diff --git a/BayesValidRox/tests/PA-A/Benchmark_PAA.py b/BayesValidRox/tests/PA-A/Benchmark_PAA.py
index 75c0a229732fc6b6db8c8a876ebea5869874b931..a3fa41fac3723ce8a5797cb1fc5b15818a88d1e8 100644
--- a/BayesValidRox/tests/PA-A/Benchmark_PAA.py
+++ b/BayesValidRox/tests/PA-A/Benchmark_PAA.py
@@ -136,7 +136,7 @@ def kdePlot_BayesFactor(BME_Dict, BF_label, plotName):
                 # legend
                 BF_label = key_i + '/' + key_j
                 legend_elements = [Patch(facecolor=Colors[i], edgecolor=Colors[i],label='BF('+BF_label+')')]
-                ax.legend(loc='best', handles=legend_elements,fontsize=SIZE-25)
+                ax.legend(loc='best', handles=legend_elements,fontsize=SIZE)
                 
             elif j == i:
                 # build a rectangle in axes coords
diff --git a/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py b/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
index 5ddd61778c94493ddb102bc99b1b308d386fb87c..cc9e5fc962ec866444e19d85e37140d1b51fedcf 100644
--- a/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
+++ b/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
@@ -59,7 +59,7 @@ def check_ranges(samples, BayesDF):
             index.append(i)
     return index
 
-def run(params, errorPerc=0.1, couplingcond='BJ', PCEEDMethod='normal'):
+def run(params, errorPerc=0.05, couplingcond='BJ', PCEEDMethod='normal'):
     
     print("\n" + "="*75)
     print("Stochastic calibration and validation of {0}.".format('ffpm-stokesdarcy'+couplingcond))
@@ -116,9 +116,9 @@ def run(params, errorPerc=0.1, couplingcond='BJ', PCEEDMethod='normal'):
     params = (1.0e-08, 1.5e-08)
     Inputs.Marginals[2].Name = '$K$' # [1e-10, 1e-7]
     # Inputs.Marginals[2].InputValues = stats.lognorm(ln_sigma, ln_loc, (rb-lb)/2).rvs(size=MCSize)
-    Inputs.Marginals[2].InputValues = stats.uniform(loc=1e-11, scale=1e-7-1e-11).rvs(size=MCSize)
-    # Inputs.Marginals[2].DistType = 'lognorm'
-    # Inputs.Marginals[2].Parameters =  params
+    # Inputs.Marginals[2].InputValues = stats.uniform(loc=1e-11, scale=1e-7-1e-11).rvs(size=MCSize)
+    Inputs.Marginals[2].DistType = 'lognorm'
+    Inputs.Marginals[2].Parameters =  params
     
     if couplingcond == 'BJ':
         Inputs.addMarginals() # AlphaBeaversJoseph
@@ -155,8 +155,8 @@ def run(params, errorPerc=0.1, couplingcond='BJ', PCEEDMethod='normal'):
     # The degree with the lowest Leave-One-Out cross-validation (LOO)
     # error (or the highest score=1-LOO)estimator is chosen as the final 
     # metamodel.
-    MetaModelOpts.MinPceDegree = 14 # default = 1
-    MetaModelOpts.MaxPceDegree = 14 #7
+    MetaModelOpts.MinPceDegree = 4 # default = 1
+    MetaModelOpts.MaxPceDegree = 4 #7
     
     # q-quasi-norm 0<q<1 (default=1)
     MetaModelOpts.q = 0.75 #np.linspace(0.3,0.6,3)
diff --git a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
index db8222f1873d6d45ada7e5120611edd6619ac67a..0d20055a3670a714938b8d034029504997319d44 100755
--- a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
+++ b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
@@ -59,7 +59,7 @@ def check_ranges(samples, BayesDF):
             index.append(i)
     return index
     
-def run(params, errorPerc=0.1, PCEEDMethod='normal'):
+def run(params, errorPerc=0.05, PCEEDMethod='normal'):
     
     print("\n" + "="*75)
     print("Stochastic calibration and validation of {0}.".format('ffpm-stokespnm'))
diff --git a/BayesValidRox/tests/PA-A/vel_diagnostics.py b/BayesValidRox/tests/PA-A/vel_diagnostics.py
index f4f7b7765d802151d73e5e25c4220b52ebb1bbe8..36cb236d0b0c6ff8addcc836faf519e2561d1863 100644
--- a/BayesValidRox/tests/PA-A/vel_diagnostics.py
+++ b/BayesValidRox/tests/PA-A/vel_diagnostics.py
@@ -57,6 +57,7 @@ validSamples = np.array(ValidSets["EDX/init_"])
 
 EDX = validSamples
 EDY = np.array(ValidSets["EDY/"+"velocity [m/s]"+"/init_"])
+ValidSets.close()
 
 critIdx = [EDX[:,1]<0.004875]
 normIdx = [EDX[:,1]>=0.004875]
@@ -66,7 +67,7 @@ critEDX = EDX[critIdx]
 critEDY = EDY[critIdx]
 
 # Select index
-idx = 7
+idx = 3
 
 # Run the surrogate model
 y_hat, y_std = PCEModel.eval_metamodel(samples=critEDX)