X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaCore%2FBasicObjects.py;h=41c5f106306e49da36699f7c9c7ea240fea51e15;hb=fd108dc05d4e654b7ab313526be991610d5388b0;hp=e5cccdfbfafb973593c9d61d9fd23f67bf023678;hpb=5fac19ce93a651a5e80cda6abc4d59b738153070;p=modules%2Fadao.git diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py index e5cccdf..41c5f10 100644 --- a/src/daComposant/daCore/BasicObjects.py +++ b/src/daComposant/daCore/BasicObjects.py @@ -1,77 +1,88 @@ -#-*-coding:iso-8859-1-*- +# -*- coding: utf-8 -*- # -# Copyright (C) 2008-2015 EDF R&D +# Copyright (C) 2008-2018 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 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. +# 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 +# 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 +# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com # -# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D +# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D """ - Définit les outils généraux élémentaires. + Définit les outils généraux élémentaires. - Ce module est destiné à etre appelée par AssimilationStudy pour constituer - les objets élémentaires de l'algorithme. + Ce module est destiné à être appelée par AssimilationStudy. """ __author__ = "Jean-Philippe ARGAUD" __all__ = [] -import logging, copy +import os +import sys +import logging +import copy import numpy -import Persistence -import PlatformInfo +from daCore import Persistence +from daCore import PlatformInfo +from daCore import Interfaces +from daCore import Templates +from daCore.Interfaces import ImportFromScript # ============================================================================== -class CacheManager: +class CacheManager(object): """ - Classe générale de gestion d'un cache de calculs + Classe générale de gestion d'un cache de calculs """ def __init__(self, - toleranceInRedundancy = 1.e-18, - lenghtOfRedundancy = -1, - ): + toleranceInRedundancy = 1.e-18, + lenghtOfRedundancy = -1, + ): """ - Les caractéristiques de tolérance peuvent être modifées à la création. + Les caractéristiques de tolérance peuvent être modifées à la création. """ self.__tolerBP = float(toleranceInRedundancy) self.__lenghtOR = int(lenghtOfRedundancy) + self.__initlnOR = self.__lenghtOR self.clearCache() def clearCache(self): + "Vide le cache" self.__listOPCV = [] # Operator Previous Calculated Points, Results, Point Norms - # logging.debug("CM Tolerance de determination des doublons : %.2e"%self.__tolerBP) + # logging.debug("CM Tolerance de determination des doublons : %.2e", self.__tolerBP) def wasCalculatedIn(self, xValue ): #, info="" ): + "Vérifie l'existence d'un calcul correspondant à la valeur" __alc = False __HxV = None - for i in xrange(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1): + for i in range(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1): if xValue.size != self.__listOPCV[i][0].size: - # logging.debug("CM Différence de la taille %s de X et de celle %s du point %i déjà calculé"%(xValue.shape,i,self.__listOPCP[i].shape)) + # logging.debug("CM Différence de la taille %s de X et de celle %s du point %i déjà calculé", xValue.shape,i,self.__listOPCP[i].shape) continue if numpy.linalg.norm(numpy.ravel(xValue) - self.__listOPCV[i][0]) < self.__tolerBP * self.__listOPCV[i][2]: __alc = True __HxV = self.__listOPCV[i][1] - # logging.debug("CM Cas%s déja calculé, portant le numéro %i"%(info,i)) + # logging.debug("CM Cas%s déja calculé, portant le numéro %i", info, i) break return __alc, __HxV def storeValueInX(self, xValue, HxValue ): - if self.__lenghtOR < 0: self.__lenghtOR = 2 * xValue.size + 2 + "Stocke un calcul correspondant à la valeur" + if self.__lenghtOR < 0: + self.__lenghtOR = 2 * xValue.size + 2 + self.__initlnOR = self.__lenghtOR while len(self.__listOPCV) > self.__lenghtOR: - # logging.debug("CM Réduction de la liste des cas à %i éléments par suppression du premier"%self.__lenghtOR) + # logging.debug("CM Réduction de la liste des cas à %i éléments par suppression du premier", self.__lenghtOR) self.__listOPCV.pop(0) self.__listOPCV.append( ( copy.copy(numpy.ravel(xValue)), @@ -79,10 +90,19 @@ class CacheManager: numpy.linalg.norm(xValue), ) ) + def disable(self): + "Inactive le cache" + self.__initlnOR = self.__lenghtOR + self.__lenghtOR = 0 + + def enable(self): + "Active le cache" + self.__lenghtOR = self.__initlnOR + # ============================================================================== -class Operator: +class Operator(object): """ - Classe générale d'interface de type opérateur + Classe générale d'interface de type opérateur simple """ NbCallsAsMatrix = 0 NbCallsAsMethod = 0 @@ -91,17 +111,17 @@ class Operator: # def __init__(self, fromMethod=None, fromMatrix=None, avoidingRedundancy = True): """ - On construit un objet de ce type en fournissant à l'aide de l'un des - deux mots-clé, soit une fonction python, soit une matrice. + On construit un objet de ce type en fournissant à l'aide de l'un des + deux mots-clé, soit une fonction python, soit une matrice. Arguments : - fromMethod : argument de type fonction Python - - fromMatrix : argument adapté au constructeur numpy.matrix - - avoidingRedundancy : évite ou pas les calculs redondants + - fromMatrix : argument adapté au constructeur numpy.matrix + - avoidingRedundancy : évite ou pas les calculs redondants """ self.__NbCallsAsMatrix, self.__NbCallsAsMethod, self.__NbCallsOfCached = 0, 0, 0 self.__AvoidRC = bool( avoidingRedundancy ) if fromMethod is not None: - self.__Method = fromMethod + self.__Method = fromMethod # logtimer(fromMethod) self.__Matrix = None self.__Type = "Method" elif fromMatrix is not None: @@ -113,47 +133,66 @@ class Operator: self.__Matrix = None self.__Type = None + def disableAvoidingRedundancy(self): + "Inactive le cache" + Operator.CM.disable() + + def enableAvoidingRedundancy(self): + "Active le cache" + if self.__AvoidRC: + Operator.CM.enable() + else: + Operator.CM.disable() + def isType(self): + "Renvoie le type" return self.__Type - def appliedTo(self, xValue): + def appliedTo(self, xValue, HValue = None): """ - Permet de restituer le résultat de l'application de l'opérateur à un - argument xValue. Cette méthode se contente d'appliquer, son argument - devant a priori être du bon type. + Permet de restituer le résultat de l'application de l'opérateur à un + argument xValue. Cette méthode se contente d'appliquer, son argument + devant a priori être du bon type. Arguments : - - xValue : argument adapté pour appliquer l'opérateur + - xValue : argument adapté pour appliquer l'opérateur """ - if self.__AvoidRC: - __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xValue) - else: - __alreadyCalculated = False - # - if __alreadyCalculated: - self.__addOneCacheCall() - HxValue = __HxV - else: - if self.__Matrix is not None: - self.__addOneMatrixCall() - HxValue = self.__Matrix * xValue - else: - self.__addOneMethodCall() - HxValue = self.__Method( xValue ) + if HValue is not None: + HxValue = numpy.asmatrix( numpy.ravel( HValue ) ).T if self.__AvoidRC: Operator.CM.storeValueInX(xValue,HxValue) + else: + if self.__AvoidRC: + __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xValue) + else: + __alreadyCalculated = False + # + if __alreadyCalculated: + self.__addOneCacheCall() + HxValue = __HxV + else: + if self.__Matrix is not None: + self.__addOneMatrixCall() + HxValue = self.__Matrix * xValue + else: + self.__addOneMethodCall() + HxValue = self.__Method( xValue ) + if self.__AvoidRC: + Operator.CM.storeValueInX(xValue,HxValue) # return HxValue - def appliedControledFormTo(self, (xValue, uValue) ): + def appliedControledFormTo(self, paire ): """ - Permet de restituer le résultat de l'application de l'opérateur à une - paire (xValue, uValue). Cette méthode se contente d'appliquer, son - argument devant a priori être du bon type. Si la uValue est None, - on suppose que l'opérateur ne s'applique qu'à xValue. + Permet de restituer le résultat de l'application de l'opérateur à une + paire (xValue, uValue). Cette méthode se contente d'appliquer, son + argument devant a priori être du bon type. Si la uValue est None, + on suppose que l'opérateur ne s'applique qu'à xValue. Arguments : - - xValue : argument X adapté pour appliquer l'opérateur - - uValue : argument U adapté pour appliquer l'opérateur + - xValue : argument X adapté pour appliquer l'opérateur + - uValue : argument U adapté pour appliquer l'opérateur """ + assert len(paire) == 2, "Incorrect number of arguments" + xValue, uValue = paire if self.__Matrix is not None: self.__addOneMatrixCall() return self.__Matrix * xValue @@ -164,19 +203,21 @@ class Operator: self.__addOneMethodCall() return self.__Method( xValue ) - def appliedInXTo(self, (xNominal, xValue) ): + def appliedInXTo(self, paire ): """ - Permet de restituer le résultat de l'application de l'opérateur à un - argument xValue, sachant que l'opérateur est valable en xNominal. - Cette méthode se contente d'appliquer, son argument devant a priori - être du bon type. Si l'opérateur est linéaire car c'est une matrice, - alors il est valable en tout point nominal et il n'est pas nécessaire + Permet de restituer le résultat de l'application de l'opérateur à un + argument xValue, sachant que l'opérateur est valable en xNominal. + Cette méthode se contente d'appliquer, son argument devant a priori + être du bon type. Si l'opérateur est linéaire car c'est une matrice, + alors il est valable en tout point nominal et il n'est pas nécessaire d'utiliser xNominal. Arguments : une liste contenant - - xNominal : argument permettant de donner le point où l'opérateur - est construit pour etre ensuite appliqué - - xValue : argument adapté pour appliquer l'opérateur + - xNominal : argument permettant de donner le point où l'opérateur + est construit pour etre ensuite appliqué + - xValue : argument adapté pour appliquer l'opérateur """ + assert len(paire) == 2, "Incorrect number of arguments" + xNominal, xValue = paire if self.__Matrix is not None: self.__addOneMatrixCall() return self.__Matrix * xValue @@ -186,7 +227,7 @@ class Operator: def asMatrix(self, ValueForMethodForm = "UnknownVoidValue"): """ - Permet de renvoyer l'opérateur sous la forme d'une matrice + Permet de renvoyer l'opérateur sous la forme d'une matrice """ if self.__Matrix is not None: self.__addOneMatrixCall() @@ -199,7 +240,7 @@ class Operator: def shape(self): """ - Renvoie la taille sous forme numpy si l'opérateur est disponible sous + Renvoie la taille sous forme numpy si l'opérateur est disponible sous la forme d'une matrice """ if self.__Matrix is not None: @@ -209,7 +250,7 @@ class Operator: def nbcalls(self, which=None): """ - Renvoie les nombres d'évaluations de l'opérateur + Renvoie les nombres d'évaluations de l'opérateur """ __nbcalls = ( self.__NbCallsAsMatrix+self.__NbCallsAsMethod, @@ -225,100 +266,333 @@ class Operator: else: return __nbcalls[which] def __addOneMatrixCall(self): + "Comptabilise un appel" self.__NbCallsAsMatrix += 1 # Decompte local Operator.NbCallsAsMatrix += 1 # Decompte global def __addOneMethodCall(self): + "Comptabilise un appel" self.__NbCallsAsMethod += 1 # Decompte local Operator.NbCallsAsMethod += 1 # Decompte global def __addOneCacheCall(self): + "Comptabilise un appel" self.__NbCallsOfCached += 1 # Decompte local Operator.NbCallsOfCached += 1 # Decompte global # ============================================================================== -class Algorithm: +class FullOperator(object): + """ + Classe générale d'interface de type opérateur complet + (Direct, Linéaire Tangent, Adjoint) + """ + def __init__(self, + name = "GenericFullOperator", + asMatrix = None, + asOneFunction = None, # Fonction + asThreeFunctions = None, # Fonctions dictionary + asScript = None, # Fonction(s) script + asDict = None, # Parameters + appliedInX = None, + avoidRC = True, + scheduledBy = None, + toBeChecked = False, + ): + "" + self.__name = str(name) + self.__check = bool(toBeChecked) + # + self.__FO = {} + # + __Parameters = {} + if (asDict is not None) and isinstance(asDict, dict): + __Parameters.update( asDict ) + if "DifferentialIncrement" in asDict: + __Parameters["withIncrement"] = asDict["DifferentialIncrement"] + if "CenteredFiniteDifference" in asDict: + __Parameters["withCenteredDF"] = asDict["CenteredFiniteDifference"] + if "EnableMultiProcessing" in asDict: + __Parameters["withmpEnabled"] = asDict["EnableMultiProcessing"] + if "NumberOfProcesses" in asDict: + __Parameters["withmpWorkers"] = asDict["NumberOfProcesses"] + # + if asScript is not None: + __Matrix, __Function = None, None + if asMatrix: + __Matrix = ImportFromScript(asScript).getvalue( self.__name ) + elif asOneFunction: + __Function = { "Direct":ImportFromScript(asScript).getvalue( "DirectOperator" ) } + __Function.update({"useApproximatedDerivatives":True}) + __Function.update(__Parameters) + elif asThreeFunctions: + __Function = { + "Direct" :ImportFromScript(asScript).getvalue( "DirectOperator" ), + "Tangent":ImportFromScript(asScript).getvalue( "TangentOperator" ), + "Adjoint":ImportFromScript(asScript).getvalue( "AdjointOperator" ), + } + __Function.update(__Parameters) + else: + __Matrix = asMatrix + if asOneFunction is not None: + if isinstance(asOneFunction, dict) and "Direct" in asOneFunction: + if asOneFunction["Direct"] is not None: + __Function = asOneFunction + else: + raise ValueError("The function has to be given in a dictionnary which have 1 key (\"Direct\")") + else: + __Function = { "Direct":asOneFunction } + __Function.update({"useApproximatedDerivatives":True}) + __Function.update(__Parameters) + elif asThreeFunctions is not None: + if isinstance(asThreeFunctions, dict) and \ + ("Tangent" in asThreeFunctions) and (asThreeFunctions["Tangent"] is not None) and \ + ("Adjoint" in asThreeFunctions) and (asThreeFunctions["Adjoint"] is not None) and \ + (("useApproximatedDerivatives" not in asThreeFunctions) or not bool(asThreeFunctions["useApproximatedDerivatives"])): + __Function = asThreeFunctions + elif isinstance(asThreeFunctions, dict) and \ + ("Direct" in asThreeFunctions) and (asThreeFunctions["Direct"] is not None): + __Function = asThreeFunctions + __Function.update({"useApproximatedDerivatives":True}) + else: + raise ValueError("The functions has to be given in a dictionnary which have either 1 key (\"Direct\") or 3 keys (\"Direct\" (optionnal), \"Tangent\" and \"Adjoint\")") + if "Direct" not in asThreeFunctions: + __Function["Direct"] = asThreeFunctions["Tangent"] + __Function.update(__Parameters) + else: + __Function = None + # + # if sys.version_info[0] < 3 and isinstance(__Function, dict): + #  for k in ("Direct", "Tangent", "Adjoint"): + #  if k in __Function and hasattr(__Function[k],"__class__"): + #  if type(__Function[k]) is type(self.__init__): + #  raise TypeError("can't use a class method (%s) as a function for the \"%s\" operator. Use a real function instead."%(type(__Function[k]),k)) + # + if appliedInX is not None and isinstance(appliedInX, dict): + __appliedInX = appliedInX + elif appliedInX is not None: + __appliedInX = {"HXb":appliedInX} + else: + __appliedInX = None + # + if scheduledBy is not None: + self.__T = scheduledBy + # + if isinstance(__Function, dict) and \ + ("useApproximatedDerivatives" in __Function) and bool(__Function["useApproximatedDerivatives"]) and \ + ("Direct" in __Function) and (__Function["Direct"] is not None): + if "withCenteredDF" not in __Function: __Function["withCenteredDF"] = False + if "withIncrement" not in __Function: __Function["withIncrement"] = 0.01 + if "withdX" not in __Function: __Function["withdX"] = None + if "withAvoidingRedundancy" not in __Function: __Function["withAvoidingRedundancy"] = True + if "withToleranceInRedundancy" not in __Function: __Function["withToleranceInRedundancy"] = 1.e-18 + if "withLenghtOfRedundancy" not in __Function: __Function["withLenghtOfRedundancy"] = -1 + if "withmpEnabled" not in __Function: __Function["withmpEnabled"] = False + if "withmpWorkers" not in __Function: __Function["withmpWorkers"] = None + from daNumerics.ApproximatedDerivatives import FDApproximation + FDA = FDApproximation( + Function = __Function["Direct"], + centeredDF = __Function["withCenteredDF"], + increment = __Function["withIncrement"], + dX = __Function["withdX"], + avoidingRedundancy = __Function["withAvoidingRedundancy"], + toleranceInRedundancy = __Function["withToleranceInRedundancy"], + lenghtOfRedundancy = __Function["withLenghtOfRedundancy"], + mpEnabled = __Function["withmpEnabled"], + mpWorkers = __Function["withmpWorkers"], + ) + self.__FO["Direct"] = Operator( fromMethod = FDA.DirectOperator, avoidingRedundancy = avoidRC ) + self.__FO["Tangent"] = Operator( fromMethod = FDA.TangentOperator, avoidingRedundancy = avoidRC ) + self.__FO["Adjoint"] = Operator( fromMethod = FDA.AdjointOperator, avoidingRedundancy = avoidRC ) + elif isinstance(__Function, dict) and \ + ("Direct" in __Function) and ("Tangent" in __Function) and ("Adjoint" in __Function) and \ + (__Function["Direct"] is not None) and (__Function["Tangent"] is not None) and (__Function["Adjoint"] is not None): + self.__FO["Direct"] = Operator( fromMethod = __Function["Direct"], avoidingRedundancy = avoidRC ) + self.__FO["Tangent"] = Operator( fromMethod = __Function["Tangent"], avoidingRedundancy = avoidRC ) + self.__FO["Adjoint"] = Operator( fromMethod = __Function["Adjoint"], avoidingRedundancy = avoidRC ) + elif asMatrix is not None: + __matrice = numpy.matrix( __Matrix, numpy.float ) + self.__FO["Direct"] = Operator( fromMatrix = __matrice, avoidingRedundancy = avoidRC ) + self.__FO["Tangent"] = Operator( fromMatrix = __matrice, avoidingRedundancy = avoidRC ) + self.__FO["Adjoint"] = Operator( fromMatrix = __matrice.T, avoidingRedundancy = avoidRC ) + del __matrice + else: + raise ValueError("Improperly defined observation operator, it requires at minima either a matrix, a Direct for approximate derivatives or a Tangent/Adjoint pair.") + # + if __appliedInX is not None: + self.__FO["AppliedInX"] = {} + if not isinstance(__appliedInX, dict): + raise ValueError("Error: observation operator defined by \"AppliedInX\" need a dictionary as argument.") + for key in list(__appliedInX.keys()): + if type( __appliedInX[key] ) is type( numpy.matrix([]) ): + # Pour le cas où l'on a une vraie matrice + self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key].A1, numpy.float ).T + elif type( __appliedInX[key] ) is type( numpy.array([]) ) and len(__appliedInX[key].shape) > 1: + # Pour le cas où l'on a un vecteur représenté en array avec 2 dimensions + self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key].reshape(len(__appliedInX[key]),), numpy.float ).T + else: + self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key], numpy.float ).T + else: + self.__FO["AppliedInX"] = None + + def getO(self): + return self.__FO + + def __repr__(self): + "x.__repr__() <==> repr(x)" + return repr(self.__V) + + def __str__(self): + "x.__str__() <==> str(x)" + return str(self.__V) + +# ============================================================================== +class Algorithm(object): """ - Classe générale d'interface de type algorithme + Classe générale d'interface de type algorithme - Elle donne un cadre pour l'écriture d'une classe élémentaire d'algorithme + Elle donne un cadre pour l'écriture d'une classe élémentaire d'algorithme d'assimilation, en fournissant un container (dictionnaire) de variables - persistantes initialisées, et des méthodes d'accès à ces variables stockées. + persistantes initialisées, et des méthodes d'accès à ces variables stockées. - Une classe élémentaire d'algorithme doit implémenter la méthode "run". + Une classe élémentaire d'algorithme doit implémenter la méthode "run". """ def __init__(self, name): """ - L'initialisation présente permet de fabriquer des variables de stockage - disponibles de manière générique dans les algorithmes élémentaires. Ces - variables de stockage sont ensuite conservées dans un dictionnaire - interne à l'objet, mais auquel on accède par la méthode "get". + L'initialisation présente permet de fabriquer des variables de stockage + disponibles de manière générique dans les algorithmes élémentaires. Ces + variables de stockage sont ensuite conservées dans un dictionnaire + interne à l'objet, mais auquel on accède par la méthode "get". - Les variables prévues sont : + Les variables prévues sont : - CostFunctionJ : fonction-cout globale, somme des deux parties suivantes - - CostFunctionJb : partie ébauche ou background de la fonction-cout + - CostFunctionJb : partie ébauche ou background de la fonction-cout - CostFunctionJo : partie observations de la fonction-cout - GradientOfCostFunctionJ : gradient de la fonction-cout globale - - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-cout + - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-cout - GradientOfCostFunctionJo : gradient de la partie observations de la fonction-cout - - CurrentState : état courant lors d'itérations + - CurrentState : état courant lors d'itérations - Analysis : l'analyse Xa - - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche - - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant - - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum + - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche + - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant + - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum - Innovation : l'innovation : d = Y - H(X) + - InnovationAtCurrentState : l'innovation à l'état courant : dn = Y - H(Xn) - SigmaObs2 : indicateur de correction optimale des erreurs d'observation - - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche + - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche - MahalanobisConsistency : indicateur de consistance des covariances - OMA : Observation moins Analysis : Y - Xa - OMB : Observation moins Background : Y - Xb - AMB : Analysis moins Background : Xa - Xb - APosterioriCovariance : matrice A - APosterioriVariances : variances de la matrice A - - APosterioriStandardDeviations : écart-types de la matrice A + - APosterioriStandardDeviations : écart-types de la matrice A - APosterioriCorrelations : correlations de la matrice A - On peut rajouter des variables à stocker dans l'initialisation de - l'algorithme élémentaire qui va hériter de cette classe + - Residu : dans le cas des algorithmes de vérification + On peut rajouter des variables à stocker dans l'initialisation de + l'algorithme élémentaire qui va hériter de cette classe """ - logging.debug("%s Initialisation"%str(name)) + logging.debug("%s Initialisation", str(name)) self._m = PlatformInfo.SystemUsage() # self._name = str( name ) self._parameters = {"StoreSupplementaryCalculations":[]} self.__required_parameters = {} + self.__required_inputs = {"RequiredInputValues":{"mandatory":(), "optional":()}} + # self.StoredVariables = {} + self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ") + self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb") + self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo") + self.StoredVariables["CostFunctionJAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJAtCurrentOptimum") + self.StoredVariables["CostFunctionJbAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJbAtCurrentOptimum") + self.StoredVariables["CostFunctionJoAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJoAtCurrentOptimum") + self.StoredVariables["GradientOfCostFunctionJ"] = Persistence.OneVector(name = "GradientOfCostFunctionJ") + self.StoredVariables["GradientOfCostFunctionJb"] = Persistence.OneVector(name = "GradientOfCostFunctionJb") + self.StoredVariables["GradientOfCostFunctionJo"] = Persistence.OneVector(name = "GradientOfCostFunctionJo") + self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState") + self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis") + self.StoredVariables["IndexOfOptimum"] = Persistence.OneIndex(name = "IndexOfOptimum") + self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum") + self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground") + self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState") + self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum") + self.StoredVariables["SimulatedObservationAtCurrentOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentOptimum") + self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation") + self.StoredVariables["InnovationAtCurrentState"] = Persistence.OneVector(name = "InnovationAtCurrentState") + self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2") + self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2") + self.StoredVariables["MahalanobisConsistency"] = Persistence.OneScalar(name = "MahalanobisConsistency") + self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA") + self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB") + self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA") + self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance") + self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances") + self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations") + self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations") + self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles") + self.StoredVariables["Residu"] = Persistence.OneScalar(name = "Residu") + + def _pre_run(self, Parameters, Xb=None, Y=None, R=None, B=None, Q=None ): + "Pré-calcul" + logging.debug("%s Lancement", self._name) + logging.debug("%s Taille mémoire utilisée de %.0f Mio", self._name, self._m.getUsedMemory("Mio")) + # + # Mise a jour de self._parameters avec Parameters + self.__setParameters(Parameters) + # + # Corrections et complements + def __test_vvalue( argument, variable, argname): + if argument is None: + if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]: + raise ValueError("%s %s vector %s has to be properly defined!"%(self._name,argname,variable)) + elif variable in self.__required_inputs["RequiredInputValues"]["optional"]: + logging.debug("%s %s vector %s is not set, but is optional."%(self._name,argname,variable)) + else: + logging.debug("%s %s vector %s is not set, but is not required."%(self._name,argname,variable)) + else: + logging.debug("%s %s vector %s is set, and its size is %i."%(self._name,argname,variable,numpy.array(argument).size)) + __test_vvalue( Xb, "Xb", "Background or initial state" ) + __test_vvalue( Y, "Y", "Observation" ) + def __test_cvalue( argument, variable, argname): + if argument is None: + if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]: + raise ValueError("%s %s error covariance matrix %s has to be properly defined!"%(self._name,argname,variable)) + elif variable in self.__required_inputs["RequiredInputValues"]["optional"]: + logging.debug("%s %s error covariance matrix %s is not set, but is optional."%(self._name,argname,variable)) + else: + logging.debug("%s %s error covariance matrix %s is not set, but is not required."%(self._name,argname,variable)) + else: + logging.debug("%s %s error covariance matrix %s is set."%(self._name,argname,variable)) + __test_cvalue( R, "R", "Observation" ) + __test_cvalue( B, "B", "Background" ) + __test_cvalue( Q, "Q", "Evolution" ) + # + if ("Bounds" in self._parameters) and isinstance(self._parameters["Bounds"], (list, tuple)) and (len(self._parameters["Bounds"]) > 0): + logging.debug("%s Prise en compte des bornes effectuee"%(self._name,)) + else: + self._parameters["Bounds"] = None + # + if logging.getLogger().level < logging.WARNING: + self._parameters["optiprint"], self._parameters["optdisp"] = 1, 1 + if PlatformInfo.has_scipy: + import scipy.optimize + self._parameters["optmessages"] = scipy.optimize.tnc.MSG_ALL + else: + self._parameters["optmessages"] = 15 + else: + self._parameters["optiprint"], self._parameters["optdisp"] = -1, 0 + if PlatformInfo.has_scipy: + import scipy.optimize + self._parameters["optmessages"] = scipy.optimize.tnc.MSG_NONE + else: + self._parameters["optmessages"] = 15 # - self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ") - self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb") - self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo") - self.StoredVariables["GradientOfCostFunctionJ"] = Persistence.OneVector(name = "GradientOfCostFunctionJ") - self.StoredVariables["GradientOfCostFunctionJb"] = Persistence.OneVector(name = "GradientOfCostFunctionJb") - self.StoredVariables["GradientOfCostFunctionJo"] = Persistence.OneVector(name = "GradientOfCostFunctionJo") - self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState") - self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis") - self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground") - self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState") - self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum") - self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation") - self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2") - self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2") - self.StoredVariables["MahalanobisConsistency"] = Persistence.OneScalar(name = "MahalanobisConsistency") - self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA") - self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB") - self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA") - self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance") - self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances") - self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations") - self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations") - self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles") - - def _pre_run(self): - logging.debug("%s Lancement"%self._name) - logging.debug("%s Taille mémoire utilisée de %.1f Mio"%(self._name, self._m.getUsedMemory("Mio"))) return 0 def _post_run(self,_oH=None): - if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]: + "Post-calcul" + if ("StoreSupplementaryCalculations" in self._parameters) and \ + "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]: for _A in self.StoredVariables["APosterioriCovariance"]: if "APosterioriVariances" in self._parameters["StoreSupplementaryCalculations"]: self.StoredVariables["APosterioriVariances"].store( numpy.diag(_A) ) @@ -329,18 +603,18 @@ class Algorithm: _C = numpy.dot(_EI, numpy.dot(_A, _EI)) self.StoredVariables["APosterioriCorrelations"].store( _C ) if _oH is not None: - logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i"%(self._name, _oH["Direct"].nbcalls(0),_oH["Tangent"].nbcalls(0),_oH["Adjoint"].nbcalls(0))) - logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i"%(self._name, _oH["Direct"].nbcalls(3),_oH["Tangent"].nbcalls(3),_oH["Adjoint"].nbcalls(3))) - logging.debug("%s Taille mémoire utilisée de %.1f Mio"%(self._name, self._m.getUsedMemory("Mio"))) - logging.debug("%s Terminé"%self._name) + logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i", self._name, _oH["Direct"].nbcalls(0),_oH["Tangent"].nbcalls(0),_oH["Adjoint"].nbcalls(0)) + logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i", self._name, _oH["Direct"].nbcalls(3),_oH["Tangent"].nbcalls(3),_oH["Adjoint"].nbcalls(3)) + logging.debug("%s Taille mémoire utilisée de %.0f Mio", self._name, self._m.getUsedMemory("Mio")) + logging.debug("%s Terminé", self._name) return 0 def get(self, key=None): """ - Renvoie l'une des variables stockées identifiée par la clé, ou le + Renvoie l'une des variables stockées identifiée par la clé, ou le dictionnaire de l'ensemble des variables disponibles en l'absence de - clé. Ce sont directement les variables sous forme objet qui sont - renvoyées, donc les méthodes d'accès à l'objet individuel sont celles + clé. Ce sont directement les variables sous forme objet qui sont + renvoyées, donc les méthodes d'accès à l'objet individuel sont celles des classes de persistance. """ if key is not None: @@ -348,29 +622,43 @@ class Algorithm: else: return self.StoredVariables - def has_key(self, key=None): - """ - Vérifie si l'une des variables stockées est identifiée par la clé. - """ - return self.StoredVariables.has_key(key) + def __contains__(self, key=None): + "D.__contains__(k) -> True if D has a key k, else False" + return key in self.StoredVariables def keys(self): - """ - Renvoie la liste des clés de variables stockées. - """ - return self.StoredVariables.keys() + "D.keys() -> list of D's keys" + if hasattr(self, "StoredVariables"): + return self.StoredVariables.keys() + else: + return [] + + def pop(self, k, d): + "D.pop(k[,d]) -> v, remove specified key and return the corresponding value" + if hasattr(self, "StoredVariables"): + return self.StoredVariables.pop(k, d) + else: + try: + msg = "'%s'"%k + except: + raise TypeError("pop expected at least 1 arguments, got 0") + "If key is not found, d is returned if given, otherwise KeyError is raised" + try: + return d + except: + raise KeyError(msg) def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None): """ - Doit implémenter l'opération élémentaire de calcul d'assimilation sous - sa forme mathématique la plus naturelle possible. + Doit implémenter l'opération élémentaire de calcul d'assimilation sous + sa forme mathématique la plus naturelle possible. """ raise NotImplementedError("Mathematical assimilation calculation has not been implemented!") def defineRequiredParameter(self, name = None, default = None, typecast = None, message = None, minval = None, maxval = None, listval = None): """ - Permet de définir dans l'algorithme des paramètres requis et leurs - caractéristiques par défaut. + Permet de définir dans l'algorithme des paramètres requis et leurs + caractéristiques par défaut. """ if name is None: raise ValueError("A name is mandatory to define a required parameter.") @@ -383,23 +671,21 @@ class Algorithm: "listval" : listval, "message" : message, } - logging.debug("%s %s (valeur par défaut = %s)"%(self._name, message, self.setParameterValue(name))) + logging.debug("%s %s (valeur par défaut = %s)", self._name, message, self.setParameterValue(name)) def getRequiredParameters(self, noDetails=True): """ - Renvoie la liste des noms de paramètres requis ou directement le - dictionnaire des paramètres requis. + Renvoie la liste des noms de paramètres requis ou directement le + dictionnaire des paramètres requis. """ if noDetails: - ks = self.__required_parameters.keys() - ks.sort() - return ks + return sorted(self.__required_parameters.keys()) else: return self.__required_parameters def setParameterValue(self, name=None, value=None): """ - Renvoie la valeur d'un paramètre requis de manière contrôlée + Renvoie la valeur d'un paramètre requis de manière contrôlée """ default = self.__required_parameters[name]["default"] typecast = self.__required_parameters[name]["typecast"] @@ -416,12 +702,12 @@ class Algorithm: if typecast is None: __val = value else: __val = typecast( value ) # - if minval is not None and (numpy.array(__val) < minval).any(): + if minval is not None and (numpy.array(__val, float) < minval).any(): raise ValueError("The parameter named \"%s\" of value \"%s\" can not be less than %s."%(name, __val, minval)) - if maxval is not None and (numpy.array(__val) > maxval).any(): + if maxval is not None and (numpy.array(__val, float) > maxval).any(): raise ValueError("The parameter named \"%s\" of value \"%s\" can not be greater than %s."%(name, __val, maxval)) if listval is not None: - if typecast is list or typecast is tuple or type(__val) is list or type(__val) is tuple: + if typecast is list or typecast is tuple or isinstance(__val,list) or isinstance(__val,tuple): for v in __val: if v not in listval: raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%(v, name, listval)) @@ -429,9 +715,16 @@ class Algorithm: raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%( __val, name,listval)) return __val - def setParameters(self, fromDico={}): + def requireInputArguments(self, mandatory=(), optional=()): + """ + Permet d'imposer des arguments requises en entrée + """ + self.__required_inputs["RequiredInputValues"]["mandatory"] = tuple( mandatory ) + self.__required_inputs["RequiredInputValues"]["optional"] = tuple( optional ) + + def __setParameters(self, fromDico={}): """ - Permet de stocker les paramètres reçus dans le dictionnaire interne. + Permet de stocker les paramètres reçus dans le dictionnaire interne. """ self._parameters.update( fromDico ) for k in self.__required_parameters.keys(): @@ -439,93 +732,607 @@ class Algorithm: self._parameters[k] = self.setParameterValue(k,fromDico[k]) else: self._parameters[k] = self.setParameterValue(k) - logging.debug("%s %s : %s"%(self._name, self.__required_parameters[k]["message"], self._parameters[k])) + logging.debug("%s %s : %s", self._name, self.__required_parameters[k]["message"], self._parameters[k]) # ============================================================================== -class Diagnostic: +class AlgorithmAndParameters(object): """ - Classe générale d'interface de type diagnostic - - Ce template s'utilise de la manière suivante : il sert de classe "patron" en - même temps que l'une des classes de persistance, comme "OneScalar" par - exemple. - - Une classe élémentaire de diagnostic doit implémenter ses deux méthodes, la - méthode "_formula" pour écrire explicitement et proprement la formule pour - l'écriture mathématique du calcul du diagnostic (méthode interne non - publique), et "calculate" pour activer la précédente tout en ayant vérifié - et préparé les données, et pour stocker les résultats à chaque pas (méthode - externe d'activation). + Classe générale d'interface d'action pour l'algorithme et ses paramètres """ - def __init__(self, name = "", parameters = {}): - self.name = str(name) - self.parameters = dict( parameters ) + def __init__(self, + name = "GenericAlgorithm", + asAlgorithm = None, + asDict = None, + asScript = None, + ): + """ + """ + self.__name = str(name) + self.__A = None + self.__P = {} + # + self.__algorithm = {} + self.__algorithmFile = None + self.__algorithmName = None + # + self.updateParameters( asDict, asScript ) + # + if asAlgorithm is None and asScript is not None: + __Algo = ImportFromScript(asScript).getvalue( "Algorithm" ) + else: + __Algo = asAlgorithm + # + if __Algo is not None: + self.__A = str(__Algo) + self.__P.update( {"Algorithm":self.__A} ) + # + self.__setAlgorithm( self.__A ) + + def updateParameters(self, + asDict = None, + asScript = None, + ): + "Mise a jour des parametres" + if asDict is None and asScript is not None: + __Dict = ImportFromScript(asScript).getvalue( self.__name, "Parameters" ) + else: + __Dict = asDict + # + if __Dict is not None: + self.__P.update( dict(__Dict) ) + + def executePythonScheme(self, asDictAO = None): + "Permet de lancer le calcul d'assimilation" + Operator.CM.clearCache() + # + if not isinstance(asDictAO, dict): + raise ValueError("The objects for algorithm calculation have to be given together as a dictionnary, and they are not") + if hasattr(asDictAO["Background"],"getO"): self.__Xb = asDictAO["Background"].getO() + elif hasattr(asDictAO["CheckingPoint"],"getO"): self.__Xb = asDictAO["CheckingPoint"].getO() + else: self.__Xb = None + if hasattr(asDictAO["Observation"],"getO"): self.__Y = asDictAO["Observation"].getO() + else: self.__Y = asDictAO["Observation"] + if hasattr(asDictAO["ControlInput"],"getO"): self.__U = asDictAO["ControlInput"].getO() + else: self.__U = asDictAO["ControlInput"] + if hasattr(asDictAO["ObservationOperator"],"getO"): self.__HO = asDictAO["ObservationOperator"].getO() + else: self.__HO = asDictAO["ObservationOperator"] + if hasattr(asDictAO["EvolutionModel"],"getO"): self.__EM = asDictAO["EvolutionModel"].getO() + else: self.__EM = asDictAO["EvolutionModel"] + if hasattr(asDictAO["ControlModel"],"getO"): self.__CM = asDictAO["ControlModel"].getO() + else: self.__CM = asDictAO["ControlModel"] + self.__B = asDictAO["BackgroundError"] + self.__R = asDictAO["ObservationError"] + self.__Q = asDictAO["EvolutionError"] + # + self.__shape_validate() + # + self.__algorithm.run( + Xb = self.__Xb, + Y = self.__Y, + U = self.__U, + HO = self.__HO, + EM = self.__EM, + CM = self.__CM, + R = self.__R, + B = self.__B, + Q = self.__Q, + Parameters = self.__P, + ) + return 0 + + def executeYACSScheme(self, FileName=None): + "Permet de lancer le calcul d'assimilation" + if FileName is None or not os.path.exists(FileName): + raise ValueError("a YACS file name has to be given for YACS execution.\n") + if not PlatformInfo.has_salome or \ + not PlatformInfo.has_yacs or \ + not PlatformInfo.has_adao: + raise ImportError("\n\n"+\ + "Unable to get SALOME, YACS or ADAO environnement variables.\n"+\ + "Please load the right environnement before trying to use it.\n") + # + import pilot + import SALOMERuntime + import loader + SALOMERuntime.RuntimeSALOME_setRuntime() + + r = pilot.getRuntime() + xmlLoader = loader.YACSLoader() + xmlLoader.registerProcCataLoader() + try: + catalogAd = r.loadCatalog("proc", os.path.abspath(FileName)) + r.addCatalog(catalogAd) + except: + pass + + try: + p = xmlLoader.load(os.path.abspath(FileName)) + except IOError as ex: + print("The YACS XML schema file can not be loaded: %s"%(ex,)) + + logger = p.getLogger("parser") + if not logger.isEmpty(): + print("The imported YACS XML schema has errors on parsing:") + print(logger.getStr()) + + if not p.isValid(): + print("The YACS XML schema is not valid and will not be executed:") + print(p.getErrorReport()) + + info=pilot.LinkInfo(pilot.LinkInfo.ALL_DONT_STOP) + p.checkConsistency(info) + if info.areWarningsOrErrors(): + print("The YACS XML schema is not coherent and will not be executed:") + print(info.getGlobalRepr()) + + e = pilot.ExecutorSwig() + e.RunW(p) + if p.getEffectiveState() != pilot.DONE: + print(p.getErrorReport()) + # + return 0 + + def get(self, key = None): + "Vérifie l'existence d'une clé de variable ou de paramètres" + if key in self.__algorithm: + return self.__algorithm.get( key ) + elif key in self.__P: + return self.__P[key] + else: + return self.__P + + def pop(self, k, d): + "Necessaire pour le pickling" + return self.__algorithm.pop(k, d) + + def getAlgorithmRequiredParameters(self, noDetails=True): + "Renvoie la liste des paramètres requis selon l'algorithme" + return self.__algorithm.getRequiredParameters(noDetails) + + def setObserver(self, __V, __O, __I, __S): + if self.__algorithm is None \ + or isinstance(self.__algorithm, dict) \ + or not hasattr(self.__algorithm,"StoredVariables"): + raise ValueError("No observer can be build before choosing an algorithm.") + if __V not in self.__algorithm: + raise ValueError("An observer requires to be set on a variable named %s which does not exist."%__V) + else: + self.__algorithm.StoredVariables[ __V ].setDataObserver( + Scheduler = __S, + HookFunction = __O, + HookParameters = __I, + ) + + def removeObserver(self, __V, __O, __A = False): + if self.__algorithm is None \ + or isinstance(self.__algorithm, dict) \ + or not hasattr(self.__algorithm,"StoredVariables"): + raise ValueError("No observer can be removed before choosing an algorithm.") + if __V not in self.__algorithm: + raise ValueError("An observer requires to be removed on a variable named %s which does not exist."%__V) + else: + return self.__algorithm.StoredVariables[ __V ].removeDataObserver( + HookFunction = __O, + AllObservers = __A, + ) + + def hasObserver(self, __V): + if self.__algorithm is None \ + or isinstance(self.__algorithm, dict) \ + or not hasattr(self.__algorithm,"StoredVariables"): + return False + if __V not in self.__algorithm: + return False + return self.__algorithm.StoredVariables[ __V ].hasDataObserver() + + def keys(self): + return list(self.__algorithm.keys()) + list(self.__P.keys()) + + def __contains__(self, key=None): + "D.__contains__(k) -> True if D has a key k, else False" + return key in self.__algorithm or key in self.__P + + def __repr__(self): + "x.__repr__() <==> repr(x)" + return repr(self.__A)+", "+repr(self.__P) + + def __str__(self): + "x.__str__() <==> str(x)" + return str(self.__A)+", "+str(self.__P) - def _formula(self, *args): + def __setAlgorithm(self, choice = None ): """ - Doit implémenter l'opération élémentaire de diagnostic sous sa forme - mathématique la plus naturelle possible. + Permet de sélectionner l'algorithme à utiliser pour mener à bien l'étude + d'assimilation. L'argument est un champ caractère se rapportant au nom + d'un algorithme réalisant l'opération sur les arguments fixes. """ - raise NotImplementedError("Diagnostic mathematical formula has not been implemented!") + if choice is None: + raise ValueError("Error: algorithm choice has to be given") + if self.__algorithmName is not None: + raise ValueError("Error: algorithm choice has already been done as \"%s\", it can't be changed."%self.__algorithmName) + daDirectory = "daAlgorithms" + # + # Recherche explicitement le fichier complet + # ------------------------------------------ + module_path = None + for directory in sys.path: + if os.path.isfile(os.path.join(directory, daDirectory, str(choice)+'.py')): + module_path = os.path.abspath(os.path.join(directory, daDirectory)) + if module_path is None: + raise ImportError("No algorithm module named \"%s\" has been found in the search path.\n The search path is %s"%(choice, sys.path)) + # + # Importe le fichier complet comme un module + # ------------------------------------------ + try: + sys_path_tmp = sys.path ; sys.path.insert(0,module_path) + self.__algorithmFile = __import__(str(choice), globals(), locals(), []) + if not hasattr(self.__algorithmFile, "ElementaryAlgorithm"): + raise ImportError("this module does not define a valid elementary algorithm.") + self.__algorithmName = str(choice) + sys.path = sys_path_tmp ; del sys_path_tmp + except ImportError as e: + raise ImportError("The module named \"%s\" was found, but is incorrect at the import stage.\n The import error message is: %s"%(choice,e)) + # + # Instancie un objet du type élémentaire du fichier + # ------------------------------------------------- + self.__algorithm = self.__algorithmFile.ElementaryAlgorithm() + return 0 + + def __shape_validate(self): + """ + Validation de la correspondance correcte des tailles des variables et + des matrices s'il y en a. + """ + if self.__Xb is None: __Xb_shape = (0,) + elif hasattr(self.__Xb,"size"): __Xb_shape = (self.__Xb.size,) + elif hasattr(self.__Xb,"shape"): + if isinstance(self.__Xb.shape, tuple): __Xb_shape = self.__Xb.shape + else: __Xb_shape = self.__Xb.shape() + else: raise TypeError("The background (Xb) has no attribute of shape: problem !") + # + if self.__Y is None: __Y_shape = (0,) + elif hasattr(self.__Y,"size"): __Y_shape = (self.__Y.size,) + elif hasattr(self.__Y,"shape"): + if isinstance(self.__Y.shape, tuple): __Y_shape = self.__Y.shape + else: __Y_shape = self.__Y.shape() + else: raise TypeError("The observation (Y) has no attribute of shape: problem !") + # + if self.__U is None: __U_shape = (0,) + elif hasattr(self.__U,"size"): __U_shape = (self.__U.size,) + elif hasattr(self.__U,"shape"): + if isinstance(self.__U.shape, tuple): __U_shape = self.__U.shape + else: __U_shape = self.__U.shape() + else: raise TypeError("The control (U) has no attribute of shape: problem !") + # + if self.__B is None: __B_shape = (0,0) + elif hasattr(self.__B,"shape"): + if isinstance(self.__B.shape, tuple): __B_shape = self.__B.shape + else: __B_shape = self.__B.shape() + else: raise TypeError("The a priori errors covariance matrix (B) has no attribute of shape: problem !") + # + if self.__R is None: __R_shape = (0,0) + elif hasattr(self.__R,"shape"): + if isinstance(self.__R.shape, tuple): __R_shape = self.__R.shape + else: __R_shape = self.__R.shape() + else: raise TypeError("The observation errors covariance matrix (R) has no attribute of shape: problem !") + # + if self.__Q is None: __Q_shape = (0,0) + elif hasattr(self.__Q,"shape"): + if isinstance(self.__Q.shape, tuple): __Q_shape = self.__Q.shape + else: __Q_shape = self.__Q.shape() + else: raise TypeError("The evolution errors covariance matrix (Q) has no attribute of shape: problem !") + # + if len(self.__HO) == 0: __HO_shape = (0,0) + elif isinstance(self.__HO, dict): __HO_shape = (0,0) + elif hasattr(self.__HO["Direct"],"shape"): + if isinstance(self.__HO["Direct"].shape, tuple): __HO_shape = self.__HO["Direct"].shape + else: __HO_shape = self.__HO["Direct"].shape() + else: raise TypeError("The observation operator (H) has no attribute of shape: problem !") + # + if len(self.__EM) == 0: __EM_shape = (0,0) + elif isinstance(self.__EM, dict): __EM_shape = (0,0) + elif hasattr(self.__EM["Direct"],"shape"): + if isinstance(self.__EM["Direct"].shape, tuple): __EM_shape = self.__EM["Direct"].shape + else: __EM_shape = self.__EM["Direct"].shape() + else: raise TypeError("The evolution model (EM) has no attribute of shape: problem !") + # + if len(self.__CM) == 0: __CM_shape = (0,0) + elif isinstance(self.__CM, dict): __CM_shape = (0,0) + elif hasattr(self.__CM["Direct"],"shape"): + if isinstance(self.__CM["Direct"].shape, tuple): __CM_shape = self.__CM["Direct"].shape + else: __CM_shape = self.__CM["Direct"].shape() + else: raise TypeError("The control model (CM) has no attribute of shape: problem !") + # + # Vérification des conditions + # --------------------------- + if not( len(__Xb_shape) == 1 or min(__Xb_shape) == 1 ): + raise ValueError("Shape characteristic of background (Xb) is incorrect: \"%s\"."%(__Xb_shape,)) + if not( len(__Y_shape) == 1 or min(__Y_shape) == 1 ): + raise ValueError("Shape characteristic of observation (Y) is incorrect: \"%s\"."%(__Y_shape,)) + # + if not( min(__B_shape) == max(__B_shape) ): + raise ValueError("Shape characteristic of a priori errors covariance matrix (B) is incorrect: \"%s\"."%(__B_shape,)) + if not( min(__R_shape) == max(__R_shape) ): + raise ValueError("Shape characteristic of observation errors covariance matrix (R) is incorrect: \"%s\"."%(__R_shape,)) + if not( min(__Q_shape) == max(__Q_shape) ): + raise ValueError("Shape characteristic of evolution errors covariance matrix (Q) is incorrect: \"%s\"."%(__Q_shape,)) + if not( min(__EM_shape) == max(__EM_shape) ): + raise ValueError("Shape characteristic of evolution operator (EM) is incorrect: \"%s\"."%(__EM_shape,)) + # + if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and not( __HO_shape[1] == max(__Xb_shape) ): + raise ValueError("Shape characteristic of observation operator (H) \"%s\" and state (X) \"%s\" are incompatible."%(__HO_shape,__Xb_shape)) + if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and not( __HO_shape[0] == max(__Y_shape) ): + raise ValueError("Shape characteristic of observation operator (H) \"%s\" and observation (Y) \"%s\" are incompatible."%(__HO_shape,__Y_shape)) + if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and len(self.__B) > 0 and not( __HO_shape[1] == __B_shape[0] ): + raise ValueError("Shape characteristic of observation operator (H) \"%s\" and a priori errors covariance matrix (B) \"%s\" are incompatible."%(__HO_shape,__B_shape)) + if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and len(self.__R) > 0 and not( __HO_shape[0] == __R_shape[1] ): + raise ValueError("Shape characteristic of observation operator (H) \"%s\" and observation errors covariance matrix (R) \"%s\" are incompatible."%(__HO_shape,__R_shape)) + # + if self.__B is not None and len(self.__B) > 0 and not( __B_shape[1] == max(__Xb_shape) ): + if self.__algorithmName in ["EnsembleBlue",]: + asPersistentVector = self.__Xb.reshape((-1,min(__B_shape))) + self.__Xb = Persistence.OneVector("Background", basetype=numpy.matrix) + for member in asPersistentVector: + self.__Xb.store( numpy.matrix( numpy.ravel(member), numpy.float ).T ) + __Xb_shape = min(__B_shape) + else: + raise ValueError("Shape characteristic of a priori errors covariance matrix (B) \"%s\" and background (Xb) \"%s\" are incompatible."%(__B_shape,__Xb_shape)) + # + if self.__R is not None and len(self.__R) > 0 and not( __R_shape[1] == max(__Y_shape) ): + raise ValueError("Shape characteristic of observation errors covariance matrix (R) \"%s\" and observation (Y) \"%s\" are incompatible."%(__R_shape,__Y_shape)) + # + if self.__EM is not None and len(self.__EM) > 0 and not isinstance(self.__EM, dict) and not( __EM_shape[1] == max(__Xb_shape) ): + raise ValueError("Shape characteristic of evolution model (EM) \"%s\" and state (X) \"%s\" are incompatible."%(__EM_shape,__Xb_shape)) + # + if self.__CM is not None and len(self.__CM) > 0 and not isinstance(self.__CM, dict) and not( __CM_shape[1] == max(__U_shape) ): + raise ValueError("Shape characteristic of control model (CM) \"%s\" and control (U) \"%s\" are incompatible."%(__CM_shape,__U_shape)) + # + if ("Bounds" in self.__P) \ + and (isinstance(self.__P["Bounds"], list) or isinstance(self.__P["Bounds"], tuple)) \ + and (len(self.__P["Bounds"]) != max(__Xb_shape)): + raise ValueError("The number \"%s\" of bound pairs for the state (X) components is different of the size \"%s\" of the state itself." \ + %(len(self.__P["Bounds"]),max(__Xb_shape))) + # + return 1 - def calculate(self, *args): +# ============================================================================== +class DataObserver(object): + """ + Classe générale d'interface de type observer + """ + def __init__(self, + name = "GenericObserver", + onVariable = None, + asTemplate = None, + asString = None, + asScript = None, + asObsObject = None, + withInfo = None, + scheduledBy = None, + withAlgo = None, + ): """ - Active la formule de calcul avec les arguments correctement rangés """ - raise NotImplementedError("Diagnostic activation method has not been implemented!") + self.__name = str(name) + self.__V = None + self.__O = None + self.__I = None + # + if onVariable is None: + raise ValueError("setting an observer has to be done over a variable name or a list of variable names, not over None.") + elif type(onVariable) in (tuple, list): + self.__V = tuple(map( str, onVariable )) + if withInfo is None: + self.__I = self.__V + else: + self.__I = (str(withInfo),)*len(self.__V) + elif isinstance(onVariable, str): + self.__V = (onVariable,) + if withInfo is None: + self.__I = (onVariable,) + else: + self.__I = (str(withInfo),) + else: + raise ValueError("setting an observer has to be done over a variable name or a list of variable names.") + # + if asString is not None: + __FunctionText = asString + elif (asTemplate is not None) and (asTemplate in Templates.ObserverTemplates): + __FunctionText = Templates.ObserverTemplates[asTemplate] + elif asScript is not None: + __FunctionText = ImportFromScript(asScript).getstring() + else: + __FunctionText = "" + __Function = ObserverF(__FunctionText) + # + if asObsObject is not None: + self.__O = asObsObject + else: + self.__O = __Function.getfunc() + # + for k in range(len(self.__V)): + ename = self.__V[k] + einfo = self.__I[k] + if ename not in withAlgo: + raise ValueError("An observer is asked to be set on a variable named %s which does not exist."%ename) + else: + withAlgo.setObserver(ename, self.__O, einfo, scheduledBy) + + def __repr__(self): + "x.__repr__() <==> repr(x)" + return repr(self.__V)+"\n"+repr(self.__O) + + def __str__(self): + "x.__str__() <==> str(x)" + return str(self.__V)+"\n"+str(self.__O) # ============================================================================== -class Covariance: +class State(object): """ - Classe générale d'interface de type covariance + Classe générale d'interface de type état """ def __init__(self, - name = "GenericCovariance", - asCovariance = None, - asEyeByScalar = None, - asEyeByVector = None, - asCovObject = None, - ): - """ - Permet de définir une covariance : - - asCovariance : entrée des données, comme une matrice compatible avec + name = "GenericVector", + asVector = None, + asPersistentVector = None, + asScript = None, + scheduledBy = None, + toBeChecked = False, + ): + """ + Permet de définir un vecteur : + - asVector : entrée des données, comme un vecteur compatible avec le + constructeur de numpy.matrix, ou "True" si entrée par script. + - asPersistentVector : entrée des données, comme une série de vecteurs + compatible avec le constructeur de numpy.matrix, ou comme un objet de + type Persistence, ou "True" si entrée par script. + - asScript : si un script valide est donné contenant une variable + nommée "name", la variable est de type "asVector" (par défaut) ou + "asPersistentVector" selon que l'une de ces variables est placée à + "True". + """ + self.__name = str(name) + self.__check = bool(toBeChecked) + # + self.__V = None + self.__T = None + self.__is_vector = False + self.__is_series = False + # + if asScript is not None: + __Vector, __Series = None, None + if asPersistentVector: + __Series = ImportFromScript(asScript).getvalue( self.__name ) + else: + __Vector = ImportFromScript(asScript).getvalue( self.__name ) + else: + __Vector, __Series = asVector, asPersistentVector + # + if __Vector is not None: + self.__is_vector = True + self.__V = numpy.matrix( numpy.asmatrix(__Vector).A1, numpy.float ).T + self.shape = self.__V.shape + self.size = self.__V.size + elif __Series is not None: + self.__is_series = True + if isinstance(__Series, (tuple, list, numpy.ndarray, numpy.matrix, str)): + self.__V = Persistence.OneVector(self.__name, basetype=numpy.matrix) + if isinstance(__Series, str): __Series = eval(__Series) + for member in __Series: + self.__V.store( numpy.matrix( numpy.asmatrix(member).A1, numpy.float ).T ) + import sys ; sys.stdout.flush() + else: + self.__V = __Series + if isinstance(self.__V.shape, (tuple, list)): + self.shape = self.__V.shape + else: + self.shape = self.__V.shape() + if len(self.shape) == 1: + self.shape = (self.shape[0],1) + self.size = self.shape[0] * self.shape[1] + else: + raise ValueError("The %s object is improperly defined, it requires at minima either a vector, a list/tuple of vectors or a persistent object. Please check your vector input."%self.__name) + # + if scheduledBy is not None: + self.__T = scheduledBy + + def getO(self, withScheduler=False): + if withScheduler: + return self.__V, self.__T + elif self.__T is None: + return self.__V + else: + return self.__V + + def isvector(self): + "Vérification du type interne" + return self.__is_vector + + def isseries(self): + "Vérification du type interne" + return self.__is_series + + def __repr__(self): + "x.__repr__() <==> repr(x)" + return repr(self.__V) + + def __str__(self): + "x.__str__() <==> str(x)" + return str(self.__V) + +# ============================================================================== +class Covariance(object): + """ + Classe générale d'interface de type covariance + """ + def __init__(self, + name = "GenericCovariance", + asCovariance = None, + asEyeByScalar = None, + asEyeByVector = None, + asCovObject = None, + asScript = None, + toBeChecked = False, + ): + """ + Permet de définir une covariance : + - asCovariance : entrée des données, comme une matrice compatible avec le constructeur de numpy.matrix - - asEyeByScalar : entrée des données comme un seul scalaire de variance, - multiplicatif d'une matrice de corrélation identité, aucune matrice - n'étant donc explicitement à donner - - asEyeByVector : entrée des données comme un seul vecteur de variance, - à mettre sur la diagonale d'une matrice de corrélation, aucune matrice - n'étant donc explicitement à donner - - asCovObject : entrée des données comme un objet python, qui a les + - asEyeByScalar : entrée des données comme un seul scalaire de variance, + multiplicatif d'une matrice de corrélation identité, aucune matrice + n'étant donc explicitement à donner + - asEyeByVector : entrée des données comme un seul vecteur de variance, + à mettre sur la diagonale d'une matrice de corrélation, aucune matrice + n'étant donc explicitement à donner + - asCovObject : entrée des données comme un objet python, qui a les methodes obligatoires "getT", "getI", "diag", "trace", "__add__", "__sub__", "__neg__", "__mul__", "__rmul__" et facultatives "shape", "size", "cholesky", "choleskyI", "asfullmatrix", "__repr__", "__str__" + - toBeChecked : booléen indiquant si le caractère SDP de la matrice + pleine doit être vérifié """ self.__name = str(name) + self.__check = bool(toBeChecked) # self.__C = None self.__is_scalar = False self.__is_vector = False self.__is_matrix = False self.__is_object = False - if asEyeByScalar is not None: + # + if asScript is not None: + __Matrix, __Scalar, __Vector, __Object = None, None, None, None + if asEyeByScalar: + __Scalar = ImportFromScript(asScript).getvalue( self.__name ) + elif asEyeByVector: + __Vector = ImportFromScript(asScript).getvalue( self.__name ) + elif asCovObject: + __Object = ImportFromScript(asScript).getvalue( self.__name ) + else: + __Matrix = ImportFromScript(asScript).getvalue( self.__name ) + else: + __Matrix, __Scalar, __Vector, __Object = asCovariance, asEyeByScalar, asEyeByVector, asCovObject + # + if __Scalar is not None: + if numpy.matrix(__Scalar).size != 1: + raise ValueError(' The diagonal multiplier given to define a sparse matrix is not a unique scalar value.\n Its actual measured size is %i. Please check your scalar input.'%numpy.matrix(__Scalar).size) self.__is_scalar = True - self.__C = numpy.abs( float(asEyeByScalar) ) + self.__C = numpy.abs( float(__Scalar) ) self.shape = (0,0) self.size = 0 - elif asEyeByVector is not None: + elif __Vector is not None: self.__is_vector = True - self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(asEyeByVector, float ) ) ) ) + self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(__Vector, float ) ) ) ) self.shape = (self.__C.size,self.__C.size) self.size = self.__C.size**2 - elif asCovariance is not None: + elif __Matrix is not None: self.__is_matrix = True - self.__C = numpy.matrix( asCovariance, float ) + self.__C = numpy.matrix( __Matrix, float ) self.shape = self.__C.shape self.size = self.__C.size - elif asCovObject is not None: + elif __Object is not None: self.__is_object = True - self.__C = asCovObject + self.__C = __Object for at in ("getT","getI","diag","trace","__add__","__sub__","__neg__","__mul__","__rmul__"): if not hasattr(self.__C,at): raise ValueError("The matrix given for %s as an object has no attribute \"%s\". Please check your object input."%(self.__name,at)) @@ -544,6 +1351,9 @@ class Covariance: self.__validate() def __validate(self): + "Validation" + if self.__C is None: + raise UnboundLocalError("%s covariance matrix value has not been set!"%(self.__name,)) if self.ismatrix() and min(self.shape) != max(self.shape): raise ValueError("The given matrix for %s is not a square one, its shape is %s. Please check your matrix input."%(self.__name,self.shape)) if self.isobject() and min(self.shape) != max(self.shape): @@ -552,25 +1362,35 @@ class Covariance: raise ValueError("The \"%s\" covariance matrix is not positive-definite. Please check your scalar input %s."%(self.__name,self.__C)) if self.isvector() and (self.__C <= 0).any(): raise ValueError("The \"%s\" covariance matrix is not positive-definite. Please check your vector input."%(self.__name,)) - if self.ismatrix() and logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug + if self.ismatrix() and (self.__check or logging.getLogger().level < logging.WARNING): try: L = numpy.linalg.cholesky( self.__C ) except: raise ValueError("The %s covariance matrix is not symmetric positive-definite. Please check your matrix input."%(self.__name,)) + if self.isobject() and (self.__check or logging.getLogger().level < logging.WARNING): + try: + L = self.__C.cholesky() + except: + raise ValueError("The %s covariance object is not symmetric positive-definite. Please check your matrix input."%(self.__name,)) def isscalar(self): + "Vérification du type interne" return self.__is_scalar def isvector(self): + "Vérification du type interne" return self.__is_vector def ismatrix(self): + "Vérification du type interne" return self.__is_matrix def isobject(self): + "Vérification du type interne" return self.__is_object def getI(self): + "Inversion" if self.ismatrix(): return Covariance(self.__name+"I", asCovariance = self.__C.I ) elif self.isvector(): @@ -583,6 +1403,7 @@ class Covariance: return None # Indispensable def getT(self): + "Transposition" if self.ismatrix(): return Covariance(self.__name+"T", asCovariance = self.__C.T ) elif self.isvector(): @@ -593,6 +1414,7 @@ class Covariance: return Covariance(self.__name+"T", asCovObject = self.__C.getT() ) def cholesky(self): + "Décomposition de Cholesky" if self.ismatrix(): return Covariance(self.__name+"C", asCovariance = numpy.linalg.cholesky(self.__C) ) elif self.isvector(): @@ -603,6 +1425,7 @@ class Covariance: return Covariance(self.__name+"C", asCovObject = self.__C.cholesky() ) def choleskyI(self): + "Inversion de la décomposition de Cholesky" if self.ismatrix(): return Covariance(self.__name+"H", asCovariance = numpy.linalg.cholesky(self.__C).I ) elif self.isvector(): @@ -613,6 +1436,7 @@ class Covariance: return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() ) def diag(self, msize=None): + "Diagonale de la matrice" if self.ismatrix(): return numpy.diag(self.__C) elif self.isvector(): @@ -626,6 +1450,7 @@ class Covariance: return self.__C.diag() def asfullmatrix(self, msize=None): + "Matrice pleine" if self.ismatrix(): return self.__C elif self.isvector(): @@ -639,6 +1464,7 @@ class Covariance: return self.__C.asfullmatrix() def trace(self, msize=None): + "Trace de la matrice" if self.ismatrix(): return numpy.trace(self.__C) elif self.isvector(): @@ -651,13 +1477,19 @@ class Covariance: elif self.isobject(): return self.__C.trace() + def getO(self): + return self + def __repr__(self): + "x.__repr__() <==> repr(x)" return repr(self.__C) def __str__(self): + "x.__str__() <==> str(x)" return str(self.__C) def __add__(self, other): + "x.__add__(y) <==> x+y" if self.ismatrix() or self.isobject(): return self.__C + numpy.asmatrix(other) elif self.isvector() or self.isscalar(): @@ -666,9 +1498,11 @@ class Covariance: return numpy.asmatrix(_A) def __radd__(self, other): + "x.__radd__(y) <==> y+x" raise NotImplementedError("%s covariance matrix __radd__ method not available for %s type!"%(self.__name,type(other))) def __sub__(self, other): + "x.__sub__(y) <==> x-y" if self.ismatrix() or self.isobject(): return self.__C - numpy.asmatrix(other) elif self.isvector() or self.isscalar(): @@ -677,27 +1511,25 @@ class Covariance: return numpy.asmatrix(_A) def __rsub__(self, other): + "x.__rsub__(y) <==> y-x" raise NotImplementedError("%s covariance matrix __rsub__ method not available for %s type!"%(self.__name,type(other))) def __neg__(self): + "x.__neg__() <==> -x" return - self.__C def __mul__(self, other): + "x.__mul__(y) <==> x*y" if self.ismatrix() and isinstance(other,numpy.matrix): return self.__C * other - elif self.ismatrix() and (isinstance(other,numpy.ndarray) \ - or isinstance(other,list) \ - or isinstance(other,tuple)): + elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)): if numpy.ravel(other).size == self.shape[1]: # Vecteur return self.__C * numpy.asmatrix(numpy.ravel(other)).T elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice return self.__C * numpy.asmatrix(other) else: raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.asmatrix(other).shape,self.__name)) - elif self.isvector() and (isinstance(other,numpy.matrix) \ - or isinstance(other,numpy.ndarray) \ - or isinstance(other,list) \ - or isinstance(other,tuple)): + elif self.isvector() and isinstance(other, (list, numpy.matrix, numpy.ndarray, tuple)): if numpy.ravel(other).size == self.shape[1]: # Vecteur return numpy.asmatrix(self.__C * numpy.ravel(other)).T elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice @@ -706,9 +1538,7 @@ class Covariance: raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.ravel(other).shape,self.__name)) elif self.isscalar() and isinstance(other,numpy.matrix): return self.__C * other - elif self.isscalar() and (isinstance(other,numpy.ndarray) \ - or isinstance(other,list) \ - or isinstance(other,tuple)): + elif self.isscalar() and isinstance(other, (list, numpy.ndarray, tuple)): if len(numpy.asarray(other).shape) == 1 or numpy.asarray(other).shape[1] == 1 or numpy.asarray(other).shape[0] == 1: return self.__C * numpy.asmatrix(numpy.ravel(other)).T else: @@ -719,6 +1549,7 @@ class Covariance: raise NotImplementedError("%s covariance matrix __mul__ method not available for %s type!"%(self.__name,type(other))) def __rmul__(self, other): + "x.__rmul__(y) <==> y*x" if self.ismatrix() and isinstance(other,numpy.matrix): return other * self.__C elif self.isvector() and isinstance(other,numpy.matrix): @@ -736,8 +1567,190 @@ class Covariance: raise NotImplementedError("%s covariance matrix __rmul__ method not available for %s type!"%(self.__name,type(other))) def __len__(self): + "x.__len__() <==> len(x)" return self.shape[0] +# ============================================================================== +class ObserverF(object): + """ + Creation d'une fonction d'observateur a partir de son texte + """ + def __init__(self, corps=""): + self.__corps = corps + def func(self,var,info): + "Fonction d'observation" + exec(self.__corps) + def getfunc(self): + "Restitution du pointeur de fonction dans l'objet" + return self.func + +# ============================================================================== +class CaseLogger(object): + """ + Conservation des commandes de creation d'un cas + """ + def __init__(self, __name="", __objname="case", __addViewers=None, __addLoaders=None): + self.__name = str(__name) + self.__objname = str(__objname) + self.__logSerie = [] + self.__switchoff = False + self.__viewers = { + "TUI":Interfaces._TUIViewer, + "DCT":Interfaces._DCTViewer, + "SCD":Interfaces._SCDViewer, + "YACS":Interfaces._YACSViewer, + } + self.__loaders = { + "TUI":Interfaces._TUIViewer, + "EPD":Interfaces._EPDViewer, + "DCT":Interfaces._DCTViewer, + } + if __addViewers is not None: + self.__viewers.update(dict(__addViewers)) + if __addLoaders is not None: + self.__loaders.update(dict(__addLoaders)) + + def register(self, __command=None, __keys=None, __local=None, __pre=None, __switchoff=False): + "Enregistrement d'une commande individuelle" + if __command is not None and __keys is not None and __local is not None and not self.__switchoff: + if "self" in __keys: __keys.remove("self") + self.__logSerie.append( (str(__command), __keys, __local, __pre, __switchoff) ) + if __switchoff: + self.__switchoff = True + if not __switchoff: + self.__switchoff = False + + def dump(self, __filename=None, __format="TUI", __upa=""): + "Restitution normalisée des commandes" + if __format in self.__viewers: + __formater = self.__viewers[__format](self.__name, self.__objname, self.__logSerie) + else: + raise ValueError("Dumping as \"%s\" is not available"%__format) + return __formater.dump(__filename, __upa) + + def load(self, __filename=None, __content=None, __object=None, __format="TUI"): + "Chargement normalisé des commandes" + if __format in self.__loaders: + __formater = self.__loaders[__format]() + else: + raise ValueError("Loading as \"%s\" is not available"%__format) + return __formater.load(__filename, __content, __object) + +# ============================================================================== +def CostFunction3D(_x, + _Hm = None, # Pour simuler Hm(x) : HO["Direct"].appliedTo + _HmX = None, # Simulation déjà faite de Hm(x) + _arg = None, # Arguments supplementaires pour Hm, sous la forme d'un tuple + _BI = None, + _RI = None, + _Xb = None, + _Y = None, + _SIV = False, # A résorber pour la 8.0 + _SSC = [], # self._parameters["StoreSupplementaryCalculations"] + _nPS = 0, # nbPreviousSteps + _QM = "DA", # QualityMeasure + _SSV = {}, # Entrée et/ou sortie : self.StoredVariables + _fRt = False, # Restitue ou pas la sortie étendue + _sSc = True, # Stocke ou pas les SSC + ): + """ + Fonction-coût générale utile pour les algorithmes statiques/3D : 3DVAR, BLUE + et dérivés, Kalman et dérivés, LeastSquares, SamplingTest, PSO, SA, Tabu, + DFO, QuantileRegression + """ + if not _sSc: + _SIV = False + _SSC = {} + else: + for k in ["CostFunctionJ", + "CostFunctionJb", + "CostFunctionJo", + "CurrentOptimum", + "CurrentState", + "IndexOfOptimum", + "SimulatedObservationAtCurrentOptimum", + "SimulatedObservationAtCurrentState", + ]: + if k not in _SSV: + _SSV[k] = [] + if hasattr(_SSV[k],"store"): + _SSV[k].append = _SSV[k].store # Pour utiliser "append" au lieu de "store" + # + _X = numpy.asmatrix(numpy.ravel( _x )).T + if _SIV or "CurrentState" in _SSC or "CurrentOptimum" in _SSC: + _SSV["CurrentState"].append( _X ) + # + if _HmX is not None: + _HX = _HmX + else: + if _Hm is None: + raise ValueError("COSTFUNCTION3D Operator has to be defined.") + if _arg is None: + _HX = _Hm( _X ) + else: + _HX = _Hm( _X, *_arg ) + _HX = numpy.asmatrix(numpy.ravel( _HX )).T + # + if "SimulatedObservationAtCurrentState" in _SSC or \ + "SimulatedObservationAtCurrentOptimum" in _SSC: + _SSV["SimulatedObservationAtCurrentState"].append( _HX ) + # + if numpy.any(numpy.isnan(_HX)): + Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan + else: + _Y = numpy.asmatrix(numpy.ravel( _Y )).T + if _QM in ["AugmentedWeightedLeastSquares", "AWLS", "AugmentedPonderatedLeastSquares", "APLS", "DA"]: + if _BI is None or _RI is None: + raise ValueError("Background and Observation error covariance matrix has to be properly defined!") + _Xb = numpy.asmatrix(numpy.ravel( _Xb )).T + Jb = 0.5 * (_X - _Xb).T * _BI * (_X - _Xb) + Jo = 0.5 * (_Y - _HX).T * _RI * (_Y - _HX) + elif _QM in ["WeightedLeastSquares", "WLS", "PonderatedLeastSquares", "PLS"]: + if _RI is None: + raise ValueError("Observation error covariance matrix has to be properly defined!") + Jb = 0. + Jo = 0.5 * (_Y - _HX).T * _RI * (_Y - _HX) + elif _QM in ["LeastSquares", "LS", "L2"]: + Jb = 0. + Jo = 0.5 * (_Y - _HX).T * (_Y - _HX) + elif _QM in ["AbsoluteValue", "L1"]: + Jb = 0. + Jo = numpy.sum( numpy.abs(_Y - _HX) ) + elif _QM in ["MaximumError", "ME"]: + Jb = 0. + Jo = numpy.max( numpy.abs(_Y - _HX) ) + elif _QM in ["QR", "Null"]: + Jb = 0. + Jo = 0. + else: + raise ValueError("Unknown asked quality measure!") + # + J = float( Jb ) + float( Jo ) + # + if _sSc: + _SSV["CostFunctionJb"].append( Jb ) + _SSV["CostFunctionJo"].append( Jo ) + _SSV["CostFunctionJ" ].append( J ) + # + if "IndexOfOptimum" in _SSC or \ + "CurrentOptimum" in _SSC or \ + "SimulatedObservationAtCurrentOptimum" in _SSC: + IndexMin = numpy.argmin( _SSV["CostFunctionJ"][_nPS:] ) + _nPS + if "IndexOfOptimum" in _SSC: + _SSV["IndexOfOptimum"].append( IndexMin ) + if "CurrentOptimum" in _SSC: + _SSV["CurrentOptimum"].append( _SSV["CurrentState"][IndexMin] ) + if "SimulatedObservationAtCurrentOptimum" in _SSC: + _SSV["SimulatedObservationAtCurrentOptimum"].append( _SSV["SimulatedObservationAtCurrentState"][IndexMin] ) + # + if _fRt: + return _SSV + else: + if _QM in ["QR"]: # Pour le QuantileRegression + return _HX + else: + return J + # ============================================================================== if __name__ == "__main__": - print '\n AUTODIAGNOSTIC \n' + print('\n AUTODIAGNOSTIC \n')