diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 0873d3c07cf4e4348e93704b700d86cfee5ee459..d67e3529fcc6eec98b63e390d905722faeb719e3 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -374,7 +374,7 @@ class PostProcessing:
         for plotidx, plot in enumerate(plot_list):
             fig, ax = plt.subplots()
             seq_dict = seq_list[plotidx]
-            name_util = list(seq_dict.keys())
+            name_util = list(seq_dict.keys())  # TODO: same as engine.out_names?
             if len(name_util) == 0:
                 continue
 
diff --git a/tests/test_PostProcessing.py b/tests/test_PostProcessing.py
index 205cbde579833f9e5fa5bf2f412736e1f7a6d43c..4867a34b7f531f83dff1b41834920776d2cbc36b 100644
--- a/tests/test_PostProcessing.py
+++ b/tests/test_PostProcessing.py
@@ -94,7 +94,11 @@ def basic_engine_sequential():
     mod = PL()
     
     expdes = ExpDesigns(inp)
-    expdes.n_samples = 3
+    expdes.n_init_samples = 4
+    expdes.n_max_samples = 4
+    expdes.X = np.array([[0, 0], [1, 1], [0.5, 0.5], [0.1, 0.5]])  # Two input dimensions
+    expdes.Y = {'Z': [[0.4], [0.5], [0.3], [0.4]]}  # Output values
+    expdes.x_values = np.array([0])
     
     engine = Engine(mm, mod, expdes)
     engine.out_names = ['Z']
@@ -103,13 +107,18 @@ def basic_engine_sequential():
     
     engine.train_sequential()
     
-    engine.SeqModifiedLOO = {'Z': np.array([0.1, 0.2, 0.3])}
-    engine.seqValidError = {'Z': np.array([0.15, 0.25, 0.35])}
-    engine.SeqKLD = {'Z': np.array([0.05, 0.1, 0.15])}
-    engine.SeqBME = {'Z': np.array([0.02, 0.04, 0.06])}
-    engine.seqRMSEMean = {'Z': np.array([0.12, 0.14, 0.16])}
-    engine.seqRMSEStd = {'Z': np.array([0.03, 0.05, 0.07])}
-    engine.SeqDistHellinger = {'Z': np.array([0.08, 0.09, 0.1])}
+    engine.SeqModifiedLOO = {'DKL_rep_1': np.array([[1.31565589e-10],
+       [1.31413432e-10]])}
+    engine.seqValidError = {}
+    engine.SeqKLD = {'DKL_rep_1': np.array([[2.6296851 ],
+       [2.60875351]])}
+    engine.SeqBME = {'DKL_rep_1': np.array([[-19.33941695],
+       [-19.29572507]])}
+    engine.seqRMSEMean = {'DKL_rep_1': np.array([[1.02174823],
+       [1.02174727]])}
+    engine.seqRMSEStd = {'DKL_rep_1': np.array([[0.76724993],
+       [0.76725023]])}
+    engine.SeqDistHellinger = {}
     
     for key, array in engine.SeqModifiedLOO.items():
         assert np.all(array != None), f"Array {key} contains None values."
@@ -326,10 +335,17 @@ def test_plot_seq_design_diagnostics(basic_engine_sequential):
     Test the plot_seq_design_diagnostics method
     """
     engine = basic_engine_sequential
+    engine.ExpDesign.n_max_samples = 4
+    engine.ExpDesign.n_init_samples = 3
+    
     post = PostProcessing(engine)
     post.plot_seq_design_diagnostics()
     # Check if the plot was created and saved
-    assert os.path.exists(f"./{post.out_dir}/Seq_Design_Diagnostics.{post.out_format}")
+    assert os.path.exists(f"./{post.out_dir}/seq_design_diagnostics/seq_BME.{post.out_format}")
+    assert os.path.exists(f"./{post.out_dir}/seq_design_diagnostics/seq_KLD.{post.out_format}")
+    assert os.path.exists(f"./{post.out_dir}/seq_design_diagnostics/seq_Modified_LOO_error.{post.out_format}")
+    assert os.path.exists(f"./{post.out_dir}/seq_design_diagnostics/seq_RMSEMean.{post.out_format}")
+    assert os.path.exists(f"./{post.out_dir}/seq_design_diagnostics/seq_RMSEStd.{post.out_format}")
 
 def test_plot_seq_design_diagnostics_with_custom_values(basic_engine_trained):
     """
@@ -341,7 +357,7 @@ def test_plot_seq_design_diagnostics_with_custom_values(basic_engine_trained):
     post = PostProcessing(engine)
     post.plot_seq_design_diagnostics()
     # Check if the plot was created and saved
-    assert os.path.exists(f"./{post.out_dir}/Seq_Design_Diagnostics.{post.out_format}")
+    assert os.path.exists(f"./{post.out_dir}/seq_design_diagnostics/Seq_Design_Diagnostics.{post.out_format}")
 
 def test_plot_seq_design_diagnostics_with_empty_values(basic_engine_trained):
     """