hooked up the interpolation and tested

src/powerbox/tools.py

@@ -206,7 +206,8 @@ def _field_average_interpolate(coords, field, bins, weights, angular_resolution=
             [
                 np.round(2 * np.pi * bins / angular_resolution),
                 np.ones_like(bins) / angular_resolution,
-            ]
+            ],
+            axis=0,
         ),
         dtype=int,
     )
@@ -307,6 +308,7 @@ def angular_average_nd(
     get_variance=False,
     log_bins=False,
     interpolation_method=None,
+    angular_resolution=0.1,
 ):
     """
     Average the first n dimensions of a given field within radial bins.
@@ -326,7 +328,7 @@ def angular_average_nd(
         An array of arbitrary dimension specifying the field to be angularly averaged.
 
     coords : list of n arrays
-        A list of 1D arrays specifying the co-ordinates in each dimension *to be average*.
+        A list of 1D arrays specifying the co-ordinates in each dimension *to be averaged*.
 
     bins : int or array.
         Specifies the radial bins for the averaged dimensions. Can be an int or array specifying radial bin edges.
@@ -394,7 +396,7 @@ def angular_average_nd(
     if len(coords) != len(field.shape):
         raise ValueError("coords should be a list of arrays, one for each dimension.")
 
-    if n == len(coords):
+    if n == len(coords) and interpolation_method is None:
         return angular_average(
             field,
             coords,
@@ -406,10 +408,10 @@ def angular_average_nd(
             log_bins=log_bins,
         )
 
-    coords = _magnitude_grid([c for i, c in enumerate(coords) if i < n])
+    coords_grid = _magnitude_grid([c for i, c in enumerate(coords) if i < n])
 
     indx, bins, sumweights = _get_binweights(
-        coords, weights, bins, average, bin_ave=bin_ave, log_bins=log_bins
+        coords_grid, weights, bins, average, bin_ave=bin_ave, log_bins=log_bins
     )
 
     n1 = np.prod(field.shape[:n])
@@ -429,7 +431,13 @@ def angular_average_nd(
             if get_variance:
                 var[:, i] = _field_variance(indx, fld, res[:, i], w, sumweights)
         elif interpolation_method == "linear":
-            res[:, i], sumweights = _field_average_interpolate(coords, fld, bins, w)
+            res[:, i], sumweights = _field_average_interpolate(
+                np.array(coords)[:n, ...],
+                fld.reshape(field.shape[:n]),
+                bins,
+                w,
+                angular_resolution=angular_resolution,
+            )
             if get_variance:
                 # TODO: Implement variance calculation for interpolation
                 var[:, i] = np.zeros_like(res[:, i])