diff --git a/ebcc/codegen/bootstrap_DFDCD.py b/ebcc/codegen/bootstrap_DFDCD.py
index 4e49f1cd..5e5b2520 100644
--- a/ebcc/codegen/bootstrap_DFDCD.py
+++ b/ebcc/codegen/bootstrap_DFDCD.py
@@ -38,8 +38,8 @@
     # Get the energy contractions in pdaggerq format
     pq.clear()
     pq.set_left_operators([["1"]])
-    pq.add_st_operator(1.0, ["f"], ["t1", "t2"])
-    pq.add_st_operator(1.0, ["v"], ["t1", "t2"])
+    pq.add_st_operator(1.0, ["f"], ["t2"])
+    pq.add_st_operator(1.0, ["v"], ["t2"])
     pq.simplify()
     terms = pq.fully_contracted_strings()
     terms = remove_reference_energy(terms)
diff --git a/ebcc/codegen/bootstrap_DFDCSD.py b/ebcc/codegen/bootstrap_DFDCSD.py
index 7cca030a..19dd9d21 100644
--- a/ebcc/codegen/bootstrap_DFDCSD.py
+++ b/ebcc/codegen/bootstrap_DFDCSD.py
@@ -81,7 +81,7 @@
     ]
 
     # Get the T amplitudes in albert format
-    output_expr, returns = get_amplitudes([terms], spin, orders=[2], density_fit=True)
+    output_expr, returns = get_amplitudes([terms_t1, terms_t2], spin, density_fit=True)
 
     # Generate the T amplitude code
     for name, codegen in code_generators.items():
diff --git a/ebcc/codegen/bootstrap_MPn.py b/ebcc/codegen/bootstrap_MPn.py
index 96a5d131..3b737095 100644
--- a/ebcc/codegen/bootstrap_MPn.py
+++ b/ebcc/codegen/bootstrap_MPn.py
@@ -140,7 +140,7 @@ def postamble():
                         if tuple(key) in {("o", "o", "v", "v") , ("v", "v", "o", "o")}:
                             continue
                         i, j, k, l = ["ov".index(k) for k in key]
-                        codegen.write(f"rdm2.{key} = np.zeros((t1.shape[{i}], t1.shape[{j}], t1.shape[{k}], t1.shape[{l}]))")
+                        codegen.write(f"rdm2.{''.join(key)} = np.zeros((t1.shape[{i}], t1.shape[{j}], t1.shape[{k}], t1.shape[{l}]))")
                     codegen.write("rdm2 = pack_2e(rdm2.oooo, rdm2.ooov, rdm2.oovo, rdm2.ovoo, rdm2.vooo, rdm2.oovv, rdm2.ovov, rdm2.ovvo, rdm2.voov, rdm2.vovo, rdm2.vvoo, rdm2.ovvv, rdm2.vovv, rdm2.vvov, rdm2.vvvo, rdm2.vvvv).transpose((0, 2, 1, 3))")
                 else:
                     for s in ("aaaa", "aabb", "bbbb"):
@@ -149,7 +149,7 @@ def postamble():
                                 continue
                             i, j, k, l = ["ov".index(k) for k in key]
                             si, sj, sk, sl = s
-                            codegen.write(f"rdm2.{si+sj+sk+sl}.{key} = np.zeros((t1.{si}.shape[{i}], t1.{sj}.shape[{j}], t1.{sk}.shape[{k}], t1.{sl}.shape[{l}]))")
+                            codegen.write(f"rdm2.{si+sj+sk+sl}.{''.join(key)} = np.zeros((t1.{si}.shape[{i}], t1.{sj}.shape[{j}], t1.{sk}.shape[{k}], t1.{sl}.shape[{l}]))")
                     codegen.write("rdm2.aaaa = pack_2e(rdm2.aaaa.oooo, rdm2.aaaa.ooov, rdm2.aaaa.oovo, rdm2.aaaa.ovoo, rdm2.aaaa.vooo, rdm2.aaaa.oovv, rdm2.aaaa.ovov, rdm2.aaaa.ovvo, rdm2.aaaa.voov, rdm2.aaaa.vovo, rdm2.aaaa.vvoo, rdm2.aaaa.ovvv, rdm2.aaaa.vovv, rdm2.aaaa.vvov, rdm2.aaaa.vvvo, rdm2.aaaa.vvvv).transpose((0, 2, 1, 3))")
                     codegen.write("rdm2.aabb = pack_2e(rdm2.abab.oooo, rdm2.abab.ooov, rdm2.abab.oovo, rdm2.abab.ovoo, rdm2.abab.vooo, rdm2.abab.oovv, rdm2.abab.ovov, rdm2.abab.ovvo, rdm2.abab.voov, rdm2.abab.vovo, rdm2.abab.vvoo, rdm2.abab.ovvv, rdm2.abab.vovv, rdm2.abab.vvov, rdm2.abab.vvvo, rdm2.abab.vvvv).transpose((0, 2, 1, 3))")
                     codegen.write("rdm2.bbbb = pack_2e(rdm2.bbbb.oooo, rdm2.bbbb.ooov, rdm2.bbbb.oovo, rdm2.bbbb.ovoo, rdm2.bbbb.vooo, rdm2.bbbb.oovv, rdm2.bbbb.ovov, rdm2.bbbb.ovvo, rdm2.bbbb.voov, rdm2.bbbb.vovo, rdm2.bbbb.vvoo, rdm2.bbbb.ovvv, rdm2.bbbb.vovv, rdm2.bbbb.vvov, rdm2.bbbb.vvvo, rdm2.bbbb.vvvv).transpose((0, 2, 1, 3))")
diff --git a/ebcc/codegen/bootstrap_common.py b/ebcc/codegen/bootstrap_common.py
index 9322e777..1fbbcbf3 100644
--- a/ebcc/codegen/bootstrap_common.py
+++ b/ebcc/codegen/bootstrap_common.py
@@ -47,6 +47,8 @@ def get_amplitudes(terms_grouped, spin, strategy="exhaust", which="t", orders=No
         for index_spins in get_amplitude_spins(indices[: n + 1], indices[n + 1 :], spin):
             expr_n = import_from_pdaggerq(terms, index_spins=index_spins)
             expr_n = spin_integrate(expr_n, spin)
+            if density_fit:
+                expr_n = tuple(_density_fit(e) for e in expr_n)
             output_n = [Tensor(*tuple(sorted(e.external_indices, key=lambda i: indices.index(i.name))), name=f"{which}{order}new") for e in expr_n]
             returns_n = (output_n[0],)
             expr.extend(expr_n)