[PA-A][PNM] update main script.

BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py

@@ -117,7 +117,7 @@ def run(params, averaging=True,errorPerc=0.05, inletLoc='top', PCEEDMethod='norm
     Inputs.Marginals[0].InputValues = stats.uniform(loc=5.0e-04, scale=1.5e-03-5.0e-04).rvs(size=MCSize)
     
     Inputs.addMarginals() #TransmissibilityTotal '$g_{t,}$'
-    Inputs.Marginals[1].Name = '$g_{t}$' # [1.0e-7, 1.0e-05]
+    Inputs.Marginals[1].Name = '$g_{ij}$' # [1.0e-7, 1.0e-05]
     Inputs.Marginals[1].InputValues = stats.uniform(loc=1e-7, scale=1e-5-1e-7).rvs(size=MCSize)
     
     # Inputs.addMarginals() #TransmissibilityThroat '$g_{t,ij}$'
@@ -377,7 +377,7 @@ def run(params, averaging=True,errorPerc=0.05, inletLoc='top', PCEEDMethod='norm
     # Plot posterior predictive
     postPredictiveplot(PCEModel.ModelObj.Name, errorPerc, averaging,
                        inletLoc=inletLoc, case='Calib', bins=20)
-    return BayesCalib
+
     #=====================================================
     #==================  VALIDATION  =====================
     #=====================================================
@@ -386,14 +386,18 @@ def run(params, averaging=True,errorPerc=0.05, inletLoc='top', PCEEDMethod='norm
     ValidInputs.addMarginals() #VyMaxTop
     ValidInputs.Marginals[0].Name = '$V^{top}$'
     ValidInputs.Marginals[0].InputValues = BayesCalib.Posterior_df['$V^{top}$']
+    
+    ValidInputs.addMarginals() #TransmissibilityTotal
+    ValidInputs.Marginals[1].Name = '$g_{ij}$'
+    ValidInputs.Marginals[1].InputValues = BayesCalib.Posterior_df['$g_{ij}$']
+    
+    # ValidInputs.addMarginals() #TransmissibilityThroat
+    # ValidInputs.Marginals[1].Name = '$g_{t,ij}$'
+    # ValidInputs.Marginals[1].InputValues = BayesCalib.Posterior_df['$g_{t,ij}$']
 
-    ValidInputs.addMarginals() #TransmissibilityThroat
-    ValidInputs.Marginals[1].Name = '$g_{t,ij}$'
-    ValidInputs.Marginals[1].InputValues = BayesCalib.Posterior_df['$g_{t,ij}$']
-
-    ValidInputs.addMarginals() #TransmissibilityHalfPore
-    ValidInputs.Marginals[2].Name = '$g_{p,i}$'
-    ValidInputs.Marginals[2].InputValues = BayesCalib.Posterior_df['$g_{p,i}$']
+    # ValidInputs.addMarginals() #TransmissibilityHalfPore
+    # ValidInputs.Marginals[2].Name = '$g_{p,i}$'
+    # ValidInputs.Marginals[2].InputValues = BayesCalib.Posterior_df['$g_{p,i}$']
 
     ValidInputs.addMarginals()
     ValidInputs.Marginals[3].Name = '$\\beta_{pore}$'
@@ -597,7 +601,7 @@ def run(params, averaging=True,errorPerc=0.05, inletLoc='top', PCEEDMethod='norm
 # nTotalSamples = 200 #100 Total No. of orig. Model runs for surrogate training
 # nBootstrapItr = 1000 # No. of bootstraping iterations for Bayesian analysis
 # BootstrapNoise = 0.005 # Noise amount for bootstraping in Bayesian analysis
-perturbedData = np.loadtxt('./data/perturbedValidData_squared_inclusion_topInflow.csv',delimiter=',')
+# perturbedData = np.loadtxt('./data/perturbedValidData_squared_inclusion_topInflow.csv',delimiter=',')
 # perturbedData = np.loadtxt('./data/perturbedValidData_squared_inclusion_leftInflow.csv',delimiter=',')
 # perturbedData = np.loadtxt('./data/perturbedValidDataAvg_squared_inclusion_topInflow.csv',delimiter=',')
 # perturbedData = np.loadtxt('./data/perturbedValidDataAvg_squared_inclusion_leftInflow.csv',delimiter=',')