From 8106503899ef7f4435894a91a2acaf7c67842acc Mon Sep 17 00:00:00 2001
From: farid <farid.mohammadi@iws.uni-stuttgart.de>
Date: Mon, 10 May 2021 15:39:24 +0200
Subject: [PATCH] [PA-A] activated PCA options.
---
.../tests/PA-A/ffpm_validation_stokesdarcy.py | 23 ++++---------------
.../tests/PA-A/ffpm_validation_stokespnm.py | 22 ++++--------------
2 files changed, 9 insertions(+), 36 deletions(-)
diff --git a/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py b/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
index 529c55d6a..d474def76 100644
--- a/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
+++ b/BayesValidRox/tests/PA-A/ffpm_validation_stokesdarcy.py
@@ -15,19 +15,6 @@ try:
except ModuleNotFoundError:
import pickle
-# import matplotlib.pyplot as plt
-# plt.rcParams.update({'font.size': 24})
-# plt.rc('figure', figsize = (24, 16))
-# plt.rc('font', family='serif', serif='Arial')
-# plt.rc('axes', grid = True)
-# plt.rc('text', usetex=True)
-# plt.rc('xtick', labelsize=24)
-# plt.rc('ytick', labelsize=24)
-# plt.rc('axes', labelsize=24)
-# plt.rc('axes', linewidth=2)
-# plt.rc('axes', grid=True)
-# plt.rc('grid', linestyle="-")
-
import matplotlib
matplotlib.use('agg')
@@ -143,7 +130,7 @@ def run(params, couplingcond='BJ', PCEEDMethod='normal'):
MetaModelOpts = Metamodel(Inputs)
# Select if you want to preserve the spatial/temporal depencencies
- # MetaModelOpts.DimRedMethod = 'PCA'
+ MetaModelOpts.DimRedMethod = 'PCA'
# MetaModelOpts.varPCAThreshold = 99.99
# Select your metamodel method
@@ -193,7 +180,7 @@ def run(params, couplingcond='BJ', PCEEDMethod='normal'):
MetaModelOpts.ExpDesign.SamplingMethod = 'latin_hypercube'
# Provide the experimental design object with a hdf5 file
- # MetaModelOpts.ExpDesign.hdf5File = "ExpDesign_ffpm-stokesdarcyBJ.hdf5"
+ # MetaModelOpts.ExpDesign.hdf5File = "ExpDesign_ffpm-stokesdarcy{}.hdf5".format(couplingcond)
# Sequential experimental design (needed only for sequential ExpDesign)
MetaModelOpts.ExpDesign.NrofNewSample = 1
@@ -291,7 +278,7 @@ def run(params, couplingcond='BJ', PCEEDMethod='normal'):
# Compute and print RMSE error
PostPCE.accuracyCheckMetaModel(Samples=MetaModelOpts.validSamples,
validOutputsDict=validModelRuns)
-
+
#=====================================================
#========= Bayesian inference (Calibration) ========
#=====================================================
@@ -564,7 +551,7 @@ def run(params, couplingcond='BJ', PCEEDMethod='normal'):
# ---------- set BayesValidRox params ----------
# PCEExpDesignMethod = 'normal' # 'normal' or 'sequential'
-# nInitSamples = 400 #50 Initial No. of orig. Model runs for surrogate training
+# nInitSamples = 300 #50 Initial No. of orig. Model runs for surrogate training
# nTotalSamples = 75 #100 Total No. of orig. Model runs for surrogate training
# nBootstrapItr = 100 # No. of bootstraping iterations for Bayesian analysis
# BootstrapNoise = 0.005 # Noise amount for bootstraping in Bayesian analysis
@@ -576,4 +563,4 @@ def run(params, couplingcond='BJ', PCEEDMethod='normal'):
#==================== Run main scripts for PA-B =======================
#==========================================================================
# PCEModel, BayesCalib, BayesValid = run(params,couplingcond='BJ', PCEEDMethod=PCEExpDesignMethod)
-# PCEModel = run(params,couplingcond='BJ', PCEEDMethod=PCEExpDesignMethod)
\ No newline at end of file
+# PCEModel = run(params,couplingcond='ER', PCEEDMethod=PCEExpDesignMethod)
\ No newline at end of file
diff --git a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
index b22439d6f..ef794af1e 100755
--- a/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
+++ b/BayesValidRox/tests/PA-A/ffpm_validation_stokespnm.py
@@ -8,7 +8,6 @@ Created on Thu Aug 13 09:53:11 2020
import sys, os
import numpy as np
import scipy.stats as stats
-from itertools import cycle
import pandas as pd
import shutil
try:
@@ -16,19 +15,6 @@ try:
except ModuleNotFoundError:
import pickle
-from matplotlib.backends.backend_pdf import PdfPages
-import matplotlib.pyplot as plt
-plt.rcParams.update({'font.size': 24})
-plt.rc('figure', figsize = (24, 16))
-plt.rc('font', family='serif', serif='Arial')
-plt.rc('axes', grid = True)
-plt.rc('text', usetex=True)
-plt.rc('xtick', labelsize=24)
-plt.rc('ytick', labelsize=24)
-plt.rc('axes', labelsize=24)
-plt.rc('axes', linewidth=2)
-plt.rc('axes', grid=True)
-plt.rc('grid', linestyle="-")
import matplotlib
matplotlib.use('agg')
@@ -138,7 +124,7 @@ def run(params, PCEEDMethod='normal'):
MetaModelOpts = Metamodel(Inputs)
# Select if you want to preserve the spatial/temporal depencencies
- # MetaModelOpts.DimRedMethod = 'PCA'
+ MetaModelOpts.DimRedMethod = 'PCA'
# MetaModelOpts.varPCAThreshold = 99.99
# Select your metamodel method
@@ -185,10 +171,10 @@ def run(params, PCEEDMethod='normal'):
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) chebyshev(FT)
# 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.SamplingMethod = 'latin_hypercube'
+ MetaModelOpts.ExpDesign.SamplingMethod = 'user'
# Provide the experimental design object with a hdf5 file
- # MetaModelOpts.ExpDesign.hdf5File = "ExpDesign_ffpm-stokespnm.hdf5"
+ MetaModelOpts.ExpDesign.hdf5File = "ExpDesign_ffpm-stokespnm.hdf5"
# Sequential experimental design (needed only for sequential ExpDesign)
MetaModelOpts.ExpDesign.NrofNewSample = 1
@@ -555,7 +541,7 @@ def run(params, PCEEDMethod='normal'):
#==========================================================================
# ---------- set BayesValidRox params ----------
# PCEExpDesignMethod = 'normal' # 'normal' or 'sequential'
-# nInitSamples = 400 #50 Initial No. of orig. Model runs for surrogate training
+# nInitSamples = 300 #50 Initial No. of orig. Model runs for surrogate training
# nTotalSamples = 200 #100 Total No. of orig. Model runs for surrogate training
# nBootstrapItr = 100 # No. of bootstraping iterations for Bayesian analysis
# BootstrapNoise = 0.005 # Noise amount for bootstraping in Bayesian analysis
--
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