view, bl_axes = flatten_axes(
+            ss.datasets[self.dataset], [self.average_axis, "freq"]
         )
-        data_view = data_view.reshape(-1, data_view.shape[-2], data_view.shape[-1])
-        data_view = mpiarray.MPIArray.wrap(data_view, axis=2, comm=ss.comm)
-        nbase = int(np.prod(data_view.shape[:-2]))
-        data_view = data_view.redistribute(axis=0)
-
-        # ... do the same for the weights, but we also need to make the weights full
-        # size
-        weight_full = np.zeros(
-            ss.datasets[self.dataset][:].shape, dtype=ss.weight.dtype
+        weight_view, _ = flatten_axes(
+            ss.weight,
+            [self.average_axis, "freq"],
+            match_dset=ss.datasets[self.dataset],
         )
-        weight_full[:] = match_axes(ss.datasets[self.dataset], ss.weight)
-        weight_view = np.moveaxis(
-            weight_full, [average_axis_pos, freq_axis_pos], [-2, -1]
-        )
-        weight_view = weight_view.reshape(
-            -1, weight_view.shape[-2], weight_view.shape[-1]
-        )
-        weight_view = mpiarray.MPIArray.wrap(weight_view, axis=2, comm=ss.comm)
-        weight_view = weight_view.redistribute(axis=0)
 
         # Use the "baselines" axis to generically represent all the other axes
 
         # Initialise the spectrum container
+        nbase = data_view.global_shape[0]
         if self.output_power_spectrum:
             delay_spec = containers.DelaySpectrum(
                 baseline=nbase,
@@ -692,7 +670,6 @@ def _process_data(self, ss):
             )
         delay_spec.redistribute("baseline")
         delay_spec.spectrum[:] = 0.0
-        bl_axes = [da for da in data_axes if da not in [self.average_axis, "freq"]]
 
         # Copy the index maps for all the flattened axes into the output container, and
         # write out their order into an attribute so we can reconstruct this easily