diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py
index 07e47e0cf6..bc9167dfd1 100644
--- a/openmc/mgxs/mgxs.py
+++ b/openmc/mgxs/mgxs.py
@@ -614,7 +614,7 @@ class MultiGroupXS(object):
 
             # Sum across all applicable fine energy group filters
             for i, filter in enumerate(tally.filters):
-                if 'energy' in filter.type and all(filter.bins == fine_edges):
+                if 'energy' in filter.type and np.all(filter.bins == fine_edges):
                     filter.bins = coarse_groups.group_edges
                     mean = np.add.reduceat(mean, energy_indices, axis=i)
                     std_dev = np.add.reduceat(std_dev**2, energy_indices, axis=i)
@@ -1734,9 +1734,12 @@ class Chi(MultiGroupXS):
         nu_fission_out = self.tallies['nu-fission-out']
 
         # Remove the coarse energy filter to keep it out of tally arithmetic
-        nu_fission_in.remove_filter(nu_fission_in.filters[-1])
+        energy_filter = nu_fission_in.find_filter('energy')
+        nu_fission_in.remove_filter(energy_filter)
 
         # Compute chi
+        nu_fission_in.add_filter(energy_filter)
+
         self._xs_tally = nu_fission_out / nu_fission_in
         super(Chi, self).compute_xs()
 
diff --git a/openmc/tallies.py b/openmc/tallies.py
index 87af32c428..d15be79833 100644
--- a/openmc/tallies.py
+++ b/openmc/tallies.py
@@ -2506,20 +2506,27 @@ class Tally(object):
         num_score_bins = new_tally.num_score_bins
         new_shape = (num_filter_bins, num_nuclides, num_score_bins)
 
+        diag_factor = self.num_filter_bins / new_filter.num_bins
         indices = np.arange(0, new_filter.num_bins**2, new_filter.num_bins+1)
+        diag_indices = np.zeros(self.num_filter_bins, dtype=np.int)
+
+        for i in range(diag_factor):
+            start = i * new_filter.num_bins
+            end = (i+1) * new_filter.num_bins
+            diag_indices[start:end] = indices + (i * new_filter.num_bins**2)
 
         if self.sum is not None:
             new_tally._sum = np.zeros(new_shape, dtype=np.float64)
-            new_tally._sum[indices, :self.num_nuclides, :self.num_scores] = self.sum
+            new_tally._sum[diag_indices, :self.num_nuclides, :self.num_scores] = self.sum
         if self.sum_sq is not None:
             new_tally._sum_sq = np.zeros(new_shape, dtype=np.float64)
-            new_tally._sum_sq[indices, :self.num_nuclides, :self.num_scores] = self.sum_sq
+            new_tally._sum_sq[diag_indices, :self.num_nuclides, :self.num_scores] = self.sum_sq
         if self.mean is not None:
             new_tally._mean = np.zeros(new_shape, dtype=np.float64)
-            new_tally._mean[indices, :self.num_nuclides, :self.num_scores] = self.mean
+            new_tally._mean[diag_indices, :self.num_nuclides, :self.num_scores] = self.mean
         if self.std_dev is not None:
             new_tally._std_dev = np.zeros(new_shape, dtype=np.float64)
-            new_tally._std_dev[indices, :self.num_nuclides, :self.num_scores] = self.std_dev
+            new_tally._std_dev[diag_indices, :self.num_nuclides, :self.num_scores] = self.std_dev
 
         # Correct each Filter's stride
         stride = new_tally.num_nuclides * new_tally.num_score_bins