From 5de5b8a576d4f522b12c6d7d5a0d40c1f3b1ff54 Mon Sep 17 00:00:00 2001
From: Farid Mohammadi <farid.mohammadi@iws.uni-stuttgart.de>
Date: Thu, 10 Mar 2022 11:12:43 +0100
Subject: [PATCH] [tests][beam] remove script.
---
tests/beam/SA_Beam.py | 183 ------------------------------------------
1 file changed, 183 deletions(-)
delete mode 100644 tests/beam/SA_Beam.py
diff --git a/tests/beam/SA_Beam.py b/tests/beam/SA_Beam.py
deleted file mode 100644
index 9766a7a26..000000000
--- a/tests/beam/SA_Beam.py
+++ /dev/null
@@ -1,183 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jul 10 14:27:35 2019
-
-@author: farid
-"""
-import sys
-import os
-import numpy as np
-import pandas as pd
-import seaborn as sns
-
-try:
- import cPickle as pickle
-except ModuleNotFoundError:
- import pickle
-
-
-from PyLink.PyLinkForwardModel import PyLinkForwardModel
-from surrogate_models.Input import Input
-from surrogate_models.surrogate_models import aPCE
-from PostProcessing.PostProcessing import PostProcessing
-from BayesInference.BayesInference import BayesInference, Discrepancy
-
-
-
-
-
-if __name__ == "__main__":
-
- #=====================================================
- #============= COMPUTATIONAL MODEL ================
- #=====================================================
- Model = PyLinkForwardModel()
-
- Model.Type = 'PyLink'
- Model.Name = 'Beam9points'
- Model.InputFile = "SSBeam_Deflection.inp"
- Model.InputTemplate = "SSBeam_Deflection.tpl.inp"
-
-
- Model.Command = "myBeam9points SSBeam_Deflection.inp"
- Model.ExecutionPath = os.getcwd()
- Model.Output.Parser = 'read_Beam_Deflection'
- Model.Output.Names = ['Deflection [m]']
- Model.Output.FileNames = ["SSBeam_Deflection_.out"]
-
- # For Bayesian inversion
- Model.MeasurementFile = 'MeasuredData.csv'
-
- # For Checking with the MonteCarlo refrence
- Model.MCReferenceFile = 'MCrefs_MeanStd.csv'
-
- #=====================================================
- #========= PROBABILISTIC INPUT MODEL ==============
- #=====================================================
- # Define the uncertain parameters with their mean and
- # standard deviation
- Inputs = Input()
-
- Inputs.addMarginals()
- Inputs.Marginals[0].Name = 'Beam width'
- Inputs.Marginals[0].DistType = 'lognorm'
- Inputs.Marginals[0].Moments = [0.15, 0.0075]
-
- Inputs.addMarginals()
- Inputs.Marginals[1].Name = 'Beam height'
- Inputs.Marginals[1].DistType = 'lognorm'
- Inputs.Marginals[1].Moments = [0.3, 0.015]
-
- Inputs.addMarginals()
- Inputs.Marginals[2].Name = 'Youngs modulus'
- Inputs.Marginals[2].DistType = 'lognorm'
- Inputs.Marginals[2].Moments = [30000e+6, 4500e+6]
-
- Inputs.addMarginals()
- Inputs.Marginals[3].Name = 'Uniform load'
- Inputs.Marginals[3].DistType = 'lognorm'
- Inputs.Marginals[3].Moments = [10000, 2000]
-
- #=====================================================
- #====== POLYNOMIAL CHAOS EXPANSION METAMODELS ======
- #=====================================================
- MetaModelOpts = aPCE(Inputs)
-
- # Specify the max degree to be compared by the adaptive algorithm:
- # 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.MaxPceDegree = 10
-
- # Select the sparse least-square minimization method for
- # the PCE coefficients calculation:
- # 1)AaPCE: Adaptive aPCE 2)BRR: Bayesian Ridge Regression
- # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression (Not Working)
-
- MetaModelOpts.RegMethod = 'BRR'
-
- # Print summary of the regression results
- #MetaModelOpts.DisplayFlag = True
-
- # ------ Experimental Design --------
- # Generate an experimental design of size NrExpDesign based on a latin
- # hypercube sampling of the input model or user-defined values of X and/or Y:
- MetaModelOpts.addExpDesign()
- MetaModelOpts.ExpDesign.MCSize = 100000
-
- MetaModelOpts.ExpDesign.NrSamples = 75
- MetaModelOpts.ExpDesign.SamplingMethod = 'PCM' # 1)MC 2)LHS 3)PCM 4)LSCM 5)user
- MetaModelOpts.ExpDesign.Method = 'normal' # 1) normal 2) sequential
- #MetaModelOpts.ExpDesign.X = np.load('CollocationPoints.npy')
-
- # Sequential experimental design (needed only for sequential ExpDesign)
- MetaModelOpts.ExpDesign.MaxNSamples = 50 #150
- MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-3
-
- # 1)'dual annealing' 2) 'minimization' 3) MC (Alph. OptDesign:MC search)
- MetaModelOpts.ExpDesign.SeqOptimMethod = 'MC'
- MetaModelOpts.ExpDesign.MaxFunItr = 100
-
- MetaModelOpts.ExpDesign.NCandidate = 500
-
- # 'dual annealing'
- # 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
- # 3)APP (A-Posterior-percision)
- MetaModelOpts.ExpDesign.UtilityFunction = 'DPP'
-
- # Optimality criteria (MC search)
- # 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
- # 3)K-Opt (K-Optimality)
- MetaModelOpts.ExpDesign.UtilityFunction = 'D-Opt'
-
- # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- # Adaptive sparse arbitrary polynomial chaos expansion
- PCEModel = MetaModelOpts.create_PCE(Model)
-
-
- #=====================================================
- #========= POST PROCESSING OF METAMODELS ===========
- #=====================================================
- PostPCE = PostProcessing(PCEModel)
-
- # Compute the moments
- PostPCE.PCEMoments(PCEModel)
-
- # Comparison with Monte-Carlo reference
- # Plot Mean & Std for all Outputs
- PostPCE.MomentLineplot(PCE_Means = PostPCE.PCEMeans,
- PCE_Stds = PostPCE.PCEStd,
- x_label = 'x [m]', SaveFig = True)
-
- # Sensitivity analysis with Sobol indices
- PostPCE.SobolIndicesPCE(Output='Deflection [m]', SaveFig = True, PlotType='box')
-
- #=====================================================
- #============== Save class objects =================
- #=====================================================
- with open('Beam_Results.pkl', 'wb') as output:
- pickle.dump(PCEModel, output, pickle.HIGHEST_PROTOCOL)
-
- pickle.dump(PostPCE, output, pickle.HIGHEST_PROTOCOL)
-
-
-# del PCEModel
-# del PostPCE
-
- # Load the objects
-# with open('CO2Benchmark_Results.pkl', 'rb') as input:
-# PCEModel = pickle.load(input)
-# PostPCE = pickle.load(input)
-
-
-
-# PostPCE.plotFlag = True
-#
-# PostPCE.ValidMetamodel(MetaModel)
-
-
-# # Sensitivity analysis with Sobol indices
-# PostPCE.SobolIndicesPCE(MetaModel)
-#
-
\ No newline at end of file
--
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