From: Jean-Philippe ARGAUD Date: Mon, 24 Jun 2013 12:45:50 +0000 (+0200) Subject: Improving redundancy treatment in Finite Differences calculations X-Git-Tag: V7_3_0~27 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=69994e4d2fd9793c4d876251ffafd443bf9f917a;p=modules%2Fadao.git Improving redundancy treatment in Finite Differences calculations --- diff --git a/src/daComposant/daCore/AssimilationStudy.py b/src/daComposant/daCore/AssimilationStudy.py index 8a1c4e1..e666b47 100644 --- a/src/daComposant/daCore/AssimilationStudy.py +++ b/src/daComposant/daCore/AssimilationStudy.py @@ -278,7 +278,7 @@ class AssimilationStudy: "withIncrement" :0.01, "withdX" :None, "withAvoidingRedundancy" :True, - "withToleranceInRedundancy" :1.e-15, + "withToleranceInRedundancy" :1.e-18, "withLenghtOfRedundancy" :-1, } """ @@ -289,7 +289,7 @@ class AssimilationStudy: if not asFunction.has_key("withIncrement"): asFunction["withIncrement"] = 0.01 if not asFunction.has_key("withdX"): asFunction["withdX"] = None if not asFunction.has_key("withAvoidingRedundancy"): asFunction["withAvoidingRedundancy"] = True - if not asFunction.has_key("withToleranceInRedundancy"): asFunction["withToleranceInRedundancy"] = 1.e-15 + if not asFunction.has_key("withToleranceInRedundancy"): asFunction["withToleranceInRedundancy"] = 1.e-18 if not asFunction.has_key("withLenghtOfRedundancy"): asFunction["withLenghtOfRedundancy"] = -1 from daNumerics.ApproximatedDerivatives import FDApproximation FDA = FDApproximation( @@ -375,7 +375,7 @@ class AssimilationStudy: "withIncrement" :0.01, "withdX" :None, "withAvoidingRedundancy" :True, - "withToleranceInRedundancy" :1.e-15, + "withToleranceInRedundancy" :1.e-18, "withLenghtOfRedundancy" :-1, } """ @@ -386,7 +386,7 @@ class AssimilationStudy: if not asFunction.has_key("withIncrement"): asFunction["withIncrement"] = 0.01 if not asFunction.has_key("withdX"): asFunction["withdX"] = None if not asFunction.has_key("withAvoidingRedundancy"): asFunction["withAvoidingRedundancy"] = True - if not asFunction.has_key("withToleranceInRedundancy"): asFunction["withToleranceInRedundancy"] = 1.e-15 + if not asFunction.has_key("withToleranceInRedundancy"): asFunction["withToleranceInRedundancy"] = 1.e-18 if not asFunction.has_key("withLenghtOfRedundancy"): asFunction["withLenghtOfRedundancy"] = -1 from daNumerics.ApproximatedDerivatives import FDApproximation FDA = FDApproximation( diff --git a/src/daComposant/daNumerics/ApproximatedDerivatives.py b/src/daComposant/daNumerics/ApproximatedDerivatives.py index ac5c73e..3a01ad9 100644 --- a/src/daComposant/daNumerics/ApproximatedDerivatives.py +++ b/src/daComposant/daNumerics/ApproximatedDerivatives.py @@ -25,7 +25,7 @@ __doc__ = """ """ __author__ = "Jean-Philippe ARGAUD" -import os, numpy, time +import os, numpy, time, copy import logging # logging.getLogger().setLevel(logging.DEBUG) @@ -46,7 +46,7 @@ class FDApproximation: increment = 0.01, dX = None, avoidingRedundancy = True, - toleranceInRedundancy = 1.e-15, + toleranceInRedundancy = 1.e-18, lenghtOfRedundancy = -1, ): self.__userFunction = Function @@ -54,7 +54,8 @@ class FDApproximation: if avoidingRedundancy: self.__avoidRC = True self.__tolerBP = float(toleranceInRedundancy) - self.__lenghtR = int(lenghtOfRedundancy) + self.__lenghtRH = int(lenghtOfRedundancy) + self.__lenghtRJ = int(lenghtOfRedundancy) self.__listDPCP = [] # Direct Operator Previous Calculated Points self.__listDPCR = [] # Direct Operator Previous Calculated Results self.__listDPCN = [] # Direct Operator Previous Calculated Point Norms @@ -79,13 +80,13 @@ class FDApproximation: # --------------------------------------------------------- def __doublon__(self, e, l, n, v=""): - __ac, __i = False, -1 + __ac, __iac = False, -1 for i in xrange(len(l)-1,-1,-1): if numpy.linalg.norm(e - l[i]) < self.__tolerBP * n[i]: - __ac, __i = True, i - if v is not None: logging.debug("FDA Cas%s déja calculé, récupération du doublon"%v) + __ac, __iac = True, i + if v is not None: logging.debug("FDA Cas%s déja calculé, récupération du doublon %i"%(v,__iac)) break - return __ac, __i + return __ac, __iac # --------------------------------------------------------- def DirectOperator(self, X ): @@ -99,22 +100,23 @@ class FDApproximation: __alreadyCalculated, __i = self.__doublon__(_X, self.__listDPCP, self.__listDPCN, " H") # if __alreadyCalculated: - logging.debug("FDA Calcul DirectOperator (par récupération)") + logging.debug("FDA Calcul DirectOperator (par récupération du doublon %i)"%__i) _HX = self.__listDPCR[__i] else: logging.debug("FDA Calcul DirectOperator (explicite)") - _HX = self.__userFunction( _X ) + _HX = numpy.ravel(self.__userFunction( _X )) if self.__avoidRC: - if self.__lenghtR < 0: self.__lenghtR = 2 * _X.size - if len(self.__listDPCP) > self.__lenghtR: + if self.__lenghtRH < 0: self.__lenghtRH = 2 * _X.size + if len(self.__listDPCP) > self.__lenghtRH: + logging.debug("FDA Réduction de la liste de H à la taille %i"%self.__lenghtRH) self.__listDPCP.pop(0) self.__listDPCR.pop(0) self.__listDPCN.pop(0) - self.__listDPCP.append( _X ) - self.__listDPCR.append( _HX ) + self.__listDPCP.append( copy.copy(_X) ) + self.__listDPCR.append( copy.copy(_HX) ) self.__listDPCN.append( numpy.linalg.norm(_X) ) # - return numpy.ravel( _HX ) + return _HX # --------------------------------------------------------- def TangentMatrix(self, X ): @@ -169,9 +171,10 @@ class FDApproximation: __bidon, __alreadyCalculatedI = self.__doublon__(_dX, self.__listJPCI, self.__listJPIN, None) if __alreadyCalculatedP == __alreadyCalculatedI > -1: __alreadyCalculated, __i = True, __alreadyCalculatedP + logging.debug("FDA Cas J déja calculé, récupération du doublon %i"%__i) # if __alreadyCalculated: - logging.debug("FDA Calcul Jacobienne (par récupération)") + logging.debug("FDA Calcul Jacobienne (par récupération du doublon %i)"%__i) _Jacobienne = self.__listJPCR[__i] else: logging.debug("FDA Calcul Jacobienne (explicite)") @@ -206,16 +209,17 @@ class FDApproximation: # _Jacobienne = numpy.matrix( numpy.vstack( _Jacobienne ) ).T if self.__avoidRC: - if self.__lenghtR < 0: self.__lenghtR = 2 * _X.size - if len(self.__listJPCP) > self.__lenghtR: + if self.__lenghtRJ < 0: self.__lenghtRJ = 2 * _X.size + if len(self.__listJPCP) > self.__lenghtRJ: + logging.debug("FDA Réduction de la liste de J à la taille %i"%self.__lenghtRJ) self.__listJPCP.pop(0) self.__listJPCI.pop(0) self.__listJPCR.pop(0) self.__listJPPN.pop(0) self.__listJPIN.pop(0) - self.__listJPCP.append( _X ) - self.__listJPCI.append( _dX ) - self.__listJPCR.append( _Jacobienne ) + self.__listJPCP.append( copy.copy(_X) ) + self.__listJPCI.append( copy.copy(_dX) ) + self.__listJPCR.append( copy.copy(_Jacobienne) ) self.__listJPPN.append( numpy.linalg.norm(_X) ) self.__listJPIN.append( numpy.linalg.norm(_Jacobienne) ) #