-#@ AJOUT OpenturnsSolver Macro
-# -*- coding: iso-8859-1 -*-
-# RESPONSABLE
+# -*- coding: utf-8 -*-
+# Copyright (C) 2007-2013 EDF R&D
+#
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
+#
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+#
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+#
"""
Ce module contient le generateur Etude pour Openturns
"""
-__revision__ = "V1.0"
+from Extensions.i18n import tr
-import os
+__revision__ = "V1.0"
defaultSTD = """#! /usr/bin/env python
# Chargement du module systeme
import sys
-sys.path.append( '%s' )
+sys.path[:0]=['%s']
# Chargement du module math
import math
# Chargement du module Open TURNS
from openturns import *
-from openturns.viewer import ViewImage,StopViewer,WaitForViewer
+
+# Fonction verifiant si un echantillon contient des valeurs non valides (NaN)
+def contain_nan_values(sample):
+ for point in sample:
+ for val in point:
+ if math.isnan(val):
+ return True
+ return False
results = {}
"""
+viewerSTD = """
+from openturns.viewer import View
+
+# Fonction de test du serveur X
+import subprocess
+xserver_available = None
+def is_xserver_available():
+ global xserver_available
+ if xserver_available is None:
+ xserver_available = True
+ try:
+ subprocess.check_call('python -c "from matplotlib import pyplot;pyplot.figure()" >/dev/null 2>&1', shell = True)
+ except:
+ xserver_available = False
+ return xserver_available
+
+"""
+
footerSTD = """
# Flush des messages en attente
'''
Generation du fichier python
'''
- def __init__ (self, appli, DictMCVal, ListeVariables, DictLois ) :
+ def __init__ (self, appli, DictMCVal, ListeVariablesIn, ListeVariablesOut, DictLois ) :
self.DictMCVal = DictMCVal
- self.ListeVariables = ListeVariables
+ self.ListeVariablesIn = ListeVariablesIn
+ self.ListeVariablesOut = ListeVariablesOut
self.DictLois = DictLois
#print "DictMCVal=", DictMCVal
- #print "ListeVariables=", ListeVariables
+ print "ListeVariablesIn= %s", ListeVariablesIn
+
+# A REPRENDRE DEPUIS ICI !!
+ print "ListeVariablesOut= %s", ListeVariablesOut
#print "DictLois=", DictLois
self.texteSTD = defaultSTD
self.OpenTURNS_path = appli.CONFIGURATION.OpenTURNS_path
"Threshold Exceedence" :
( "ThresholdExceedence",
{ "Simulation" : "Simulation",
- "Analytical" : "Analytical",
+ "FORM_SORM" : "Analytical",
"MonteCarlo" : "MonteCarlo",
"LHS" : "LHS",
"ImportanceSampling" : "ImportanceSampling",
"marginal" : "marginal",
"collection" : "collection",
"copula" : "copula",
+ "correlation" : "correlation",
+ "R" : "R",
+ "vars" : "vars",
+ "description" : "description",
"inputRandomVector" : "inputRandomVector",
"outputRandomVector" : "outputRandomVector",
"myQuadraticCumul" : "myQuadraticCumul",
"SRRCcoefficient" : 'results["SRRCcoefficient"]',
"kernel" : "kernel",
"kernelSmoothedDist" : "kernelSmoothedDist",
- "kernelSmoothedPDF" : "kernelSmoothedPDF",
+ "kernelSmoothedPDFDrawing" : "kernelSmoothedPDFDrawing",
+ "kernelSmoothedGraph" : "kernelSmoothedGraph",
+ "meanVector" : "meanVector",
+ "importanceDensity" : "importanceDensity",
"myEvent" : "myEvent",
"myAlgo" : "myAlgo",
"myResult" : "myResult",
"length" : "length",
"coefficientOfVariation" : 'results["coefficientOfVariation"]',
"convergenceGraph" : "convergenceGraph",
- "iterations" : 'results["iterations"]',
+ "convergenceDrawing" : "convergenceDrawing",
+ "simulationNumbers" : 'results["simulationNumbers"]',
"myOptimizer" : "myOptimizer",
"specificParameters" : "specificParameters",
"startingPoint" : "startingPoint",
"eventProbabilitySensitivity" : 'results["eventProbabilitySensitivity"]',
"hasoferReliabilityIndexSensitivity" : 'results["hasoferReliabilityIndexSensitivity"]',
"eventProbabilitySensitivityGraph" : "eventProbabilitySensitivityGraph",
+ "eventProbabilitySensitivityDrawing" : "eventProbabilitySensitivityDrawing",
"hasoferReliabilityIndexSensitivityGraph" : "hasoferReliabilityIndexSensitivityGraph",
+ "hasoferReliabilityIndexSensitivityDrawing" : "hasoferReliabilityIndexSensitivityDrawing",
"modelEvaluationCalls" : 'results["modelEvaluationCalls"]',
"modelGradientCalls" : 'results["modelGradientCalls"]',
"modelHessianCalls" : 'results["modelHessianCalls"]',
Imprime l entete commun a tous les fichiers
'''
txt = headerSTD % self.OpenTURNS_path
+ txt += viewerSTD
txt += "# Definit le niveau d'affichage de la log\n"
txt += "%s = Log.NONE\n" % self.variable["flags"]
for flag in self.logFlags.keys():
Produit le fichier study correspondant a une analyse Min/Max
'''
txt = self.Header()
- txt += "# Etude 'Min/Max'\n"
-
txt += self.Model()
Methode = None
if ( Traitement is not None ):
txt += apply( STDGenerateur.__dict__[ Traitement ], (self,) )
- txt += self.MinMaxComputation()
txt += self.MinMaxResult()
txt += self.Footer()
'''
Importe le modele physique
'''
- fonction = None
if ( self.DictMCVal.has_key( 'FileName' ) ):
name = self.DictMCVal[ 'FileName' ]
- #fonction = name[name.rfind('/')+1:name.rfind('.xml')]
txt = "# Charge le modele physique\n"
- txt = "%s = WrapperFile( '%s' )\n" % (self.variable["wrapper"], name)
+ txt += "%s = WrapperFile( '%s' )\n" % (self.variable["wrapper"], name)
+ txt += "%s = %s.getWrapperData()\n" % (self.variable["wrapperdata"], self.variable["wrapper"])
+ txt += "# Ces lignes sont utiles pour le fonctionnement du script sous Salome\n"
txt += "if globals().has_key('%s'):\n" % self.variable["framework"]
- txt += " %s = %s.getWrapperData()\n" % (self.variable["wrapperdata"], self.variable["wrapper"])
txt += " %s = %s.getFrameworkData()\n" % (self.variable["frameworkdata"], self.variable["wrapperdata"])
txt += " %s.studyid_ = %s['%s']\n" % (self.variable["frameworkdata"], self.variable["framework"], self.variable["studyid"])
txt += " %s.studycase_ = %s['%s']\n" % (self.variable["frameworkdata"], self.variable["framework"], self.variable["studycase"])
txt += " %s.componentname_ = %s['%s']\n" % (self.variable["frameworkdata"], self.variable["framework"], self.variable["componentname"])
txt += " %s.setFrameworkData( %s )\n" % (self.variable["wrapperdata"], self.variable["frameworkdata"])
txt += " %s.setWrapperData( %s )\n" % (self.variable["wrapper"], self.variable["wrapperdata"])
+ txt += "# Fin des lignes pour Salome\n"
txt += "%s = NumericalMathFunction( %s )\n" % (self.variable["model"], self.variable["wrapper"],)
- txt += "%s = %s.getInputNumericalPointDimension()\n" % (self.variable["n"], self.variable["model"])
+ txt += "%s = %s.getInputDimension()\n" % (self.variable["n"], self.variable["model"])
txt += "\n"
return txt
txt += self.TranslationVector()
txt += "%s = %s\n" % (self.variable["inputSample"], self.variable["myExperimentPlane"])
txt += "\n"
+ txt += "# Etude 'Min/Max'\n"
+ txt += "# Calcul\n"
+ txt += "%s = %s( %s )\n" % (self.variable["outputSample"], self.variable["model"], self.variable["inputSample"])
+ txt += "if contain_nan_values( %s ):\n" % (self.variable["outputSample"])
+ txt += " raise Exception('Some computations failed')\n"
+ txt += "\n"
return txt
def MinMaxRandomSampling (self):
Etude par echantillonage aleatoire
'''
size = 0
- if ( self.DictMCVal.has_key( 'PointsNumber' ) ):
- size = self.DictMCVal[ 'PointsNumber' ]
+ if ( self.DictMCVal.has_key( 'SimulationsNumber' ) ):
+ size = self.DictMCVal[ 'SimulationsNumber' ]
txt = "# Etude par echantillonage aleatoire\n"
txt += self.InputDistribution()
txt += self.InputRandomVector()
- txt += "%s = %d\n" % (self.variable["inSize"], size)
- txt += "%s = %s.getNumericalSample( %s )\n" % (self.variable["inputSample"], self.variable["inputRandomVector"], self.variable["inSize"])
txt += "\n"
+ txt += "# Etude 'Min/Max'\n"
+ txt += "# Calcul\n"
+ txt += "%s = %d\n" % (self.variable["inSize"], size)
+ txt += "%s = RandomVector( %s, %s )\n" % (self.variable["outputRandomVector"], self.variable["model"], self.variable["inputRandomVector"])
+ txt += "%s = %s.getSample( %s )\n" % (self.variable["outputSample"], self.variable["outputRandomVector"], self.variable["inSize"])
+ txt += "if contain_nan_values( %s ):\n" % (self.variable["outputSample"])
+ txt += " raise Exception('Some computations failed')\n"
return txt
def InputDistribution (self):
Cree la loi jointe des variables d entree
'''
txt = "# Definit la loi jointe des variables d'entree\n"
- txt += "%s = DistributionCollection( %d )\n" % (self.variable["collection"], len( self.ListeVariables ))
+ txt += "%s = DistributionCollection( %s )\n" % (self.variable["collection"], self.variable["n"])
+ txt += "%s = Description( %s )\n" % (self.variable["description"], self.variable["n"])
txt += "\n"
dictVariables = {}
- for variable in self.ListeVariables:
+ for variable in self.ListeVariablesIn:
nomVar = variable['ModelVariable'].get_name()
dictVariables[ nomVar ] = variable['Distribution']
if loi.has_key( 'Kind' ):
marginale = "%s_%d" % (self.variable["marginal"], i)
txt += "# Definit la loi marginale de la composante %d\n" % i
- txt += "%s = %s\n" % (marginale, apply( STDGenerateur.__dict__[ loi[ 'Kind' ] ], (self, loi, i, self.variable["collection"]) ))
+ txt += "%s = %s\n" % (marginale, apply( STDGenerateur.__dict__[ loi[ 'Kind' ] ], (self, loi) ))
+ txt += "%s.setName( '%s' )\n" % (marginale, conceptloi.get_name())
+ txt += "%s[ %d ] = '%s'\n" % (self.variable["description"], i, variable)
txt += "%s[ %d ] = Distribution( %s )\n" % (self.variable["collection"], i, marginale)
txt += "\n"
i += 1
- txt += self.Copula( len( self.ListeVariables ) )
+ txt += self.Copula()
txt += "# Definit la loi jointe\n"
txt += "%s = ComposedDistribution( %s, Copula( %s ) )\n" % (self.variable["distribution"], self.variable["collection"], self.variable["copula"])
+ txt += "%s.setDescription( %s )\n" % (self.variable["distribution"], self.variable["description"])
txt += "\n"
return txt
- def Copula (self, dimension):
+ def Copula (self):
'''
Cree la copule de la loi jointe
'''
txt = "# Definit la copule de la loi jointe\n"
- txt += "%s = IndependentCopula( %d )\n" % (self.variable["copula"], dimension)
+
+ if ( not self.DictMCVal.has_key( 'Copula' ) ):
+ self.DictMCVal[ 'Copula' ] = 'Independent'
+
+ if ( self.DictMCVal[ 'Copula' ] in ( 'Independent', ) ):
+ txt += "%s = IndependentCopula( %s )\n" % (self.variable["copula"], self.variable["n"])
+ elif ( self.DictMCVal[ 'Copula' ] in ( 'Normal', ) ):
+ varList = self.DictMCVal[ 'CorrelationMatrix' ][0]
+ dimension = len(varList)
+ txt += "%s = {}\n" % self.variable["correlation"]
+ for i in range( dimension ):
+ txt += "%s['%s'] = {}\n" % (self.variable["correlation"], varList[i])
+ for j in range ( dimension ):
+ txt += "%s['%s']['%s'] = %g\n" % (self.variable["correlation"], varList[i], varList[j], self.DictMCVal[ 'CorrelationMatrix' ][i+1][j])
+ txt += "%s = getCorrelationMatrixFromMap( %s.getVariableList(), %s )\n" % (self.variable["R"], self.variable["wrapperdata"], self.variable["correlation"])
+ txt += "%s = NormalCopula( %s )\n" % (self.variable["copula"], self.variable["R"])
+
txt += "\n"
return txt
'''
Cree le vector aleatoire de sortie
'''
+ nomVar = "output"
+ for variable in self.ListeVariablesOut:
+ nomVar = variable['ModelVariable'].get_name()
+
txt = "# Definit le vecteur aleatoire de sortie\n"
txt += "%s = RandomVector( %s, %s )\n" % (self.variable["outputRandomVector"], self.variable["model"], self.variable["inputRandomVector"])
+ txt += "%s.setName( '%s' )\n" % (self.variable["outputRandomVector"], nomVar)
txt += "\n"
return txt
txt += "\n"
return txt
- def MinMaxComputation (self):
- '''
- Realise le calcul deterministe
- '''
- txt = "# Calcul\n"
- txt += "%s = %s( %s )\n" % (self.variable["outputSample"], self.variable["model"], self.variable["inputSample"])
- txt += "\n"
- return txt
-
def MinMaxResult (self):
'''
Produit les resultats de l etude
'''
txt = "# Resultats\n"
txt += "%s = %s.getMin()\n" % (self.variable["minValue"], self.variable["outputSample"])
- txt += "print '%s = ', %s\n" % (self.variable["minValue"], self.variable["minValue"])
+ txt += "print '%s = ', %s\n" % ("minValue", self.variable["minValue"])
txt += "\n"
txt += "%s = %s.getMax()\n" % (self.variable["maxValue"], self.variable["outputSample"])
- txt += "print '%s = ', %s\n" % (self.variable["maxValue"], self.variable["maxValue"])
+ txt += "print '%s = ', %s\n" % ("maxValue", self.variable["maxValue"])
txt += "\n"
return txt
Produit le fichier study correspondant a une analyse d incertitude en valeur centrale
'''
txt = self.Header()
- txt += "# Etude 'Central Uncertainty'\n"
-
txt += self.Model()
txt += self.InputDistribution()
txt += self.InputRandomVector()
Traitement = subDict[ Methode ]
if ( Traitement is not None ):
+ txt += "# Etude 'Central Uncertainty'\n"
txt += apply( STDGenerateur.__dict__[ Traitement ], (self,) )
txt += self.Footer()
if ( self.DictMCVal.has_key( 'MeanFirstOrder' ) ):
if ( self.DictMCVal[ 'MeanFirstOrder' ] == "yes" ):
txt += "%s = %s.getMeanFirstOrder()\n" % (self.variable["meanFirstOrder"], self.variable["myQuadraticCumul"])
- txt += "print '%s = ', %s\n" % (self.variable["meanFirstOrder"], self.variable["meanFirstOrder"])
+ txt += "print '%s = ', %s\n" % ("mean First Order", self.variable["meanFirstOrder"])
txt += "\n"
if ( self.DictMCVal.has_key( 'MeanSecondOrder' ) ):
if ( self.DictMCVal[ 'MeanSecondOrder' ] == "yes" ):
txt += "%s = %s.getMeanSecondOrder()\n" % (self.variable["meanSecondOrder"], self.variable["myQuadraticCumul"])
- txt += "print '%s = ', %s\n" % (self.variable["meanSecondOrder"], self.variable["meanSecondOrder"])
+ txt += "print '%s = ', %s\n" % ("mean Second Order", self.variable["meanSecondOrder"])
txt += "\n"
if ( self.DictMCVal.has_key( 'StandardDeviationFirstOrder' ) ):
txt += "dim = %s.getDimension()\n" % self.variable["standardDeviationFirstOrder"]
txt += "for i in range( dim ):\n"
txt += " %s[ i, i ] = math.sqrt( %s[ i, i ] )\n" % (self.variable["standardDeviationFirstOrder"], self.variable["standardDeviationFirstOrder"])
- txt += "print '%s = ', %s\n" % (self.variable["standardDeviationFirstOrder"], self.variable["standardDeviationFirstOrder"])
+ txt += " print '%s = ', %s[ i, i ]\n" % ("standard Deviation First Order", self.variable["standardDeviationFirstOrder"])
txt += "\n"
- if ( self.DictMCVal.has_key( 'NumericalResults' ) ):
- if ( self.DictMCVal[ 'NumericalResults' ] == "yes" ):
- txt += "if ( %s.getDimension() == 1):\n" % self.variable["outputRandomVector"]
- txt += " %s = %s.getImportanceFactors()\n" % (self.variable["importanceFactors"], self.variable["myQuadraticCumul"])
- txt += " print '%s = ', %s\n" % (self.variable["importanceFactors"], self.variable["importanceFactors"])
+ if ( self.DictMCVal.has_key( 'ImportanceFactor' ) ):
+ if ( self.DictMCVal[ 'ImportanceFactor' ] == "yes" ):
+ txt += "%s = %s.getImportanceFactors()\n" % (self.variable["importanceFactors"], self.variable["myQuadraticCumul"])
+ txt += "for i in range(%s.getDimension()):\n" % self.variable["importanceFactors"]
+ txt += " print %s.getDescription()[i], ':', %s[i]*100., '%%'\n" % (self.variable["distribution"], self.variable["importanceFactors"])
txt += "\n"
-
- if ( self.DictMCVal.has_key( 'GraphicalResults' ) ):
- if ( self.DictMCVal[ 'GraphicalResults' ] == "yes" ):
txt += "%s = %s.drawImportanceFactors()\n" % (self.variable["importanceFactorsGraph"], self.variable["myQuadraticCumul"])
- txt += "Show( %s )\n" % self.variable["importanceFactorsGraph"]
- txt += "%s.draw( '%s' )\n" % (self.variable["importanceFactorsGraph"], self.variable["importanceFactorsDrawing"])
- txt += "ViewImage( %s.getBitmap() )\n" % self.variable["importanceFactorsGraph"]
+ txt += "%s = '%s'\n" % (self.variable["importanceFactorsDrawing"], self.DictMCVal[ 'ImportanceFactorDrawingFilename' ])
+ txt += "%s.draw( %s )\n" % (self.variable["importanceFactorsGraph"], self.variable["importanceFactorsDrawing"])
+ txt += "if is_xserver_available():\n"
+ txt += " view = View(%s)\n" % self.variable["importanceFactorsGraph"]
+ txt += " view.show(block=True)\n"
+ txt += "else:\n"
+ txt += " print 'Warning: cannot display image', %s.getBitmap(), '(probably because no X server was found)'\n" % self.variable["importanceFactorsGraph"]
txt += "print 'bitmap =', %s.getBitmap()\n" % self.variable["importanceFactorsGraph"]
txt += "print 'postscript =', %s.getPostscript()\n" % self.variable["importanceFactorsGraph"]
txt += "\n"
Etude par echantillonage aleatoire
'''
size = 0
- if ( self.DictMCVal.has_key( 'PointsNumber' ) ):
- size = self.DictMCVal[ 'PointsNumber' ]
+ if ( self.DictMCVal.has_key( 'SimulationsNumber' ) ):
+ size = self.DictMCVal[ 'SimulationsNumber' ]
txt = "# Echantillonnage aleatoire de la variable de sortie\n"
txt += "%s = %d\n" % (self.variable["inSize"], size)
- txt += "%s = %s.getNumericalSample( %s )\n" % (self.variable["outputSample"], self.variable["outputRandomVector"], self.variable["inSize"])
+ txt += "%s = %s.getSample( %s )\n" % (self.variable["inputSample"], self.variable["inputRandomVector"], self.variable["inSize"])
+ txt += "%s = %s( %s )\n" % (self.variable["outputSample"], self.variable["model"], self.variable["inputSample"])
+ txt += "if contain_nan_values( %s ):\n" % (self.variable["outputSample"])
+ txt += " raise Exception('Some computations failed')\n"
txt += "\n"
if ( self.DictMCVal.has_key( 'EmpiricalMean' ) ):
if ( self.DictMCVal[ 'EmpiricalMean' ] == "yes" ):
txt += "%s = %s.computeMean()\n" % (self.variable["empiricalMean"], self.variable["outputSample"])
- txt += "print '%s =', %s\n" % (self.variable["empiricalMean"], self.variable["empiricalMean"])
+ txt += "print '%s =', %s[0]\n" % ("empirical Mean", self.variable["empiricalMean"])
txt += "\n"
if ( self.DictMCVal.has_key( 'EmpiricalStandardDeviation' ) ):
txt += "dim = %s.getDimension()\n" % self.variable["empiricalStandardDeviation"]
txt += "for i in range( dim ):\n"
txt += " %s[ i, i ] = math.sqrt( %s[ i, i ] )\n" % (self.variable["empiricalStandardDeviation"], self.variable["empiricalStandardDeviation"])
- txt += "print '%s = ', %s\n" % (self.variable["empiricalStandardDeviation"], self.variable["empiricalStandardDeviation"])
+ txt += " print '%s = ', %s[ i, i ]\n" % ("empirical Standard Deviation", self.variable["empiricalStandardDeviation"])
txt += "\n"
if ( self.DictMCVal.has_key( 'EmpiricalQuantile_Order' ) ):
ordre = self.DictMCVal[ 'EmpiricalQuantile_Order' ]
txt += "%s = %s.computeQuantile( %s )\n" % (self.variable["empiricalQuantile"], self.variable["outputSample"], ordre)
- txt += "print '%s =', %s\n" % (self.variable["empiricalQuantile"], self.variable["empiricalQuantile"])
+ txt += "print '%s ( %s ) =', %s\n" % ("empirical Quantile", ordre, self.variable["empiricalQuantile"])
txt += "\n"
- if ( self.DictMCVal.has_key( 'AnalysedCorrelations' ) ):
- if ( self.DictMCVal[ 'AnalysedCorrelations' ] == "yes" ):
- txt += "# Ou est le %s ?\n" % self.variable["inputSample"]
- txt += "#if ( ( %s.getDimension() == 1 ) and ( %s.getDimension() == 1 ) ):\n" % (self.variable["inputSample"], self.variable["outputSample"])
- txt += "# %s = CorrelationAnalysis.PCC( %s, %s )\n" % (self.variable["PCCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
- txt += "# print '%s = ', %s\n" % (self.variable["PCCcoefficient"], self.variable["PCCcoefficient"])
- txt += "# %s = CorrelationAnalysis.PRCC( %s, %s )\n" % (self.variable["PRCCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
- txt += "# print '%s = ', %s\n" % (self.variable["PRCCcoefficient"], self.variable["PRCCcoefficient"])
- txt += "# %s = CorrelationAnalysis.SRC( %s, %s )\n" % (self.variable["SRCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
- txt += "# print '%s = ', %s\n" % (self.variable["SRCcoefficient"], self.variable["SRCcoefficient"])
- txt += "# %s = CorrelationAnalysis.SRRC( %s, %s )\n" % (self.variable["SRRCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
- txt += "# print '%s = ', %s\n" % (self.variable["SRRCcoefficient"], self.variable["SRRCcoefficient"])
+ if ( self.DictMCVal.has_key( 'CorrelationAnalysis' ) ):
+ if ( self.DictMCVal[ 'CorrelationAnalysis' ] == "yes" ):
+ txt += "if ( %s.getDimension() == 1 ):\n" % self.variable["outputSample"]
+ txt += " %s = CorrelationAnalysis.PCC( %s, %s )\n" % (self.variable["PCCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
+ txt += " print 'PCC Coefficients:'\n"
+ txt += " for i in range( %s ):\n" % self.variable["n"]
+ txt += " print %s.getDescription()[i], ':', %s[i]\n" % (self.variable["distribution"], self.variable["PCCcoefficient"])
+ txt += "\n"
+ txt += " %s = CorrelationAnalysis.PRCC( %s, %s )\n" % (self.variable["PRCCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
+ txt += " print 'PRCC Coefficients:'\n"
+ txt += " for i in range( %s ):\n" % self.variable["n"]
+ txt += " print %s.getDescription()[i], ':', %s[i]\n" % (self.variable["distribution"], self.variable["PRCCcoefficient"])
+ txt += "\n"
+ txt += " %s = CorrelationAnalysis.SRC( %s, %s )\n" % (self.variable["SRCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
+ txt += " print 'SRC Coefficients:'\n"
+ txt += " for i in range( %s ):\n" % self.variable["n"]
+ txt += " print %s.getDescription()[i], ':', %s[i]\n" % (self.variable["distribution"], self.variable["SRCcoefficient"])
+ txt += "\n"
+ txt += " %s = CorrelationAnalysis.SRRC( %s, %s )\n" % (self.variable["SRRCcoefficient"], self.variable["inputSample"], self.variable["outputSample"])
+ txt += " print 'SRRC Coefficients:'\n"
+ txt += " for i in range( %s ):\n" % self.variable["n"]
+ txt += " print %s.getDescription()[i], ':', %s[i]\n" % (self.variable["distribution"], self.variable["SRRCcoefficient"])
txt += "\n"
if ( self.DictMCVal.has_key( 'KernelSmoothing' ) ):
txt += "# Kernel Smoohing\n"
txt += "%s = KernelSmoothing()\n" % self.variable["kernel"]
txt += "if ( %s.getDimension() == 1 ):\n" % self.variable["outputSample"]
- txt += " %s = %s.buildImplementation( %s, 'TRUE')\n" % (self.variable["kernelSmoothedDist"], self.variable["kernel"], self.variable["outputSample"])
- txt += " %s = %s.drawPDF()\n" % (self.variable["kernelSmoothedPDF"], self.variable["kernelSmoothedDist"])
- txt += " Show( %s )\n" % self.variable["kernelSmoothedPDF"]
+ txt += " %s.setName( 'Output' )\n" % self.variable["outputSample"]
+ txt += " %s = %s.build( %s, 'TRUE')\n" % (self.variable["kernelSmoothedDist"], self.variable["kernel"], self.variable["outputSample"])
+ txt += " %s = %s.drawPDF()\n" % (self.variable["kernelSmoothedGraph"], self.variable["kernelSmoothedDist"])
+ txt += " %s = '%s'\n" % (self.variable["kernelSmoothedPDFDrawing"], self.DictMCVal[ 'KernelSmoothingDrawingFilename' ])
+ txt += " %s.draw( %s )\n" % (self.variable["kernelSmoothedGraph"], self.variable["kernelSmoothedPDFDrawing"])
+ txt += " if is_xserver_available():\n"
+ txt += " view = View(%s)\n" % self.variable["kernelSmoothedGraph"]
+ txt += " view.show(block=True)\n"
+ txt += " else:\n"
+ txt += " print 'Warning: cannot display image', %s.getBitmap(), '(probably because no X server was found)'\n" % self.variable["kernelSmoothedGraph"]
+ txt += " print 'bitmap =', %s.getBitmap()\n" % self.variable["kernelSmoothedGraph"]
+ txt += " print 'postscript =', %s.getPostscript()\n" % self.variable["kernelSmoothedGraph"]
txt += "\n"
return txt
blockSize = None
if ( self.DictMCVal.has_key( 'BlockSize' ) ):
- maxOuterSampling = self.DictMCVal[ 'BlockSize' ]
+ blockSize = self.DictMCVal[ 'BlockSize' ]
txt += "%s.setBlockSize( %s )\n" % (self.variable["myAlgo"], blockSize)
maxCoefficientOfVariation = None
if ( self.DictMCVal.has_key( 'Probability' ) ):
if ( self.DictMCVal[ 'Probability' ] == "yes" ):
txt += "%s = %s.getProbabilityEstimate()\n" % (self.variable["probability"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["probability"], self.variable["probability"])
+ txt += "print '%s =', %s\n" % ("probability", self.variable["probability"])
txt += "\n"
if ( self.DictMCVal.has_key( 'StandardDeviation' ) ):
if ( self.DictMCVal[ 'StandardDeviation' ] == "yes" ):
txt += "%s = math.sqrt( %s.getProbabilityEstimate() )\n" % (self.variable["standardDeviation"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["standardDeviation"], self.variable["standardDeviation"])
+ txt += "print '%s =', %s\n" % ("standard Deviation", self.variable["standardDeviation"])
txt += "\n"
if ( self.DictMCVal.has_key( 'ConfidenceInterval' ) and self.DictMCVal.has_key( 'Probability' ) ):
if ( self.DictMCVal.has_key( 'VariationCoefficient' ) ):
if ( self.DictMCVal[ 'VariationCoefficient' ] == "yes" ):
txt += "%s = %s.getCoefficientOfVariation()\n" % (self.variable["coefficientOfVariation"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["coefficientOfVariation"], self.variable["coefficientOfVariation"])
+ txt += "print '%s =', %s\n" % ("coefficient of Variation", self.variable["coefficientOfVariation"])
txt += "\n"
- if ( self.DictMCVal.has_key( 'IterationNumber' ) ):
- if ( self.DictMCVal[ 'IterationNumber' ] == "yes" ):
- txt += "%s = %s.getOuterSampling()\n" % (self.variable["iterations"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["iterations"], self.variable["iterations"])
+ if ( self.DictMCVal.has_key( 'SimulationsNumber' ) ):
+ if ( self.DictMCVal[ 'SimulationsNumber' ] == "yes" ):
+ txt += "%s = %s.getOuterSampling()\n" % (self.variable["simulationNumbers"], self.variable["myResult"])
+ txt += "print '%s =', %s\n" % ("simulation Numbers", self.variable["simulationNumbers"])
txt += "\n"
- if ( self.DictMCVal.has_key( 'ConvergenceGraph' ) ):
- if ( self.DictMCVal[ 'ConvergenceGraph' ] == "yes" ):
- txt += "%s = %s.drawProbabilityConvergence()\n" % (self.variable["convergenceGraph"], self.variable["myAlgo"])
- txt += "Show( %s )\n" % self.variable["convergenceGraph"]
+ if ( self.DictMCVal.has_key( 'ConvergenceGraph' ) and self.DictMCVal.has_key( 'ConfidenceInterval' ) ):
+ if ( ( self.DictMCVal[ 'ConvergenceGraph' ] == "yes" ) and ( self.DictMCVal[ 'ConfidenceInterval' ] == "yes" ) ):
+ txt += "%s = %s\n" % (self.variable["alpha"], self.DictMCVal[ 'Level' ])
+ txt += "%s = %s.drawProbabilityConvergence( %s )\n" % (self.variable["convergenceGraph"], self.variable["myAlgo"], self.variable["alpha"])
+ txt += "%s = '%s'\n" % (self.variable["convergenceDrawing"], self.DictMCVal[ 'ConvergenceDrawingFilename' ])
+ txt += "%s.draw( %s )\n" % (self.variable["convergenceGraph"], self.variable["convergenceDrawing"])
+ txt += "if is_xserver_available():\n"
+ txt += " view = View(%s)\n" % self.variable["convergenceGraph"]
+ txt += " view.show(block=True)\n"
+ txt += "else:\n"
+ txt += " print 'Warning: cannot display image', %s.getBitmap(), '(probably because no X server was found)'\n" % self.variable["convergenceGraph"]
txt += "\n"
return txt
'''
txt = ""
- iterations = None
+ simulationNumbers = None
if ( self.DictMCVal.has_key( 'MaximumIterationsNumber' ) ):
- iterations = self.DictMCVal[ 'MaximumIterationsNumber' ]
- txt += "%s.setMaximumIterationsNumber( %s )\n" % (self.variable["myOptimizer"], iterations)
+ simulationNumbers = self.DictMCVal[ 'MaximumIterationsNumber' ]
+ txt += "%s.setMaximumIterationsNumber( %s )\n" % (self.variable["myOptimizer"], simulationNumbers)
absoluteError = None
if ( self.DictMCVal.has_key( 'MaximumAbsoluteError' ) ):
if ( self.DictMCVal.has_key( 'HasoferReliabilityIndex' ) ):
if ( self.DictMCVal[ 'HasoferReliabilityIndex' ] == "yes" ):
txt += "%s = %s.getHasoferReliabilityIndex()\n" % (self.variable["hasoferReliabilityIndex"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["hasoferReliabilityIndex"], self.variable["hasoferReliabilityIndex"])
+ txt += "print '%s =', %s\n" % ("hasofer Reliability Index", self.variable["hasoferReliabilityIndex"])
txt += "\n"
if ( self.DictMCVal.has_key( 'DesignPoint' ) ):
if ( self.DictMCVal[ 'DesignPoint' ] == "yes" ):
txt += "%s = %s.getStandardSpaceDesignPoint()\n" % (self.variable["standardSpaceDesignPoint"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["standardSpaceDesignPoint"], self.variable["standardSpaceDesignPoint"])
+ txt += "print '%s =', %s\n" % ("standard Space Design Point", self.variable["standardSpaceDesignPoint"])
txt += "%s = %s.getPhysicalSpaceDesignPoint()\n" % (self.variable["physicalSpaceDesignPoint"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["physicalSpaceDesignPoint"], self.variable["physicalSpaceDesignPoint"])
+ txt += "print '%s =', %s\n" % ("physical Space Design Point", self.variable["physicalSpaceDesignPoint"])
txt += "\n"
- if ( self.DictMCVal.has_key( 'ImportanceFactorNumericalResults' ) ):
- if ( self.DictMCVal[ 'ImportanceFactorNumericalResults' ] == "yes" ):
+ if ( self.DictMCVal.has_key( 'ImportanceFactor' ) ):
+ if ( self.DictMCVal[ 'ImportanceFactor' ] == "yes" ):
+ txt += "print 'Importance Factors:'\n"
txt += "%s = %s.getImportanceFactors()\n" % (self.variable["importanceFactors"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["importanceFactors"], self.variable["importanceFactors"])
+ txt += "for i in range(%s.getDimension()):\n" % self.variable["importanceFactors"]
+ txt += " print %s.getDescription()[i], ':', %s[i]*100., '%%'\n" % (self.variable["distribution"], self.variable["importanceFactors"])
txt += "\n"
-
- if ( self.DictMCVal.has_key( 'ImportanceFactorGraphicalResults' ) ):
- if ( self.DictMCVal[ 'ImportanceFactorGraphicalResults' ] == "yes" ):
txt += "%s = %s.drawImportanceFactors()\n" % (self.variable["importanceFactorsGraph"], self.variable["myResult"])
- txt += "Show( %s )\n" % self.variable["importanceFactorsGraph"]
+ txt += "%s = '%s'\n" % (self.variable["importanceFactorsDrawing"], self.DictMCVal[ 'ImportanceFactorDrawingFilename' ])
+ txt += "%s.draw( %s )\n" % (self.variable["importanceFactorsGraph"], self.variable["importanceFactorsDrawing"])
+ txt += "if is_xserver_available():\n"
+ txt += " view = View(%s)\n" % self.variable["importanceFactorsGraph"]
+ txt += " view.show(block=True)\n"
+ txt += "else:\n"
+ txt += " print 'Warning: cannot display image', %s.getBitmap(), '(probably because no X server was found)'\n" % self.variable["importanceFactorsGraph"]
+ txt += "print 'bitmap =', %s.getBitmap()\n" % self.variable["importanceFactorsGraph"]
+ txt += "print 'postscript =', %s.getPostscript()\n" % self.variable["importanceFactorsGraph"]
txt += "\n"
- if ( self.DictMCVal.has_key( 'FORMEventProbabilitySensitivityNumericalResults' ) ):
- if ( self.DictMCVal[ 'FORMEventProbabilitySensitivityNumericalResults' ] == "yes" ):
+ if ( self.DictMCVal.has_key( 'FORMEventProbabilitySensitivity' ) ):
+ if ( self.DictMCVal[ 'FORMEventProbabilitySensitivity' ] == "yes" ):
txt += "%s = %s.getEventProbabilitySensitivity()\n" % (self.variable["eventProbabilitySensitivity"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["eventProbabilitySensitivity"], self.variable["eventProbabilitySensitivity"])
+ txt += "print 'FORM Event Probability Sensitivity:'\n"
+ txt += "for i in range( %s ):\n" % self.variable["n"]
+ txt += " print %s.getDescription()[i], ':'\n" % self.variable["distribution"]
+ txt += " for j in range( %s[i].getDimension() ):\n" % self.variable["eventProbabilitySensitivity"]
+ txt += " print ' ', %s[i].getDescription()[j], ':', %s[i][j]\n" % (self.variable["eventProbabilitySensitivity"], self.variable["eventProbabilitySensitivity"])
txt += "\n"
-
- if ( self.DictMCVal.has_key( 'FORMEventProbabilitySensitivityGraphicalResults' ) ):
- if ( self.DictMCVal[ 'FORMEventProbabilitySensitivityGraphicalResults' ] == "yes" ):
- txt += "%s = %s.drawEventProbabilitySensitivity()\n" % (self.variable["eventProbabilitySensitivityGraph"], self.variable["myResult"])
- txt += "Show( %s[0] )\n" % self.variable["eventProbabilitySensitivityGraph"]
+ txt += "%s = %s.drawEventProbabilitySensitivity()[0]\n" % (self.variable["eventProbabilitySensitivityGraph"], self.variable["myResult"])
+ txt += "%s = '%s'\n" % (self.variable["eventProbabilitySensitivityDrawing"], self.DictMCVal[ 'FORMEventProbabilitySensitivityDrawingFilename' ])
+ txt += "%s.draw( %s )\n" % (self.variable["eventProbabilitySensitivityGraph"], self.variable["eventProbabilitySensitivityDrawing"])
+ txt += "if is_xserver_available():\n"
+ txt += " view = View(%s)\n" % self.variable["eventProbabilitySensitivityGraph"]
+ txt += " view.show(block=True)\n"
+ txt += "else:\n"
+ txt += " print 'Warning: cannot display image', %s.getBitmap(), '(probably because no X server was found)'\n" % self.variable["eventProbabilitySensitivityGraph"]
+ txt += "print 'bitmap =', %s.getBitmap()\n" % self.variable["eventProbabilitySensitivityGraph"]
+ txt += "print 'postscript =', %s.getPostscript()\n" % self.variable["eventProbabilitySensitivityGraph"]
txt += "\n"
- if ( self.DictMCVal.has_key( 'HasoferReliabilityIndexSensitivityNumericalResults' ) ):
- if ( self.DictMCVal[ 'HasoferReliabilityIndexSensitivityNumericalResults' ] == "yes" ):
+ if ( self.DictMCVal.has_key( 'HasoferReliabilityIndexSensitivity' ) ):
+ if ( self.DictMCVal[ 'HasoferReliabilityIndexSensitivity' ] == "yes" ):
txt += "%s = %s.getHasoferReliabilityIndexSensitivity()\n" % (self.variable["hasoferReliabilityIndexSensitivity"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["hasoferReliabilityIndexSensitivity"], self.variable["hasoferReliabilityIndexSensitivity"])
+ txt += "print 'Hasofer Reliability Index Sensitivity:'\n"
+ txt += "for i in range( %s ):\n" % self.variable["n"]
+ txt += " print %s.getDescription()[i], ':'\n" % self.variable["distribution"]
+ txt += " for j in range( %s[i].getDimension() ):\n" % self.variable["hasoferReliabilityIndexSensitivity"]
+ txt += " print ' ', %s[i].getDescription()[j], ':', %s[i][j]\n" % (self.variable["hasoferReliabilityIndexSensitivity"], self.variable["hasoferReliabilityIndexSensitivity"])
txt += "\n"
-
- if ( self.DictMCVal.has_key( 'HasoferReliabilityIndexSensitivityGraphicalResults' ) ):
- if ( self.DictMCVal[ 'HasoferReliabilityIndexSensitivityGraphicalResults' ] == "yes" ):
- txt += "%s = %s.drawHasoferReliabilityIndexSensitivity()\n" % (self.variable["hasoferReliabilityIndexSensitivityGraph"], self.variable["myResult"])
- txt += "Show( %s[0] )\n" % self.variable["hasoferReliabilityIndexSensitivityGraph"]
+ txt += "%s = %s.drawHasoferReliabilityIndexSensitivity()[0]\n" % (self.variable["hasoferReliabilityIndexSensitivityGraph"], self.variable["myResult"])
+ txt += "%s = '%s'\n" % (self.variable["hasoferReliabilityIndexSensitivityDrawing"], self.DictMCVal[ 'HasoferReliabilityIndexSensitivityDrawingFilename' ])
+ txt += "%s.draw( %s )\n" % (self.variable["hasoferReliabilityIndexSensitivityGraph"], self.variable["hasoferReliabilityIndexSensitivityDrawing"])
+ txt += "if is_xserver_available():\n"
+ txt += " view = View(%s)\n" % self.variable["hasoferReliabilityIndexSensitivityGraph"]
+ txt += " view.show(block=True)\n"
+ txt += "else:\n"
+ txt += " print 'Warning: cannot display image', %s.getBitmap(), '(probably because no X server was found)'\n" % self.variable["hasoferReliabilityIndexSensitivityGraph"]
+ txt += "print 'bitmap =', %s.getBitmap()\n" % self.variable["hasoferReliabilityIndexSensitivityGraph"]
+ txt += "print 'postscript =', %s.getPostscript()\n" % self.variable["hasoferReliabilityIndexSensitivityGraph"]
txt += "\n"
if ( self.DictMCVal.has_key( 'TvedtApproximation' ) ):
if ( self.DictMCVal[ 'TvedtApproximation' ] == "yes" ):
txt += "%s = %s.getEventProbabilityTvedt()\n" % (self.variable["tvedtApproximation"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["tvedtApproximation"], self.variable["tvedtApproximation"])
+ txt += "print '%s =', %s\n" % ("Tvedt Approximation", self.variable["tvedtApproximation"])
txt += "\n"
if ( self.DictMCVal.has_key( 'HohenBichlerApproximation' ) ):
if ( self.DictMCVal[ 'HohenBichlerApproximation' ] == "yes" ):
txt += "%s = %s.getEventProbabilityHohenBichler()\n" % (self.variable["hohenBichlerApproximation"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["hohenBichlerApproximation"], self.variable["tvedtApproximation"])
+ txt += "print '%s =', %s\n" % ("HohenBichler Approximation", self.variable["tvedtApproximation"])
txt += "\n"
if ( self.DictMCVal.has_key( 'BreitungApproximation' ) ):
if ( self.DictMCVal[ 'BreitungApproximation' ] == "yes" ):
txt += "%s = %s.getEventProbabilityBreitung()\n" % (self.variable["breitungApproximation"], self.variable["myResult"])
- txt += "print '%s =', %s\n" % (self.variable["breitungApproximation"], self.variable["breitungApproximation"])
+ txt += "print '%s =', %s\n" % ("Breitung Approximation", self.variable["breitungApproximation"])
txt += "\n"
txt = "# Evenement de defaillance\n"
txt += "%s = Event( %s, ComparisonOperator( %s() ), %s )\n" % (self.variable["myEvent"], self.variable["outputRandomVector"], operator, threshold)
+ txt += "%s.setName( '%s' )\n" % (self.variable["myEvent"], "myEvent")
txt += "\n"
return txt
'''
Methode de tirage d importance
'''
+ dimension = 0
+ if ( self.DictMCVal.has_key( 'MeanVector' ) ):
+ meanVector = self.DictMCVal[ 'MeanVector' ]
+ dimension = len( meanVector )
+
txt = "# Simulation par Tirage d'importance\n"
- txt += "%s = ImportanceSampling( %s )\n" % (self.variable["myAlgo"], self.variable["myEvent"])
+ txt += "# Densite d'importance\n"
+ txt += "%s = NumericalPoint( %s )\n" % (self.variable["meanVector"], self.variable["n"])
+ for i in range(dimension):
+ txt += "%s[%d] = %g\n" % (self.variable["meanVector"], i, meanVector[i])
+
+ txt += "%s = Normal( %s, CovarianceMatrix( IdentityMatrix( %s ) ) )\n" % (self.variable["importanceDensity"], self.variable["meanVector"], self.variable["n"])
+ txt += "%s = ImportanceSampling( %s, Distribution( %s ) )\n" % (self.variable["myAlgo"], self.variable["myEvent"], self.variable["importanceDensity"])
txt += "\n"
return txt
'''
Do the computation
'''
+ txt = ""
if ( self.DictMCVal.has_key( 'FunctionCallsNumber' ) ):
if ( self.DictMCVal[ 'FunctionCallsNumber' ] == "yes" ):
- txt = "%s = %s.getEvaluationCallsNumber()\n" % (self.variable["modelEvaluationCalls"], self.variable["model"])
+ txt += "%s = %s.getEvaluationCallsNumber()\n" % (self.variable["modelEvaluationCalls"], self.variable["model"])
txt += "%s = %s.getGradientCallsNumber()\n" % (self.variable["modelGradientCalls"], self.variable["model"])
txt += "%s = %s.getHessianCallsNumber()\n" % (self.variable["modelHessianCalls"], self.variable["model"])
txt += "\n"
txt += "%s = %s.getGradientCallsNumber() - %s\n" % (self.variable["modelGradientCalls"], self.variable["model"], self.variable["modelGradientCalls"])
txt += "%s = %s.getHessianCallsNumber() - %s\n" % (self.variable["modelHessianCalls"], self.variable["model"], self.variable["modelHessianCalls"])
txt += "\n"
- txt += "print '%s =', %s\n" % (self.variable["modelEvaluationCalls"], self.variable["modelEvaluationCalls"])
- txt += "print '%s =', %s\n" % (self.variable["modelGradientCalls"], self.variable["modelGradientCalls"])
- txt += "print '%s =', %s\n" % (self.variable["modelHessianCalls"], self.variable["modelHessianCalls"])
+ txt += "print '%s =', %s\n" % ("model Evaluation Calls", self.variable["modelEvaluationCalls"])
+ txt += "print '%s =', %s\n" % ("model Gradient Calls", self.variable["modelGradientCalls"])
+ txt += "print '%s =', %s\n" % ("model Hessian Calls", self.variable["modelHessianCalls"])
txt += "\n"
return txt
txt += "\n"
return txt
- def Beta (self, loi, i, collection):
+ def Beta (self, loi):
'''
Definition de la loi Beta
'''
txt = "Beta( %g, %g, %g, %g, %s )" % (arg1, arg2, arg3, arg4, settings[ loi[ 'Settings' ] ])
return txt
- def Exponential (self, loi, i, collection):
+ def Exponential (self, loi):
'''
Definition de la loi Exponential
'''
txt = "Exponential( %g, %g )" % (arg1, arg2)
return txt
- def Gamma (self, loi, i, collection):
+ def Gamma (self, loi):
'''
Definition de la loi Gamma
'''
txt = "Gamma( %g, %g, %g, %s )" % (arg1, arg2, arg3, settings[ loi[ 'Settings' ] ])
return txt
- def Geometric (self, loi, i, collection):
+ def Geometric (self, loi):
'''
Definition de la loi Geometric
'''
txt = "Geometric( %g )" % loi[ 'P' ]
return txt
- def Gumbel (self, loi, i, collection):
+ def Gumbel (self, loi):
'''
Definition de la loi Gumbel
'''
settings = {
- "AlphaBeta" : "Gamma.ALPHABETA",
- "MuSigma" : "Gamma.MUSIGMA",
+ "AlphaBeta" : "Gumbel.ALPHABETA",
+ "MuSigma" : "Gumbel.MUSIGMA",
}
if loi[ 'Settings' ] == 'AlphaBeta' :
arg1 = loi[ 'Alpha' ]
arg1 = loi[ 'Mu' ]
arg2 = loi[ 'Sigma' ]
- txt = "Gamma( %g, %g, %s )" % (arg1, arg2, settings[ loi[ 'Settings' ] ])
+ txt = "Gumbel( %g, %g, %s )" % (arg1, arg2, settings[ loi[ 'Settings' ] ])
return txt
- def Histogram (self, loi, i, collection):
+ def Histogram (self, loi):
'''
Definition de la loi Histogram
'''
txt = "Histogram( %g, %s )" % (arg1, arg2)
return txt
- def Logistic (self, loi, i, collection):
+ def Laplace (self, loi):
+ '''
+ Definition de la loi Laplace
+ '''
+ arg1 = loi[ 'Lambda' ]
+ arg2 = loi[ 'Mu' ]
+ txt = "Laplace( %g, %g )" % (arg1, arg2)
+ return txt
+
+ def Logistic (self, loi):
'''
Definition de la loi Logistic
'''
txt = "Logistic( %g, %g )" % (arg1, arg2)
return txt
- def LogNormal (self, loi, i, collection):
+ def LogNormal (self, loi):
'''
Definition de la loi LogNormal
'''
txt = "LogNormal( %g, %g, %g, %s )" % (arg1, arg2, arg3, settings[ loi[ 'Settings' ] ])
return txt
- def MultiNomial (self, loi, i, collection):
+ def MultiNomial (self, loi):
'''
Definition de la loi MultiNomial
'''
txt = "MultiNomial( NumericalPoint( %s ) , %d)" % (arg1, arg2)
return txt
- def Normal (self, loi, i, collection):
+ def NonCentralStudent (self, loi):
+ '''
+ Definition de la loi NonCentralStudent
+ '''
+ arg1 = loi[ 'Nu' ]
+ arg2 = loi[ 'Delta' ]
+ arg3 = loi[ 'Gamma' ]
+ txt = "NonCentralStudent( %g, %g )" % (arg1, arg2, arg3)
+ return txt
+
+ def Normal (self, loi):
'''
Definition de la loi Normal
'''
txt = "Normal( %g, %g )" % (arg1, arg2)
return txt
- def TruncatedNormal (self, loi, i, collection):
+ def TruncatedNormal (self, loi):
'''
Definition de la loi TruncatedNormal
'''
txt = "TruncatedNormal( %g, %g, %g, %g )" % (arg1, arg2, arg3, arg4)
return txt
- def Poisson (self, loi, i, collection):
+ def Poisson (self, loi):
'''
- Definition de la loi
+ Definition de la loi Poisson
'''
txt = "Poisson( %g )" % loi[ 'Lambda' ]
return txt
- def Student (self, loi, i, collection):
+ def Rayleigh (self, loi):
+ '''
+ Definition de la loi Rayleigh
+ '''
+ arg1 = loi[ 'Sigma' ]
+ arg2 = loi[ 'Gamma' ]
+ txt = "Rayleigh( %g, %g )" % (arg1, arg2)
+ return txt
+
+ def Student (self, loi):
'''
Definition de la loi Student
'''
arg1 = loi[ 'Mu' ]
arg2 = loi[ 'Nu' ]
- txt = "Student( %g, %g )" % (arg1, arg2)
+ arg3 = loi[ 'Sigma' ]
+ txt = "Student( %g, %g, %g )" % (arg1, arg2, arg3)
return txt
- def Triangular (self, loi, i, collection):
+ def Triangular (self, loi):
'''
Definition de la loi Triangular
'''
txt = "Triangular( %g, %g, %g )" % (arg1, arg2, arg3)
return txt
- def Uniform (self, loi, i, collection):
+ def Uniform (self, loi):
'''
Definition de la loi Uniform
'''
txt = "Uniform( %g, %g )" % (arg1, arg2)
return txt
- def UserDefined (self, loi, i, collection):
+ def UserDefined (self, loi):
'''
Definition de la loi UserDefined
'''
txt = "** UserDefined not defined yet **"
return txt
- def Weibull (self, loi, i, collection):
+ def Weibull (self, loi):
'''
Definition de la loi Weibull
'''
arg2 = loi[ 'Sigma' ]
arg3 = loi[ 'Gamma' ]
- txt = "Weibull( %g, %g, %s )" % (arg1, arg2, arg3, settings[ loi[ 'Settings' ] ])
+ txt = "Weibull( %g, %g, %g, %s )" % (arg1, arg2, arg3, settings[ loi[ 'Settings' ] ])
return txt
+
+
+ def GraphiquePDF (self, loi, chemin, fichier):
+ '''
+ Produit une image PNG representant la PDF de la loi
+ '''
+ txt = headerSTD % self.OpenTURNS_path
+ txt += "dist = %s\n" % apply( STDGenerateur.__dict__[ loi[ 'Kind' ] ], (self, loi) )
+ txt += "graph = dist.drawPDF()\n"
+ txt += "graph.draw( '%s/', '%s' , 640, 480, GraphImplementation.PNG)\n" % (chemin, fichier)
+ txt += footerSTD
+ return txt
+
