X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaCore%2FBasicObjects.py;h=0cc2cb04fd55367082218c5480a1033ae341fe4f;hb=8f27741af02e5f1125f56475f0bb80e2fe709bf9;hp=ca25024a9eaf6bd7aca120b14212e906e87ac764;hpb=9286d9df331338c6bb9709c27ea1ac950d181dd1;p=modules%2Fadao.git diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py index ca25024..0cc2cb0 100644 --- a/src/daComposant/daCore/BasicObjects.py +++ b/src/daComposant/daCore/BasicObjects.py @@ -1,6 +1,6 @@ -#-*-coding:iso-8859-1-*- +# -*- coding: utf-8 -*- # -# Copyright (C) 2008-2016 EDF R&D +# Copyright (C) 2008-2021 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 @@ -21,101 +21,138 @@ # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D """ - Définit les outils généraux élémentaires. - - Ce module est destiné à être appelée par AssimilationStudy. + Définit les outils généraux élémentaires. """ __author__ = "Jean-Philippe ARGAUD" __all__ = [] -import logging, copy +import os +import sys +import logging +import copy +import time import numpy -import Persistence -import PlatformInfo +from functools import partial +from daCore import Persistence, PlatformInfo, Interfaces +from daCore import Templates # ============================================================================== 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, ): """ - Les caractéristiques de tolérance peuvent être modifées à la création. + Les caractéristiques de tolérance peuvent être modifiées à la création. """ - self.__tolerBP = float(toleranceInRedundancy) - self.__lenghtOR = int(lenghtOfRedundancy) - self.__initlnOR = self.__lenghtOR + self.__tolerBP = float(toleranceInRedundancy) + self.__lenghtOR = int(lenghtOfRedundancy) + self.__initlnOR = self.__lenghtOR + self.__seenNames = [] + self.__enabled = True self.clearCache() def clearCache(self): "Vide le cache" - self.__listOPCV = [] # Operator Previous Calculated Points, Results, Point Norms + self.__listOPCV = [] # Previous Calculated Points, Results, Point Norms, Operator + self.__seenNames = [] # 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" + def wasCalculatedIn(self, xValue, oName="" ): #, 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): - 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) - 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) - break + if self.__enabled: + for i in range(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1): + if not hasattr(xValue, 'size') or (str(oName) != self.__listOPCV[i][3]) or (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) + pass + elif 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) + break return __alc, __HxV - def storeValueInX(self, xValue, HxValue ): - "Stocke un calcul correspondant à la valeur" + def storeValueInX(self, xValue, HxValue, oName="" ): + "Stocke pour un opérateur o un calcul Hx correspondant à la valeur x" if self.__lenghtOR < 0: self.__lenghtOR = 2 * xValue.size + 2 self.__initlnOR = self.__lenghtOR + self.__seenNames.append(str(oName)) + if str(oName) not in self.__seenNames: # Etend la liste si nouveau + self.__lenghtOR += 2 * xValue.size + 2 + self.__initlnOR += self.__lenghtOR + self.__seenNames.append(str(oName)) 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)), copy.copy(HxValue), numpy.linalg.norm(xValue), + str(oName), ) ) def disable(self): "Inactive le cache" self.__initlnOR = self.__lenghtOR self.__lenghtOR = 0 + self.__enabled = False def enable(self): "Active le cache" self.__lenghtOR = self.__initlnOR + self.__enabled = True # ============================================================================== class Operator(object): """ - Classe générale d'interface de type opérateur + Classe générale d'interface de type opérateur simple """ NbCallsAsMatrix = 0 NbCallsAsMethod = 0 NbCallsOfCached = 0 CM = CacheManager() # - def __init__(self, fromMethod=None, fromMatrix=None, avoidingRedundancy = True): + def __init__(self, + name = "GenericOperator", + fromMethod = None, + fromMatrix = None, + avoidingRedundancy = True, + inputAsMultiFunction = False, + enableMultiProcess = False, + extraArguments = None, + ): """ - 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 ou un multi-fonction python, soit une + matrice. Arguments : + - name : nom d'opérateur - 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 : booléen évitant (ou pas) les calculs redondants + - inputAsMultiFunction : booléen indiquant une fonction explicitement + définie (ou pas) en multi-fonction + - extraArguments : arguments supplémentaires passés à la fonction de + base et ses dérivées (tuple ou dictionnaire) """ + self.__name = str(name) self.__NbCallsAsMatrix, self.__NbCallsAsMethod, self.__NbCallsOfCached = 0, 0, 0 - self.__AvoidRC = bool( avoidingRedundancy ) - if fromMethod is not None: - self.__Method = fromMethod + self.__AvoidRC = bool( avoidingRedundancy ) + self.__inputAsMF = bool( inputAsMultiFunction ) + self.__mpEnabled = bool( enableMultiProcess ) + self.__extraArgs = extraArguments + if fromMethod is not None and self.__inputAsMF: + self.__Method = fromMethod # logtimer(fromMethod) + self.__Matrix = None + self.__Type = "Method" + elif fromMethod is not None and not self.__inputAsMF: + self.__Method = partial( MultiFonction, _sFunction=fromMethod, _mpEnabled=self.__mpEnabled) self.__Matrix = None self.__Type = "Method" elif fromMatrix is not None: @@ -142,95 +179,188 @@ class Operator(object): "Renvoie le type" return self.__Type - def appliedTo(self, xValue, HValue = None): + def appliedTo(self, xValue, HValue = None, argsAsSerie = False, returnSerieAsArrayMatrix = False): """ - 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 une + série d'arguments xValue. Cette méthode se contente d'appliquer, chaque + argument devant a priori être du bon type. Arguments : - - xValue : argument adapté pour appliquer l'opérateur + - les arguments par série sont : + - xValue : argument adapté pour appliquer l'opérateur + - HValue : valeur précalculée de l'opérateur en ce point + - argsAsSerie : indique si les arguments sont une mono ou multi-valeur """ - if HValue is not None: - HxValue = numpy.asmatrix( numpy.ravel( HValue ) ).T - if self.__AvoidRC: - Operator.CM.storeValueInX(xValue,HxValue) + if argsAsSerie: + _xValue = xValue + _HValue = HValue else: - if self.__AvoidRC: - __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xValue) + _xValue = (xValue,) + if HValue is not None: + _HValue = (HValue,) else: - __alreadyCalculated = False + _HValue = HValue + PlatformInfo.isIterable( _xValue, True, " in Operator.appliedTo" ) + # + if _HValue is not None: + assert len(_xValue) == len(_HValue), "Incompatible number of elements in xValue and HValue" + _HxValue = [] + for i in range(len(_HValue)): + _HxValue.append( numpy.asmatrix( numpy.ravel( _HValue[i] ) ).T ) + if self.__AvoidRC: + Operator.CM.storeValueInX(_xValue[i],_HxValue[-1],self.__name) + else: + _HxValue = [] + _xserie = [] + _hindex = [] + for i, xv in enumerate(_xValue): + if self.__AvoidRC: + __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xv,self.__name) + else: + __alreadyCalculated = False + # + if __alreadyCalculated: + self.__addOneCacheCall() + _hv = __HxV + else: + if self.__Matrix is not None: + self.__addOneMatrixCall() + _xv = numpy.matrix(numpy.ravel(xv)).T + _hv = self.__Matrix * _xv + else: + self.__addOneMethodCall() + _xserie.append( xv ) + _hindex.append( i ) + _hv = None + _HxValue.append( _hv ) # - if __alreadyCalculated: - self.__addOneCacheCall() - HxValue = __HxV - else: - if self.__Matrix is not None: - self.__addOneMatrixCall() - HxValue = self.__Matrix * xValue + if len(_xserie)>0 and self.__Matrix is None: + if self.__extraArgs is None: + _hserie = self.__Method( _xserie ) # Calcul MF else: - self.__addOneMethodCall() - HxValue = self.__Method( xValue ) - if self.__AvoidRC: - Operator.CM.storeValueInX(xValue,HxValue) + _hserie = self.__Method( _xserie, self.__extraArgs ) # Calcul MF + if not hasattr(_hserie, "pop"): + raise TypeError("The user input multi-function doesn't seem to return sequence results, behaving like a mono-function. It has to be checked.") + for i in _hindex: + _xv = _xserie.pop(0) + _hv = _hserie.pop(0) + _HxValue[i] = _hv + if self.__AvoidRC: + Operator.CM.storeValueInX(_xv,_hv,self.__name) + # + if returnSerieAsArrayMatrix: + _HxValue = numpy.stack([numpy.ravel(_hv) for _hv in _HxValue], axis=1) # - return HxValue + if argsAsSerie: return _HxValue + else: return _HxValue[-1] - def appliedControledFormTo(self, (xValue, uValue) ): + def appliedControledFormTo(self, paires, argsAsSerie = False, returnSerieAsArrayMatrix = False): """ - 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 des + paires (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 + - paires : les arguments par paire sont : + - xValue : argument X adapté pour appliquer l'opérateur + - uValue : argument U adapté pour appliquer l'opérateur + - argsAsSerie : indique si l'argument est une mono ou multi-valeur """ + if argsAsSerie: _xuValue = paires + else: _xuValue = (paires,) + PlatformInfo.isIterable( _xuValue, True, " in Operator.appliedControledFormTo" ) + # if self.__Matrix is not None: - self.__addOneMatrixCall() - return self.__Matrix * xValue - elif uValue is not None: - self.__addOneMethodCall() - return self.__Method( (xValue, uValue) ) - else: - self.__addOneMethodCall() - return self.__Method( xValue ) - - def appliedInXTo(self, (xNominal, xValue) ): - """ - 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 + _HxValue = [] + for paire in _xuValue: + _xValue, _uValue = paire + _xValue = numpy.matrix(numpy.ravel(_xValue)).T + self.__addOneMatrixCall() + _HxValue.append( self.__Matrix * _xValue ) + else: + _xuArgs = [] + for paire in _xuValue: + _xValue, _uValue = paire + if _uValue is not None: + _xuArgs.append( paire ) + else: + _xuArgs.append( _xValue ) + self.__addOneMethodCall( len(_xuArgs) ) + if self.__extraArgs is None: + _HxValue = self.__Method( _xuArgs ) # Calcul MF + else: + _HxValue = self.__Method( _xuArgs, self.__extraArgs ) # Calcul MF + # + if returnSerieAsArrayMatrix: + _HxValue = numpy.stack([numpy.ravel(_hv) for _hv in _HxValue], axis=1) + # + if argsAsSerie: return _HxValue + else: return _HxValue[-1] + + def appliedInXTo(self, paires, argsAsSerie = False, returnSerieAsArrayMatrix = False): + """ + Permet de restituer le résultat de l'application de l'opérateur à une + série d'arguments 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 xNominal peut + être quelconque. Il n'y a qu'une seule paire par défaut, et argsAsSerie + permet d'indiquer que l'argument est multi-paires. + Arguments : + - paires : les arguments par paire sont : + - xNominal : série d'arguments permettant de donner le point où + l'opérateur est construit pour être ensuite appliqué + - xValue : série d'arguments adaptés pour appliquer l'opérateur + - argsAsSerie : indique si l'argument est une mono ou multi-valeur """ + if argsAsSerie: _nxValue = paires + else: _nxValue = (paires,) + PlatformInfo.isIterable( _nxValue, True, " in Operator.appliedInXTo" ) + # if self.__Matrix is not None: - self.__addOneMatrixCall() - return self.__Matrix * xValue + _HxValue = [] + for paire in _nxValue: + _xNominal, _xValue = paire + _xValue = numpy.matrix(numpy.ravel(_xValue)).T + self.__addOneMatrixCall() + _HxValue.append( self.__Matrix * _xValue ) else: - self.__addOneMethodCall() - return self.__Method( (xNominal, xValue) ) + self.__addOneMethodCall( len(_nxValue) ) + if self.__extraArgs is None: + _HxValue = self.__Method( _nxValue ) # Calcul MF + else: + _HxValue = self.__Method( _nxValue, self.__extraArgs ) # Calcul MF + # + if returnSerieAsArrayMatrix: + _HxValue = numpy.stack([numpy.ravel(_hv) for _hv in _HxValue], axis=1) + # + if argsAsSerie: return _HxValue + else: return _HxValue[-1] - def asMatrix(self, ValueForMethodForm = "UnknownVoidValue"): + def asMatrix(self, ValueForMethodForm = "UnknownVoidValue", argsAsSerie = False): """ - 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() - return self.__Matrix - elif ValueForMethodForm is not "UnknownVoidValue": # Ne pas utiliser "None" - self.__addOneMethodCall() - return numpy.matrix( self.__Method( (ValueForMethodForm, None) ) ) + mValue = [self.__Matrix,] + elif not isinstance(ValueForMethodForm,str) or ValueForMethodForm != "UnknownVoidValue": # Ne pas utiliser "None" + mValue = [] + if argsAsSerie: + self.__addOneMethodCall( len(ValueForMethodForm) ) + for _vfmf in ValueForMethodForm: + mValue.append( numpy.matrix( self.__Method(((_vfmf, None),)) ) ) + else: + self.__addOneMethodCall() + mValue = self.__Method(((ValueForMethodForm, None),)) else: raise ValueError("Matrix form of the operator defined as a function/method requires to give an operating point.") + # + if argsAsSerie: return mValue + else: return mValue[-1] 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: @@ -240,7 +370,7 @@ class Operator(object): 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, @@ -260,61 +390,246 @@ class Operator(object): self.__NbCallsAsMatrix += 1 # Decompte local Operator.NbCallsAsMatrix += 1 # Decompte global - def __addOneMethodCall(self): + def __addOneMethodCall(self, nb = 1): "Comptabilise un appel" - self.__NbCallsAsMethod += 1 # Decompte local - Operator.NbCallsAsMethod += 1 # Decompte global + self.__NbCallsAsMethod += nb # Decompte local + Operator.NbCallsAsMethod += nb # Decompte global def __addOneCacheCall(self): "Comptabilise un appel" self.__NbCallsOfCached += 1 # Decompte local Operator.NbCallsOfCached += 1 # Decompte global +# ============================================================================== +class FullOperator(object): + """ + Classe générale d'interface de type opérateur complet + (Direct, Linéaire Tangent, Adjoint) + """ + def __init__(self, + name = "GenericFullOperator", + asMatrix = None, + asOneFunction = None, # 1 Fonction + asThreeFunctions = None, # 3 Fonctions in a dictionary + asScript = None, # 1 or 3 Fonction(s) by script + asDict = None, # Parameters + appliedInX = None, + extraArguments = None, + avoidRC = True, + inputAsMF = False,# Fonction(s) as Multi-Functions + scheduledBy = None, + toBeChecked = False, + ): + "" + self.__name = str(name) + self.__check = bool(toBeChecked) + self.__extraArgs = extraArguments + # + self.__FO = {} + # + __Parameters = {} + if (asDict is not None) and isinstance(asDict, dict): + __Parameters.update( asDict ) + # Priorité à EnableMultiProcessingInDerivatives=True + if "EnableMultiProcessing" in __Parameters and __Parameters["EnableMultiProcessing"]: + __Parameters["EnableMultiProcessingInDerivatives"] = True + __Parameters["EnableMultiProcessingInEvaluation"] = False + if "EnableMultiProcessingInDerivatives" not in __Parameters: + __Parameters["EnableMultiProcessingInDerivatives"] = False + if __Parameters["EnableMultiProcessingInDerivatives"]: + __Parameters["EnableMultiProcessingInEvaluation"] = False + if "EnableMultiProcessingInEvaluation" not in __Parameters: + __Parameters["EnableMultiProcessingInEvaluation"] = False + if "withIncrement" in __Parameters: # Temporaire + __Parameters["DifferentialIncrement"] = __Parameters["withIncrement"] + # + if asScript is not None: + __Matrix, __Function = None, None + if asMatrix: + __Matrix = Interfaces.ImportFromScript(asScript).getvalue( self.__name ) + elif asOneFunction: + __Function = { "Direct":Interfaces.ImportFromScript(asScript).getvalue( "DirectOperator" ) } + __Function.update({"useApproximatedDerivatives":True}) + __Function.update(__Parameters) + elif asThreeFunctions: + __Function = { + "Direct" :Interfaces.ImportFromScript(asScript).getvalue( "DirectOperator" ), + "Tangent":Interfaces.ImportFromScript(asScript).getvalue( "TangentOperator" ), + "Adjoint":Interfaces.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 "CenteredFiniteDifference" not in __Function: __Function["CenteredFiniteDifference"] = False + if "DifferentialIncrement" not in __Function: __Function["DifferentialIncrement"] = 0.01 + if "withdX" not in __Function: __Function["withdX"] = None + if "withAvoidingRedundancy" not in __Function: __Function["withAvoidingRedundancy"] = avoidRC + if "withToleranceInRedundancy" not in __Function: __Function["withToleranceInRedundancy"] = 1.e-18 + if "withLenghtOfRedundancy" not in __Function: __Function["withLenghtOfRedundancy"] = -1 + if "NumberOfProcesses" not in __Function: __Function["NumberOfProcesses"] = None + if "withmfEnabled" not in __Function: __Function["withmfEnabled"] = inputAsMF + from daCore import NumericObjects + FDA = NumericObjects.FDApproximation( + name = self.__name, + Function = __Function["Direct"], + centeredDF = __Function["CenteredFiniteDifference"], + increment = __Function["DifferentialIncrement"], + dX = __Function["withdX"], + avoidingRedundancy = __Function["withAvoidingRedundancy"], + toleranceInRedundancy = __Function["withToleranceInRedundancy"], + lenghtOfRedundancy = __Function["withLenghtOfRedundancy"], + mpEnabled = __Function["EnableMultiProcessingInDerivatives"], + mpWorkers = __Function["NumberOfProcesses"], + mfEnabled = __Function["withmfEnabled"], + ) + self.__FO["Direct"] = Operator( name = self.__name, fromMethod = FDA.DirectOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] ) + self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = FDA.TangentOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs ) + self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = FDA.AdjointOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs ) + 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( name = self.__name, fromMethod = __Function["Direct"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] ) + self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = __Function["Tangent"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs ) + self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = __Function["Adjoint"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs ) + elif asMatrix is not None: + __matrice = numpy.matrix( __Matrix, numpy.float ) + self.__FO["Direct"] = Operator( name = self.__name, fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] ) + self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) + self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMatrix = __matrice.T, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) + del __matrice + else: + raise ValueError("The %s object is improperly defined or undefined, it requires at minima either a matrix, a Direct operator for approximate derivatives or a Tangent/Adjoint operators pair. Please check your operator input."%self.__name) + # + if __appliedInX is not None: + self.__FO["AppliedInX"] = {} + 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.__FO) + + def __str__(self): + "x.__str__() <==> str(x)" + return str(self.__FO) + # ============================================================================== 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". - - Les variables prévues sont : - - CostFunctionJ : fonction-cout globale, somme des deux parties suivantes - - 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 - - GradientOfCostFunctionJo : gradient de la partie observations de la fonction-cout - - 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 + 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 : + - APosterioriCorrelations : matrice de corrélations de la matrice A + - APosterioriCovariance : matrice de covariances a posteriori : A + - APosterioriStandardDeviations : vecteur des écart-types de la matrice A + - APosterioriVariances : vecteur des variances de la matrice A + - Analysis : vecteur d'analyse : Xa + - BMA : Background moins Analysis : Xa - Xb + - CostFunctionJ : fonction-coût globale, somme des deux parties suivantes Jb et Jo + - CostFunctionJAtCurrentOptimum : fonction-coût globale à l'état optimal courant lors d'itérations + - CostFunctionJb : partie ébauche ou background de la fonction-coût : Jb + - CostFunctionJbAtCurrentOptimum : partie ébauche à l'état optimal courant lors d'itérations + - CostFunctionJo : partie observations de la fonction-coût : Jo + - CostFunctionJoAtCurrentOptimum : partie observations à l'état optimal courant lors d'itérations + - CurrentIterationNumber : numéro courant d'itération dans les algorithmes itératifs, à partir de 0 + - CurrentOptimum : état optimal courant lors d'itérations + - CurrentState : état courant lors d'itérations + - GradientOfCostFunctionJ : gradient de la fonction-coût globale + - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-coût + - GradientOfCostFunctionJo : gradient de la partie observations de la fonction-coût + - IndexOfOptimum : index de l'état optimal courant lors d'itérations - 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 + - InnovationAtCurrentState : l'innovation à l'état courant : dn = Y - H(Xn) + - JacobianMatrixAtBackground : matrice jacobienne à l'état d'ébauche + - JacobianMatrixAtCurrentState : matrice jacobienne à l'état courant + - JacobianMatrixAtOptimum : matrice jacobienne à l'optimum + - KalmanGainAtOptimum : gain de Kalman à l'optimum - MahalanobisConsistency : indicateur de consistance des covariances - - OMA : Observation moins Analysis : Y - Xa + - OMA : Observation moins Analyse : 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 - - APosterioriCorrelations : correlations de la matrice A - - 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 + - ForecastState : état prédit courant lors d'itérations + - Residu : dans le cas des algorithmes de vérification + - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche + - SigmaObs2 : indicateur de correction optimale des erreurs d'observation + - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche + - SimulatedObservationAtCurrentOptimum : l'état observé H(X) à l'état optimal courant + - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant + - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum + - SimulationQuantiles : états observés H(X) pour les quantiles demandés + 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)) self._m = PlatformInfo.SystemUsage() @@ -322,44 +637,146 @@ class Algorithm(object): self._name = str( name ) self._parameters = {"StoreSupplementaryCalculations":[]} self.__required_parameters = {} - self.StoredVariables = {} + self.__required_inputs = { + "RequiredInputValues":{"mandatory":(), "optional":()}, + "ClassificationTags":[], + } + self.__variable_names_not_public = {"nextStep":False} # Duplication dans AlgorithmAndParameters + self.__canonical_parameter_name = {} # Correspondance "lower"->"correct" + self.__canonical_stored_name = {} # Correspondance "lower"->"correct" # + self.StoredVariables = {} + self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations") + self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance") + self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations") + self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances") + self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis") + self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA") 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["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb") self.StoredVariables["CostFunctionJbAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJbAtCurrentOptimum") + self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo") self.StoredVariables["CostFunctionJoAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJoAtCurrentOptimum") + self.StoredVariables["CurrentIterationNumber"] = Persistence.OneIndex(name = "CurrentIterationNumber") + self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum") + self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState") + self.StoredVariables["ForecastState"] = Persistence.OneVector(name = "ForecastState") 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["IndexOfOptimum"] = Persistence.OneIndex(name = "IndexOfOptimum") self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation") + self.StoredVariables["InnovationAtCurrentAnalysis"] = Persistence.OneVector(name = "InnovationAtCurrentAnalysis") self.StoredVariables["InnovationAtCurrentState"] = Persistence.OneVector(name = "InnovationAtCurrentState") - self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2") - self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2") + self.StoredVariables["JacobianMatrixAtBackground"] = Persistence.OneMatrix(name = "JacobianMatrixAtBackground") + self.StoredVariables["JacobianMatrixAtCurrentState"] = Persistence.OneMatrix(name = "JacobianMatrixAtCurrentState") + self.StoredVariables["JacobianMatrixAtOptimum"] = Persistence.OneMatrix(name = "JacobianMatrixAtOptimum") + self.StoredVariables["KalmanGainAtOptimum"] = Persistence.OneMatrix(name = "KalmanGainAtOptimum") 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") + self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2") + self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2") + self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground") + self.StoredVariables["SimulatedObservationAtCurrentAnalysis"]= Persistence.OneVector(name = "SimulatedObservationAtCurrentAnalysis") + self.StoredVariables["SimulatedObservationAtCurrentOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentOptimum") + self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState") + self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum") + self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles") + # + for k in self.StoredVariables: + self.__canonical_stored_name[k.lower()] = k + # + for k, v in self.__variable_names_not_public.items(): + self.__canonical_parameter_name[k.lower()] = k + self.__canonical_parameter_name["algorithm"] = "Algorithm" + self.__canonical_parameter_name["storesupplementarycalculations"] = "StoreSupplementaryCalculations" - def _pre_run(self): - "Pré-calcul" + def _pre_run(self, Parameters, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None ): + "Pré-calcul" logging.debug("%s Lancement", self._name) - logging.debug("%s Taille mémoire utilisée de %.1f Mio", self._name, self._m.getUsedMemory("Mio")) + logging.debug("%s Taille mémoire utilisée de %.0f Mio"%(self._name, self._m.getUsedMemory("Mio"))) + self._getTimeState(reset=True) + # + # Mise a jour des paramètres internes avec le contenu de Parameters, en + # reprenant les valeurs par défauts pour toutes celles non définies + self.__setParameters(Parameters, reset=True) + for k, v in self.__variable_names_not_public.items(): + if k not in self._parameters: self.__setParameters( {k:v} ) + # + # Corrections et compléments des vecteurs + def __test_vvalue(argument, variable, argname, symbol=None): + if symbol is None: symbol = variable + if argument is None: + if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]: + raise ValueError("%s %s vector %s is not set and has to be properly defined!"%(self._name,argname,symbol)) + elif variable in self.__required_inputs["RequiredInputValues"]["optional"]: + logging.debug("%s %s vector %s is not set, but is optional."%(self._name,argname,symbol)) + else: + logging.debug("%s %s vector %s is not set, but is not required."%(self._name,argname,symbol)) + else: + logging.debug("%s %s vector %s is set, and its size is %i."%(self._name,argname,symbol,numpy.array(argument).size)) + return 0 + __test_vvalue( Xb, "Xb", "Background or initial state" ) + __test_vvalue( Y, "Y", "Observation" ) + __test_vvalue( U, "U", "Control" ) + # + # Corrections et compléments des covariances + def __test_cvalue(argument, variable, argname, symbol=None): + if symbol is None: symbol = variable + if argument is None: + if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]: + raise ValueError("%s %s error covariance matrix %s is not set and has to be properly defined!"%(self._name,argname,symbol)) + 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,symbol)) + else: + logging.debug("%s %s error covariance matrix %s is not set, but is not required."%(self._name,argname,symbol)) + else: + logging.debug("%s %s error covariance matrix %s is set."%(self._name,argname,symbol)) + return 0 + __test_cvalue( B, "B", "Background" ) + __test_cvalue( R, "R", "Observation" ) + __test_cvalue( Q, "Q", "Evolution" ) + # + # Corrections et compléments des opérateurs + def __test_ovalue(argument, variable, argname, symbol=None): + if symbol is None: symbol = variable + if argument is None or (isinstance(argument,dict) and len(argument)==0): + if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]: + raise ValueError("%s %s operator %s is not set and has to be properly defined!"%(self._name,argname,symbol)) + elif variable in self.__required_inputs["RequiredInputValues"]["optional"]: + logging.debug("%s %s operator %s is not set, but is optional."%(self._name,argname,symbol)) + else: + logging.debug("%s %s operator %s is not set, but is not required."%(self._name,argname,symbol)) + else: + logging.debug("%s %s operator %s is set."%(self._name,argname,symbol)) + return 0 + __test_ovalue( HO, "HO", "Observation", "H" ) + __test_ovalue( EM, "EM", "Evolution", "M" ) + __test_ovalue( CM, "CM", "Control Model", "C" ) + # + 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 + # return 0 def _post_run(self,_oH=None): @@ -375,45 +792,71 @@ class Algorithm(object): _EI = numpy.diag(1./numpy.sqrt(numpy.diag(_A))) _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) + if _oH is not None and "Direct" in _oH and "Tangent" in _oH and "Adjoint" in _oH: + 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 Durées d'utilisation CPU de %.1fs et elapsed de %.1fs", self._name, self._getTimeState()[0], self._getTimeState()[1]) + logging.debug("%s Terminé", self._name) return 0 + def _toStore(self, key): + "True if in StoreSupplementaryCalculations, else False" + return key in self._parameters["StoreSupplementaryCalculations"] + 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: - return self.StoredVariables[key] + return self.StoredVariables[self.__canonical_stored_name[key.lower()]] else: return self.StoredVariables def __contains__(self, key=None): "D.__contains__(k) -> True if D has a key k, else False" - return key in self.StoredVariables + if key is None or key.lower() not in self.__canonical_stored_name: + return False + else: + return self.__canonical_stored_name[key.lower()] in self.StoredVariables def keys(self): "D.keys() -> list of D's keys" - return self.StoredVariables.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") and k.lower() in self.__canonical_stored_name: + return self.StoredVariables.pop(self.__canonical_stored_name[k.lower()], 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): + def defineRequiredParameter(self, name = None, default = None, typecast = None, message = None, minval = None, maxval = None, listval = None, listadv = 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.") @@ -424,31 +867,33 @@ class Algorithm(object): "minval" : minval, "maxval" : maxval, "listval" : listval, + "listadv" : listadv, "message" : message, } - logging.debug("%s %s (valeur par défaut = %s)", self._name, message, self.setParameterValue(name)) + self.__canonical_parameter_name[name.lower()] = 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"] - minval = self.__required_parameters[name]["minval"] - maxval = self.__required_parameters[name]["maxval"] - listval = self.__required_parameters[name]["listval"] + __k = self.__canonical_parameter_name[name.lower()] + default = self.__required_parameters[__k]["default"] + typecast = self.__required_parameters[__k]["typecast"] + minval = self.__required_parameters[__k]["minval"] + maxval = self.__required_parameters[__k]["maxval"] + listval = self.__required_parameters[__k]["listval"] + listadv = self.__required_parameters[__k]["listadv"] # if value is None and default is None: __val = None @@ -457,71 +902,788 @@ class Algorithm(object): else: __val = typecast( default ) else: if typecast is None: __val = value - else: __val = typecast( value ) + else: + try: + __val = typecast( value ) + except: + raise ValueError("The value '%s' for the parameter named '%s' can not be correctly evaluated with type '%s'."%(value, __k, typecast)) # 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)) + raise ValueError("The parameter named '%s' of value '%s' can not be less than %s."%(__k, __val, minval)) 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: + raise ValueError("The parameter named '%s' of value '%s' can not be greater than %s."%(__k, __val, maxval)) + if listval is not None or listadv is not None: 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)) - elif __val not in listval: - raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%( __val, name,listval)) + if listval is not None and v in listval: continue + elif listadv is not None and v in listadv: continue + else: + raise ValueError("The value '%s' is not allowed for the parameter named '%s', it has to be in the list %s."%(v, __k, listval)) + elif not (listval is not None and __val in listval) and not (listadv is not None and __val in listadv): + raise ValueError("The value '%s' is not allowed for the parameter named '%s', it has to be in the list %s."%( __val, __k,listval)) + # return __val - def setParameters(self, fromDico={}): + def requireInputArguments(self, mandatory=(), optional=()): """ - Permet de stocker les paramètres reçus dans le dictionnaire interne. + Permet d'imposer des arguments de calcul requis en entrée. + """ + self.__required_inputs["RequiredInputValues"]["mandatory"] = tuple( mandatory ) + self.__required_inputs["RequiredInputValues"]["optional"] = tuple( optional ) + + def getInputArguments(self): + """ + Permet d'obtenir les listes des arguments de calcul requis en entrée. + """ + return self.__required_inputs["RequiredInputValues"]["mandatory"], self.__required_inputs["RequiredInputValues"]["optional"] + + def setAttributes(self, tags=()): + """ + Permet d'adjoindre des attributs comme les tags de classification. + Renvoie la liste actuelle dans tous les cas. + """ + self.__required_inputs["ClassificationTags"].extend( tags ) + return self.__required_inputs["ClassificationTags"] + + def __setParameters(self, fromDico={}, reset=False): + """ + Permet de stocker les paramètres reçus dans le dictionnaire interne. """ self._parameters.update( fromDico ) + __inverse_fromDico_keys = {} + for k in fromDico.keys(): + if k.lower() in self.__canonical_parameter_name: + __inverse_fromDico_keys[self.__canonical_parameter_name[k.lower()]] = k + #~ __inverse_fromDico_keys = dict([(self.__canonical_parameter_name[k.lower()],k) for k in fromDico.keys()]) + __canonic_fromDico_keys = __inverse_fromDico_keys.keys() for k in self.__required_parameters.keys(): - if k in fromDico.keys(): - self._parameters[k] = self.setParameterValue(k,fromDico[k]) - else: + if k in __canonic_fromDico_keys: + self._parameters[k] = self.setParameterValue(k,fromDico[__inverse_fromDico_keys[k]]) + elif reset: self._parameters[k] = self.setParameterValue(k) + else: + pass logging.debug("%s %s : %s", self._name, self.__required_parameters[k]["message"], self._parameters[k]) + def _getTimeState(self, reset=False): + """ + Initialise ou restitue le temps de calcul (cpu/elapsed) à la seconde + """ + if reset: + self.__initial_cpu_time = time.process_time() + self.__initial_elapsed_time = time.perf_counter() + return 0., 0. + else: + self.__cpu_time = time.process_time() - self.__initial_cpu_time + self.__elapsed_time = time.perf_counter() - self.__initial_elapsed_time + return self.__cpu_time, self.__elapsed_time + + def _StopOnTimeLimit(self, X=None, withReason=False): + "Stop criteria on time limit: True/False [+ Reason]" + c, e = self._getTimeState() + if "MaximumCpuTime" in self._parameters and c > self._parameters["MaximumCpuTime"]: + __SC, __SR = True, "Reached maximum CPU time (%.1fs > %.1fs)"%(c, self._parameters["MaximumCpuTime"]) + elif "MaximumElapsedTime" in self._parameters and e > self._parameters["MaximumElapsedTime"]: + __SC, __SR = True, "Reached maximum elapsed time (%.1fs > %.1fs)"%(e, self._parameters["MaximumElapsedTime"]) + else: + __SC, __SR = False, "" + if withReason: + return __SC, __SR + else: + return __SC + # ============================================================================== -class Diagnostic(object): +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 = {}): - "Initialisation" - 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 = Interfaces.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 ) + # + self.__variable_names_not_public = {"nextStep":False} # Duplication dans Algorithm + + def updateParameters(self, + asDict = None, + asScript = None, + ): + "Mise a jour des parametres" + if asDict is None and asScript is not None: + __Dict = Interfaces.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") + else: + __file = os.path.abspath(FileName) + logging.debug("The YACS file name is \"%s\"."%__file) + 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", __file) + r.addCatalog(catalogAd) + except: + pass + + try: + p = xmlLoader.load(__file) + 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: + allvariables = self.__P + for k in self.__variable_names_not_public: allvariables.pop(k, None) + return allvariables + + 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 getAlgorithmInputArguments(self): + "Renvoie la liste des entrées requises selon l'algorithme" + return self.__algorithm.getInputArguments() + + def getAlgorithmAttributes(self): + "Renvoie la liste des attributs selon l'algorithme" + return self.__algorithm.setAttributes() + + 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): + __allvariables = list(self.__algorithm.keys()) + list(self.__P.keys()) + for k in self.__variable_names_not_public: + if k in __allvariables: __allvariables.remove(k) + return __allvariables + + 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 _formula(self, *args): + def __str__(self): + "x.__str__() <==> str(x)" + return str(self.__A)+", "+str(self.__P) + + 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 calculate(self, *args): + def __shape_validate(self): """ - Active la formule de calcul avec les arguments correctement rangés + Validation de la correspondance correcte des tailles des variables et + des matrices s'il y en a. """ - raise NotImplementedError("Diagnostic activation method has not been implemented!") + 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 + +# ============================================================================== +class RegulationAndParameters(object): + """ + Classe générale d'interface d'action pour la régulation et ses paramètres + """ + def __init__(self, + name = "GenericRegulation", + asAlgorithm = None, + asDict = None, + asScript = None, + ): + """ + """ + self.__name = str(name) + self.__P = {} + # + if asAlgorithm is None and asScript is not None: + __Algo = Interfaces.ImportFromScript(asScript).getvalue( "Algorithm" ) + else: + __Algo = asAlgorithm + # + if asDict is None and asScript is not None: + __Dict = Interfaces.ImportFromScript(asScript).getvalue( self.__name, "Parameters" ) + else: + __Dict = asDict + # + if __Dict is not None: + self.__P.update( dict(__Dict) ) + # + if __Algo is not None: + self.__P.update( {"Algorithm":str(__Algo)} ) + + def get(self, key = None): + "Vérifie l'existence d'une clé de variable ou de paramètres" + if key in self.__P: + return self.__P[key] + else: + return self.__P + +# ============================================================================== +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, + ): + """ + """ + 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 asObsObject is not None: + self.__O = asObsObject + else: + __FunctionText = str(UserScript('Observer', asTemplate, asString, asScript)) + __Function = Observer2Func(__FunctionText) + 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 UserScript(object): + """ + Classe générale d'interface de type texte de script utilisateur + """ + def __init__(self, + name = "GenericUserScript", + asTemplate = None, + asString = None, + asScript = None, + ): + """ + """ + self.__name = str(name) + # + if asString is not None: + self.__F = asString + elif self.__name == "UserPostAnalysis" and (asTemplate is not None) and (asTemplate in Templates.UserPostAnalysisTemplates): + self.__F = Templates.UserPostAnalysisTemplates[asTemplate] + elif self.__name == "Observer" and (asTemplate is not None) and (asTemplate in Templates.ObserverTemplates): + self.__F = Templates.ObserverTemplates[asTemplate] + elif asScript is not None: + self.__F = Interfaces.ImportFromScript(asScript).getstring() + else: + self.__F = "" + + def __repr__(self): + "x.__repr__() <==> repr(x)" + return repr(self.__F) + + def __str__(self): + "x.__str__() <==> str(x)" + return str(self.__F) + +# ============================================================================== +class ExternalParameters(object): + """ + Classe générale d'interface de type texte de script utilisateur + """ + def __init__(self, + name = "GenericExternalParameters", + asDict = None, + asScript = None, + ): + """ + """ + self.__name = str(name) + self.__P = {} + # + self.updateParameters( asDict, asScript ) + + def updateParameters(self, + asDict = None, + asScript = None, + ): + "Mise a jour des parametres" + if asDict is None and asScript is not None: + __Dict = Interfaces.ImportFromScript(asScript).getvalue( self.__name, "ExternalParameters" ) + else: + __Dict = asDict + # + if __Dict is not None: + self.__P.update( dict(__Dict) ) + + def get(self, key = None): + if key in self.__P: + return self.__P[key] + else: + return list(self.__P.keys()) + + def keys(self): + return list(self.__P.keys()) + + def pop(self, k, d): + return self.__P.pop(k, d) + + def items(self): + return self.__P.items() + + def __contains__(self, key=None): + "D.__contains__(k) -> True if D has a key k, else False" + return key in self.__P + +# ============================================================================== +class State(object): + """ + Classe générale d'interface de type état + """ + def __init__(self, + name = "GenericVector", + asVector = None, + asPersistentVector = None, + asScript = None, + asDataFile = None, + colNames = None, + colMajor = False, + 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". + - asDataFile : si un ou plusieurs fichiers valides sont donnés + contenant des valeurs en colonnes, elles-mêmes nommées "colNames" + (s'il n'y a pas de nom de colonne indiquée, on cherche une colonne + nommée "name"), on récupère les colonnes et on les range ligne après + ligne (colMajor=False, par défaut) ou colonne après colonne + (colMajor=True). La variable résultante 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 = Interfaces.ImportFromScript(asScript).getvalue( self.__name ) + else: + __Vector = Interfaces.ImportFromScript(asScript).getvalue( self.__name ) + elif asDataFile is not None: + __Vector, __Series = None, None + if asPersistentVector: + if colNames is not None: + __Series = Interfaces.ImportFromFile(asDataFile).getvalue( colNames )[1] + else: + __Series = Interfaces.ImportFromFile(asDataFile).getvalue( [self.__name,] )[1] + if bool(colMajor) and not Interfaces.ImportFromFile(asDataFile).getformat() == "application/numpy.npz": + __Series = numpy.transpose(__Series) + elif not bool(colMajor) and Interfaces.ImportFromFile(asDataFile).getformat() == "application/numpy.npz": + __Series = numpy.transpose(__Series) + else: + if colNames is not None: + __Vector = Interfaces.ImportFromFile(asDataFile).getvalue( colNames )[1] + else: + __Vector = Interfaces.ImportFromFile(asDataFile).getvalue( [self.__name,] )[1] + if bool(colMajor): + __Vector = numpy.ravel(__Vector, order = "F") + else: + __Vector = numpy.ravel(__Vector, order = "C") + 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 ) + 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 or undefined, 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 + Classe générale d'interface de type covariance """ def __init__(self, name = "GenericCovariance", @@ -529,24 +1691,25 @@ class Covariance(object): asEyeByScalar = None, asEyeByVector = None, asCovObject = None, + asScript = None, toBeChecked = False, ): """ - Permet de définir une covariance : - - asCovariance : entrée des données, comme une matrice compatible avec + 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 + - toBeChecked : booléen indiquant si le caractère SDP de la matrice + pleine doit être vérifié """ self.__name = str(name) self.__check = bool(toBeChecked) @@ -556,24 +1719,40 @@ class Covariance(object): 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 = Interfaces.ImportFromScript(asScript).getvalue( self.__name ) + elif asEyeByVector: + __Vector = Interfaces.ImportFromScript(asScript).getvalue( self.__name ) + elif asCovObject: + __Object = Interfaces.ImportFromScript(asScript).getvalue( self.__name ) + else: + __Matrix = Interfaces.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)) @@ -593,6 +1772,8 @@ class Covariance(object): 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): @@ -613,19 +1794,19 @@ class Covariance(object): 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" + "Vérification du type interne" return self.__is_scalar def isvector(self): - "Vérification du type interne" + "Vérification du type interne" return self.__is_vector def ismatrix(self): - "Vérification du type interne" + "Vérification du type interne" return self.__is_matrix def isobject(self): - "Vérification du type interne" + "Vérification du type interne" return self.__is_object def getI(self): @@ -653,7 +1834,7 @@ class Covariance(object): return Covariance(self.__name+"T", asCovObject = self.__C.getT() ) def cholesky(self): - "Décomposition de Cholesky" + "Décomposition de Cholesky" if self.ismatrix(): return Covariance(self.__name+"C", asCovariance = numpy.linalg.cholesky(self.__C) ) elif self.isvector(): @@ -664,7 +1845,7 @@ class Covariance(object): return Covariance(self.__name+"C", asCovObject = self.__C.cholesky() ) def choleskyI(self): - "Inversion de la décomposition de Cholesky" + "Inversion de la décomposition de Cholesky" if self.ismatrix(): return Covariance(self.__name+"H", asCovariance = numpy.linalg.cholesky(self.__C).I ) elif self.isvector(): @@ -674,6 +1855,30 @@ class Covariance(object): elif self.isobject() and hasattr(self.__C,"choleskyI"): return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() ) + def sqrtm(self): + "Racine carrée matricielle" + if self.ismatrix(): + import scipy + return Covariance(self.__name+"C", asCovariance = scipy.linalg.sqrtm(self.__C) ) + elif self.isvector(): + return Covariance(self.__name+"C", asEyeByVector = numpy.sqrt( self.__C ) ) + elif self.isscalar(): + return Covariance(self.__name+"C", asEyeByScalar = numpy.sqrt( self.__C ) ) + elif self.isobject() and hasattr(self.__C,"sqrt"): + return Covariance(self.__name+"C", asCovObject = self.__C.sqrt() ) + + def sqrtmI(self): + "Inversion de la racine carrée matricielle" + if self.ismatrix(): + import scipy + return Covariance(self.__name+"H", asCovariance = scipy.linalg.sqrtm(self.__C).I ) + elif self.isvector(): + return Covariance(self.__name+"H", asEyeByVector = 1.0 / numpy.sqrt( self.__C ) ) + elif self.isscalar(): + return Covariance(self.__name+"H", asEyeByScalar = 1.0 / numpy.sqrt( self.__C ) ) + elif self.isobject() and hasattr(self.__C,"sqrtI"): + return Covariance(self.__name+"H", asCovObject = self.__C.sqrtI() ) + def diag(self, msize=None): "Diagonale de la matrice" if self.ismatrix(): @@ -716,6 +1921,9 @@ class Covariance(object): elif self.isobject(): return self.__C.trace() + def getO(self): + return self + def __repr__(self): "x.__repr__() <==> repr(x)" return repr(self.__C) @@ -756,21 +1964,16 @@ class Covariance(object): def __mul__(self, other): "x.__mul__(y) <==> x*y" - if self.ismatrix() and isinstance(other,numpy.matrix): + if self.ismatrix() and isinstance(other, (int, numpy.matrix, float)): 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 @@ -779,9 +1982,7 @@ class Covariance(object): 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: @@ -793,15 +1994,22 @@ class Covariance(object): def __rmul__(self, other): "x.__rmul__(y) <==> y*x" - if self.ismatrix() and isinstance(other,numpy.matrix): + if self.ismatrix() and isinstance(other, (int, numpy.matrix, float)): return other * self.__C + elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)): + if numpy.ravel(other).size == self.shape[1]: # Vecteur + return numpy.asmatrix(numpy.ravel(other)) * self.__C + elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice + return numpy.asmatrix(other) * self.__C + else: + raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.asmatrix(other).shape,self.shape,self.__name)) elif self.isvector() and isinstance(other,numpy.matrix): if numpy.ravel(other).size == self.shape[0]: # Vecteur return numpy.asmatrix(numpy.ravel(other) * self.__C) elif numpy.asmatrix(other).shape[1] == self.shape[0]: # Matrice return numpy.asmatrix(numpy.array(other) * self.__C) else: - raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.ravel(other).shape,self.__name)) + raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.ravel(other).shape,self.shape,self.__name)) elif self.isscalar() and isinstance(other,numpy.matrix): return other * self.__C elif self.isobject(): @@ -813,26 +2021,156 @@ class Covariance(object): "x.__len__() <==> len(x)" return self.shape[0] +# ============================================================================== +class Observer2Func(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, + "SCD" :Interfaces._SCDViewer, + "YACS":Interfaces._YACSViewer, + } + self.__loaders = { + "TUI" :Interfaces._TUIViewer, + "COM" :Interfaces._COMViewer, + } + 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 MultiFonction( + __xserie, + _extraArguments = None, + _sFunction = lambda x: x, + _mpEnabled = False, + _mpWorkers = None, + ): + """ + Pour une liste ordonnée de vecteurs en entrée, renvoie en sortie la liste + correspondante de valeurs de la fonction en argument + """ + # Vérifications et définitions initiales + # logging.debug("MULTF Internal multifonction calculations begin with function %s"%(_sFunction.__name__,)) + if not PlatformInfo.isIterable( __xserie ): + raise TypeError("MultiFonction not iterable unkown input type: %s"%(type(__xserie),)) + if _mpEnabled: + if (_mpWorkers is None) or (_mpWorkers is not None and _mpWorkers < 1): + __mpWorkers = None + else: + __mpWorkers = int(_mpWorkers) + try: + import multiprocessing + __mpEnabled = True + except ImportError: + __mpEnabled = False + else: + __mpEnabled = False + __mpWorkers = None + # + # Calculs effectifs + if __mpEnabled: + _jobs = [] + if _extraArguments is None: + _jobs = __xserie + elif _extraArguments is not None and isinstance(_extraArguments, (list, tuple, map)): + for __xvalue in __xserie: + _jobs.append( [__xvalue, ] + list(_extraArguments) ) + else: + raise TypeError("MultiFonction extra arguments unkown input type: %s"%(type(_extraArguments),)) + # logging.debug("MULTF Internal multiprocessing calculations begin : evaluation of %i point(s)"%(len(_jobs),)) + import multiprocessing + with multiprocessing.Pool(__mpWorkers) as pool: + __multiHX = pool.map( _sFunction, _jobs ) + pool.close() + pool.join() + # logging.debug("MULTF Internal multiprocessing calculation end") + else: + # logging.debug("MULTF Internal monoprocessing calculation begin") + __multiHX = [] + if _extraArguments is None: + for __xvalue in __xserie: + __multiHX.append( _sFunction( __xvalue ) ) + elif _extraArguments is not None and isinstance(_extraArguments, (list, tuple, map)): + for __xvalue in __xserie: + __multiHX.append( _sFunction( __xvalue, *_extraArguments ) ) + elif _extraArguments is not None and isinstance(_extraArguments, dict): + for __xvalue in __xserie: + __multiHX.append( _sFunction( __xvalue, **_extraArguments ) ) + else: + raise TypeError("MultiFonction extra arguments unkown input type: %s"%(type(_extraArguments),)) + # logging.debug("MULTF Internal monoprocessing calculation end") + # + # logging.debug("MULTF Internal multifonction calculations end") + return __multiHX + # ============================================================================== def CostFunction3D(_x, _Hm = None, # Pour simuler Hm(x) : HO["Direct"].appliedTo - _HmX = None, # Simulation déjà faite de Hm(x) + _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 + _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 + _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, + 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: @@ -930,4 +2268,4 @@ def CostFunction3D(_x, # ============================================================================== if __name__ == "__main__": - print '\n AUTODIAGNOSTIC \n' + print('\n AUTODIAGNOSTIC\n')