diff --git a/pyerrors/obs.py b/pyerrors/obs.py
index ea10e578..f7029a63 100644
--- a/pyerrors/obs.py
+++ b/pyerrors/obs.py
@@ -1146,14 +1146,13 @@ def derived_observable(func, data, array_mode=False, **kwargs):
 
     final_result = np.zeros(new_values.shape, dtype=object)
 
-    # TODO: array mode does not work when matrices are defined on differenet ensembles
     if array_mode is True:
 
-        class Zero_grad():
+        class _Zero_grad():
             def __init__(self):
                 self.grad = 0
 
-        zero_grad = Zero_grad()
+        zero_grad = _Zero_grad()
 
         d_extracted = {}
         g_extracted = {}
@@ -1161,7 +1160,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
             d_extracted[name] = []
             for i_dat, dat in enumerate(data):
                 ens_length = len(new_idl_d[name])
-                d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas[name], o.idl[name], o.shape[name], new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
+                d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas.get(name, np.zeros(ens_length)), o.idl.get(name, new_idl_d[name]), o.shape.get(name, ens_length), new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
         for name in new_cov_names:
             g_extracted[name] = []
             for i_dat, dat in enumerate(data):
diff --git a/tests/linalg_test.py b/tests/linalg_test.py
index bdbce655..73bcf5bb 100644
--- a/tests/linalg_test.py
+++ b/tests/linalg_test.py
@@ -29,10 +29,10 @@ def get_complex_matrix(dimension):
 
 
 def test_matmul():
-    for dim in [4, 8]:
+    for dim in [4, 6]:
         for const in [1, pe.cov_Obs(1.0, 0.002, 'cov')]:
             my_list = []
-            length = 1000 + np.random.randint(200)
+            length = 100 + np.random.randint(200)
             for i in range(dim ** 2):
                 my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
             my_array = const * np.array(my_list).reshape((dim, dim))
@@ -41,7 +41,7 @@ def test_matmul():
                 assert e.is_zero(), t
 
             my_list = []
-            length = 1000 + np.random.randint(200)
+            length = 100 + np.random.randint(200)
             for i in range(dim ** 2):
                 my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
                                        pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
@@ -189,7 +189,7 @@ def test_matmul_irregular_histories():
     standard_array = []
     for i in range(dim ** 2):
         standard_array.append(pe.Obs([np.random.normal(1.1, 0.2, length)], ['ens1']))
-    standard_matrix = np.array(standard_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov')  # * pe.pseudo_Obs(0.1, 0.002, 'qr')
+    standard_matrix = np.array(standard_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(0.1, 0.002, 'qr')
 
     for idl in [range(1, 501, 2), range(250, 273), [2, 8, 19, 20, 78]]:
         irregular_array = []