From 01a73c69084435885e78d5e0e9f6a5022fe84204 Mon Sep 17 00:00:00 2001
From: farid <farid.mohammadi@iws.uni-stuttgart.de>
Date: Sun, 20 Jun 2021 16:41:16 +0200
Subject: [PATCH] [PA-A] save post_plot only in pdf format.
---
.../tests/PA-A/ffpm_validation_stokespnm.py | 17 +++++++++--------
BayesValidRox/tests/PA-A/post_plot.py | 2 +-
2 files changed, 10 insertions(+), 9 deletions(-)
diff --git a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
index c33684ce4..559da9846 100755
--- a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
+++ b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
@@ -156,8 +156,8 @@ def run(params, averaging=True,errorPerc=0.05, 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 = 10 # default = 1 10
- MetaModelOpts.MaxPceDegree = 10 #10
+ MetaModelOpts.MinPceDegree = 7 # default = 1 10
+ MetaModelOpts.MaxPceDegree = 7 #10
# q-quasi-norm 0<q<1 (default=1)
MetaModelOpts.q = 0.75 #np.linspace(0.3,0.6,3)
@@ -306,14 +306,15 @@ def run(params, averaging=True,errorPerc=0.05, PCEEDMethod='normal'):
# Select the inference method
BayesOptsCalib.SamplingMethod = 'MCMC'
- # import emcee
- # BayesOptsCalib.MCMCmoves = emcee.moves.StretchMove()
+ import emcee
+ BayesOptsCalib.MCMCmoves =[(emcee.moves.DEMove(), 0.8),
+ (emcee.moves.DESnookerMove(), 0.2),]
BayesOptsCalib.MCMCnwalkers = 500 #300
# BayesOptsCalib.MCMCnSteps = 1500 #5000
# Maximum a Posteriori based on primary analysis
- theta_MAP = np.array([1e-3,5.26480244078e-06,1.7642875191945115e-05,
- 35237.668793254459160])
- BayesOptsCalib.MCMCinitSamples = theta_MAP
+ # theta_MAP = np.array([1e-3,5.26480244078e-06,1.7642875191945115e-05,
+ # 35237.668793254459160])
+ # BayesOptsCalib.MCMCinitSamples = theta_MAP
BayesOptsCalib.MAP ='mean'
BayesOptsCalib.PlotPostDist = True
@@ -366,7 +367,7 @@ def run(params, averaging=True,errorPerc=0.05, PCEEDMethod='normal'):
#=====================================================
# Plot posterior predictive
postPredictiveplot(PCEModel.ModelObj.Name, errorPerc, averaging, case='Calib')
-
+ return BayesCalib
#=====================================================
#================== VALIDATION =====================
#=====================================================
diff --git a/BayesValidRox/tests/PA-A/post_plot.py b/BayesValidRox/tests/PA-A/post_plot.py
index 8ecda790b..f49c1694a 100755
--- a/BayesValidRox/tests/PA-A/post_plot.py
+++ b/BayesValidRox/tests/PA-A/post_plot.py
@@ -138,7 +138,7 @@ def postPredictiveplot(modelName, errorPrec, averaging=True, case='Calib', bins=
plt.title(title,fontdict=font)
fig.subplots_adjust(top=0.95)
plotname = OutputName if OutputName == 'p' else 'velocity'
- fig.savefig('./'+OutputDir+'/PointID_'+str(idx+1)+'_'+plotname+'.svg', bbox_inches='tight')
+ # fig.savefig('./'+OutputDir+'/PointID_'+str(idx+1)+'_'+plotname+'.svg', bbox_inches='tight')
fig.savefig('./'+OutputDir+'/PointID_'+str(idx+1)+'_'+plotname+'.pdf', bbox_inches='tight')
plt.close()
--
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