-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2015 EDF R&D
+# Copyright (C) 2008-2018 EDF R&D
#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# Lesser General Public License for more details.
#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
#
-# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
"""
- Définit les outils généraux élémentaires.
+ Définit les outils généraux élémentaires.
- Ce module est destiné à etre appelée par AssimilationStudy pour constituer
- les objets élémentaires de l'algorithme.
+ Ce module est destiné à être appelée par AssimilationStudy.
"""
__author__ = "Jean-Philippe ARGAUD"
__all__ = []
-import logging, copy
+import os
+import sys
+import logging
+import copy
import numpy
-import Persistence
-import PlatformInfo
+from daCore import Persistence
+from daCore import PlatformInfo
+from daCore import Interfaces
+from daCore import Templates
+from daCore.Interfaces import ImportFromScript
# ==============================================================================
-class CacheManager:
+class CacheManager(object):
"""
- Classe générale de gestion d'un cache de calculs
+ Classe générale de gestion d'un cache de calculs
"""
def __init__(self,
- toleranceInRedundancy = 1.e-18,
- lenghtOfRedundancy = -1,
- ):
+ toleranceInRedundancy = 1.e-18,
+ lenghtOfRedundancy = -1,
+ ):
"""
- Les caractéristiques de tolérance peuvent être modifées à la création.
+ Les caractéristiques de tolérance peuvent être modifées à la création.
"""
self.__tolerBP = float(toleranceInRedundancy)
self.__lenghtOR = int(lenghtOfRedundancy)
+ self.__initlnOR = self.__lenghtOR
self.clearCache()
def clearCache(self):
+ "Vide le cache"
self.__listOPCV = [] # Operator Previous Calculated Points, Results, Point Norms
- # logging.debug("CM Tolerance de determination des doublons : %.2e"%self.__tolerBP)
+ # logging.debug("CM Tolerance de determination des doublons : %.2e", self.__tolerBP)
def wasCalculatedIn(self, xValue ): #, info="" ):
+ "Vérifie l'existence d'un calcul correspondant à la valeur"
__alc = False
__HxV = None
- for i in xrange(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1):
+ for i in range(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1):
if xValue.size != self.__listOPCV[i][0].size:
- # logging.debug("CM Différence de la taille %s de X et de celle %s du point %i déjà calculé"%(xValue.shape,i,self.__listOPCP[i].shape))
+ # logging.debug("CM Différence de la taille %s de X et de celle %s du point %i déjà calculé", xValue.shape,i,self.__listOPCP[i].shape)
continue
if numpy.linalg.norm(numpy.ravel(xValue) - self.__listOPCV[i][0]) < self.__tolerBP * self.__listOPCV[i][2]:
__alc = True
__HxV = self.__listOPCV[i][1]
- # logging.debug("CM Cas%s déja calculé, portant le numéro %i"%(info,i))
+ # logging.debug("CM Cas%s déja calculé, portant le numéro %i", info, i)
break
return __alc, __HxV
def storeValueInX(self, xValue, HxValue ):
- if self.__lenghtOR < 0: self.__lenghtOR = 2 * xValue.size + 2
+ "Stocke un calcul correspondant à la valeur"
+ if self.__lenghtOR < 0:
+ self.__lenghtOR = 2 * xValue.size + 2
+ self.__initlnOR = self.__lenghtOR
while len(self.__listOPCV) > self.__lenghtOR:
- # logging.debug("CM Réduction de la liste des cas à %i éléments par suppression du premier"%self.__lenghtOR)
+ # logging.debug("CM Réduction de la liste des cas à %i éléments par suppression du premier", self.__lenghtOR)
self.__listOPCV.pop(0)
self.__listOPCV.append( (
copy.copy(numpy.ravel(xValue)),
numpy.linalg.norm(xValue),
) )
+ def disable(self):
+ "Inactive le cache"
+ self.__initlnOR = self.__lenghtOR
+ self.__lenghtOR = 0
+
+ def enable(self):
+ "Active le cache"
+ self.__lenghtOR = self.__initlnOR
+
# ==============================================================================
-class Operator:
+class Operator(object):
"""
- Classe générale d'interface de type opérateur
+ Classe générale d'interface de type opérateur simple
"""
NbCallsAsMatrix = 0
NbCallsAsMethod = 0
#
def __init__(self, fromMethod=None, fromMatrix=None, avoidingRedundancy = True):
"""
- On construit un objet de ce type en fournissant à l'aide de l'un des
- deux mots-clé, soit une fonction python, soit une matrice.
+ On construit un objet de ce type en fournissant à l'aide de l'un des
+ deux mots-clé, soit une fonction python, soit une matrice.
Arguments :
- fromMethod : argument de type fonction Python
- - fromMatrix : argument adapté au constructeur numpy.matrix
- - avoidingRedundancy : évite ou pas les calculs redondants
+ - fromMatrix : argument adapté au constructeur numpy.matrix
+ - avoidingRedundancy : évite ou pas les calculs redondants
"""
self.__NbCallsAsMatrix, self.__NbCallsAsMethod, self.__NbCallsOfCached = 0, 0, 0
self.__AvoidRC = bool( avoidingRedundancy )
if fromMethod is not None:
- self.__Method = fromMethod
+ self.__Method = fromMethod # logtimer(fromMethod)
self.__Matrix = None
self.__Type = "Method"
elif fromMatrix is not None:
self.__Matrix = None
self.__Type = None
+ def disableAvoidingRedundancy(self):
+ "Inactive le cache"
+ Operator.CM.disable()
+
+ def enableAvoidingRedundancy(self):
+ "Active le cache"
+ if self.__AvoidRC:
+ Operator.CM.enable()
+ else:
+ Operator.CM.disable()
+
def isType(self):
+ "Renvoie le type"
return self.__Type
- def appliedTo(self, xValue):
+ def appliedTo(self, xValue, HValue = None):
"""
- Permet de restituer le résultat de l'application de l'opérateur à un
- argument xValue. Cette méthode se contente d'appliquer, son argument
- devant a priori être du bon type.
+ Permet de restituer le résultat de l'application de l'opérateur à un
+ argument xValue. Cette méthode se contente d'appliquer, son argument
+ devant a priori être du bon type.
Arguments :
- - xValue : argument adapté pour appliquer l'opérateur
+ - xValue : argument adapté pour appliquer l'opérateur
"""
- if self.__AvoidRC:
- __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xValue)
- else:
- __alreadyCalculated = False
- #
- if __alreadyCalculated:
- self.__addOneCacheCall()
- HxValue = __HxV
- else:
- if self.__Matrix is not None:
- self.__addOneMatrixCall()
- HxValue = self.__Matrix * xValue
- else:
- self.__addOneMethodCall()
- HxValue = self.__Method( xValue )
+ if HValue is not None:
+ HxValue = numpy.asmatrix( numpy.ravel( HValue ) ).T
if self.__AvoidRC:
Operator.CM.storeValueInX(xValue,HxValue)
+ else:
+ if self.__AvoidRC:
+ __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xValue)
+ else:
+ __alreadyCalculated = False
+ #
+ if __alreadyCalculated:
+ self.__addOneCacheCall()
+ HxValue = __HxV
+ else:
+ if self.__Matrix is not None:
+ self.__addOneMatrixCall()
+ HxValue = self.__Matrix * xValue
+ else:
+ self.__addOneMethodCall()
+ HxValue = self.__Method( xValue )
+ if self.__AvoidRC:
+ Operator.CM.storeValueInX(xValue,HxValue)
#
return HxValue
- def appliedControledFormTo(self, (xValue, uValue) ):
+ def appliedControledFormTo(self, paire ):
"""
- Permet de restituer le résultat de l'application de l'opérateur à une
- paire (xValue, uValue). Cette méthode se contente d'appliquer, son
- argument devant a priori être du bon type. Si la uValue est None,
- on suppose que l'opérateur ne s'applique qu'à xValue.
+ Permet de restituer le résultat de l'application de l'opérateur à une
+ paire (xValue, uValue). Cette méthode se contente d'appliquer, son
+ argument devant a priori être du bon type. Si la uValue est None,
+ on suppose que l'opérateur ne s'applique qu'à xValue.
Arguments :
- - xValue : argument X adapté pour appliquer l'opérateur
- - uValue : argument U adapté pour appliquer l'opérateur
+ - xValue : argument X adapté pour appliquer l'opérateur
+ - uValue : argument U adapté pour appliquer l'opérateur
"""
+ assert len(paire) == 2, "Incorrect number of arguments"
+ xValue, uValue = paire
if self.__Matrix is not None:
self.__addOneMatrixCall()
return self.__Matrix * xValue
self.__addOneMethodCall()
return self.__Method( xValue )
- def appliedInXTo(self, (xNominal, xValue) ):
+ def appliedInXTo(self, paire ):
"""
- Permet de restituer le résultat de l'application de l'opérateur à un
- argument xValue, sachant que l'opérateur est valable en xNominal.
- Cette méthode se contente d'appliquer, son argument devant a priori
- être du bon type. Si l'opérateur est linéaire car c'est une matrice,
- alors il est valable en tout point nominal et il n'est pas nécessaire
+ Permet de restituer le résultat de l'application de l'opérateur à un
+ argument xValue, sachant que l'opérateur est valable en xNominal.
+ Cette méthode se contente d'appliquer, son argument devant a priori
+ être du bon type. Si l'opérateur est linéaire car c'est une matrice,
+ alors il est valable en tout point nominal et il n'est pas nécessaire
d'utiliser xNominal.
Arguments : une liste contenant
- - xNominal : argument permettant de donner le point où l'opérateur
- est construit pour etre ensuite appliqué
- - xValue : argument adapté pour appliquer l'opérateur
+ - xNominal : argument permettant de donner le point où l'opérateur
+ est construit pour etre ensuite appliqué
+ - xValue : argument adapté pour appliquer l'opérateur
"""
+ assert len(paire) == 2, "Incorrect number of arguments"
+ xNominal, xValue = paire
if self.__Matrix is not None:
self.__addOneMatrixCall()
return self.__Matrix * xValue
def asMatrix(self, ValueForMethodForm = "UnknownVoidValue"):
"""
- Permet de renvoyer l'opérateur sous la forme d'une matrice
+ Permet de renvoyer l'opérateur sous la forme d'une matrice
"""
if self.__Matrix is not None:
self.__addOneMatrixCall()
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:
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,
else: return __nbcalls[which]
def __addOneMatrixCall(self):
+ "Comptabilise un appel"
self.__NbCallsAsMatrix += 1 # Decompte local
Operator.NbCallsAsMatrix += 1 # Decompte global
def __addOneMethodCall(self):
+ "Comptabilise un appel"
self.__NbCallsAsMethod += 1 # Decompte local
Operator.NbCallsAsMethod += 1 # Decompte global
def __addOneCacheCall(self):
+ "Comptabilise un appel"
self.__NbCallsOfCached += 1 # Decompte local
Operator.NbCallsOfCached += 1 # Decompte global
# ==============================================================================
-class Algorithm:
+class FullOperator(object):
+ """
+ Classe générale d'interface de type opérateur complet
+ (Direct, Linéaire Tangent, Adjoint)
+ """
+ def __init__(self,
+ name = "GenericFullOperator",
+ asMatrix = None,
+ asOneFunction = None, # Fonction
+ asThreeFunctions = None, # Fonctions dictionary
+ asScript = None, # Fonction(s) script
+ asDict = None, # Parameters
+ appliedInX = None,
+ avoidRC = True,
+ scheduledBy = None,
+ toBeChecked = False,
+ ):
+ ""
+ self.__name = str(name)
+ self.__check = bool(toBeChecked)
+ #
+ self.__FO = {}
+ #
+ __Parameters = {}
+ if (asDict is not None) and isinstance(asDict, dict):
+ __Parameters.update( asDict )
+ if "DifferentialIncrement" in asDict:
+ __Parameters["withIncrement"] = asDict["DifferentialIncrement"]
+ if "CenteredFiniteDifference" in asDict:
+ __Parameters["withCenteredDF"] = asDict["CenteredFiniteDifference"]
+ if "EnableMultiProcessing" in asDict:
+ __Parameters["withmpEnabled"] = asDict["EnableMultiProcessing"]
+ if "NumberOfProcesses" in asDict:
+ __Parameters["withmpWorkers"] = asDict["NumberOfProcesses"]
+ #
+ if asScript is not None:
+ __Matrix, __Function = None, None
+ if asMatrix:
+ __Matrix = ImportFromScript(asScript).getvalue( self.__name )
+ elif asOneFunction:
+ __Function = { "Direct":ImportFromScript(asScript).getvalue( "DirectOperator" ) }
+ __Function.update({"useApproximatedDerivatives":True})
+ __Function.update(__Parameters)
+ elif asThreeFunctions:
+ __Function = {
+ "Direct" :ImportFromScript(asScript).getvalue( "DirectOperator" ),
+ "Tangent":ImportFromScript(asScript).getvalue( "TangentOperator" ),
+ "Adjoint":ImportFromScript(asScript).getvalue( "AdjointOperator" ),
+ }
+ __Function.update(__Parameters)
+ else:
+ __Matrix = asMatrix
+ if asOneFunction is not None:
+ if isinstance(asOneFunction, dict) and "Direct" in asOneFunction:
+ if asOneFunction["Direct"] is not None:
+ __Function = asOneFunction
+ else:
+ raise ValueError("The function has to be given in a dictionnary which have 1 key (\"Direct\")")
+ else:
+ __Function = { "Direct":asOneFunction }
+ __Function.update({"useApproximatedDerivatives":True})
+ __Function.update(__Parameters)
+ elif asThreeFunctions is not None:
+ if isinstance(asThreeFunctions, dict) and \
+ ("Tangent" in asThreeFunctions) and (asThreeFunctions["Tangent"] is not None) and \
+ ("Adjoint" in asThreeFunctions) and (asThreeFunctions["Adjoint"] is not None) and \
+ (("useApproximatedDerivatives" not in asThreeFunctions) or not bool(asThreeFunctions["useApproximatedDerivatives"])):
+ __Function = asThreeFunctions
+ elif isinstance(asThreeFunctions, dict) and \
+ ("Direct" in asThreeFunctions) and (asThreeFunctions["Direct"] is not None):
+ __Function = asThreeFunctions
+ __Function.update({"useApproximatedDerivatives":True})
+ else:
+ raise ValueError("The functions has to be given in a dictionnary which have either 1 key (\"Direct\") or 3 keys (\"Direct\" (optionnal), \"Tangent\" and \"Adjoint\")")
+ if "Direct" not in asThreeFunctions:
+ __Function["Direct"] = asThreeFunctions["Tangent"]
+ __Function.update(__Parameters)
+ else:
+ __Function = None
+ #
+ # if sys.version_info[0] < 3 and isinstance(__Function, dict):
+ # for k in ("Direct", "Tangent", "Adjoint"):
+ # if k in __Function and hasattr(__Function[k],"__class__"):
+ # if type(__Function[k]) is type(self.__init__):
+ # raise TypeError("can't use a class method (%s) as a function for the \"%s\" operator. Use a real function instead."%(type(__Function[k]),k))
+ #
+ if appliedInX is not None and isinstance(appliedInX, dict):
+ __appliedInX = appliedInX
+ elif appliedInX is not None:
+ __appliedInX = {"HXb":appliedInX}
+ else:
+ __appliedInX = None
+ #
+ if scheduledBy is not None:
+ self.__T = scheduledBy
+ #
+ if isinstance(__Function, dict) and \
+ ("useApproximatedDerivatives" in __Function) and bool(__Function["useApproximatedDerivatives"]) and \
+ ("Direct" in __Function) and (__Function["Direct"] is not None):
+ if "withCenteredDF" not in __Function: __Function["withCenteredDF"] = False
+ if "withIncrement" not in __Function: __Function["withIncrement"] = 0.01
+ if "withdX" not in __Function: __Function["withdX"] = None
+ if "withAvoidingRedundancy" not in __Function: __Function["withAvoidingRedundancy"] = True
+ if "withToleranceInRedundancy" not in __Function: __Function["withToleranceInRedundancy"] = 1.e-18
+ if "withLenghtOfRedundancy" not in __Function: __Function["withLenghtOfRedundancy"] = -1
+ if "withmpEnabled" not in __Function: __Function["withmpEnabled"] = False
+ if "withmpWorkers" not in __Function: __Function["withmpWorkers"] = None
+ from daNumerics.ApproximatedDerivatives import FDApproximation
+ FDA = FDApproximation(
+ Function = __Function["Direct"],
+ centeredDF = __Function["withCenteredDF"],
+ increment = __Function["withIncrement"],
+ dX = __Function["withdX"],
+ avoidingRedundancy = __Function["withAvoidingRedundancy"],
+ toleranceInRedundancy = __Function["withToleranceInRedundancy"],
+ lenghtOfRedundancy = __Function["withLenghtOfRedundancy"],
+ mpEnabled = __Function["withmpEnabled"],
+ mpWorkers = __Function["withmpWorkers"],
+ )
+ self.__FO["Direct"] = Operator( fromMethod = FDA.DirectOperator, avoidingRedundancy = avoidRC )
+ self.__FO["Tangent"] = Operator( fromMethod = FDA.TangentOperator, avoidingRedundancy = avoidRC )
+ self.__FO["Adjoint"] = Operator( fromMethod = FDA.AdjointOperator, avoidingRedundancy = avoidRC )
+ elif isinstance(__Function, dict) and \
+ ("Direct" in __Function) and ("Tangent" in __Function) and ("Adjoint" in __Function) and \
+ (__Function["Direct"] is not None) and (__Function["Tangent"] is not None) and (__Function["Adjoint"] is not None):
+ self.__FO["Direct"] = Operator( fromMethod = __Function["Direct"], avoidingRedundancy = avoidRC )
+ self.__FO["Tangent"] = Operator( fromMethod = __Function["Tangent"], avoidingRedundancy = avoidRC )
+ self.__FO["Adjoint"] = Operator( fromMethod = __Function["Adjoint"], avoidingRedundancy = avoidRC )
+ elif asMatrix is not None:
+ __matrice = numpy.matrix( __Matrix, numpy.float )
+ self.__FO["Direct"] = Operator( fromMatrix = __matrice, avoidingRedundancy = avoidRC )
+ self.__FO["Tangent"] = Operator( fromMatrix = __matrice, avoidingRedundancy = avoidRC )
+ self.__FO["Adjoint"] = Operator( fromMatrix = __matrice.T, avoidingRedundancy = avoidRC )
+ del __matrice
+ else:
+ raise ValueError("Improperly defined observation operator, it requires at minima either a matrix, a Direct for approximate derivatives or a Tangent/Adjoint pair.")
+ #
+ if __appliedInX is not None:
+ self.__FO["AppliedInX"] = {}
+ if not isinstance(__appliedInX, dict):
+ raise ValueError("Error: observation operator defined by \"AppliedInX\" need a dictionary as argument.")
+ for key in list(__appliedInX.keys()):
+ if type( __appliedInX[key] ) is type( numpy.matrix([]) ):
+ # Pour le cas où l'on a une vraie matrice
+ self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key].A1, numpy.float ).T
+ elif type( __appliedInX[key] ) is type( numpy.array([]) ) and len(__appliedInX[key].shape) > 1:
+ # Pour le cas où l'on a un vecteur représenté en array avec 2 dimensions
+ self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key].reshape(len(__appliedInX[key]),), numpy.float ).T
+ else:
+ self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key], numpy.float ).T
+ else:
+ self.__FO["AppliedInX"] = None
+
+ def getO(self):
+ return self.__FO
+
+ def __repr__(self):
+ "x.__repr__() <==> repr(x)"
+ return repr(self.__V)
+
+ def __str__(self):
+ "x.__str__() <==> str(x)"
+ return str(self.__V)
+
+# ==============================================================================
+class Algorithm(object):
"""
- Classe générale d'interface de type algorithme
+ Classe générale d'interface de type algorithme
- Elle donne un cadre pour l'écriture d'une classe élémentaire d'algorithme
+ Elle donne un cadre pour l'écriture d'une classe élémentaire d'algorithme
d'assimilation, en fournissant un container (dictionnaire) de variables
- persistantes initialisées, et des méthodes d'accès à ces variables stockées.
+ persistantes initialisées, et des méthodes d'accès à ces variables stockées.
- Une classe élémentaire d'algorithme doit implémenter la méthode "run".
+ Une classe élémentaire d'algorithme doit implémenter la méthode "run".
"""
def __init__(self, name):
"""
- L'initialisation présente permet de fabriquer des variables de stockage
- disponibles de manière générique dans les algorithmes élémentaires. Ces
- variables de stockage sont ensuite conservées dans un dictionnaire
- interne à l'objet, mais auquel on accède par la méthode "get".
+ L'initialisation présente permet de fabriquer des variables de stockage
+ disponibles de manière générique dans les algorithmes élémentaires. Ces
+ variables de stockage sont ensuite conservées dans un dictionnaire
+ interne à l'objet, mais auquel on accède par la méthode "get".
- Les variables prévues sont :
+ Les variables prévues sont :
- CostFunctionJ : fonction-cout globale, somme des deux parties suivantes
- - CostFunctionJb : partie ébauche ou background de la fonction-cout
+ - CostFunctionJb : partie ébauche ou background de la fonction-cout
- CostFunctionJo : partie observations de la fonction-cout
- GradientOfCostFunctionJ : gradient de la fonction-cout globale
- - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-cout
+ - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-cout
- GradientOfCostFunctionJo : gradient de la partie observations de la fonction-cout
- - CurrentState : état courant lors d'itérations
+ - CurrentState : état courant lors d'itérations
- Analysis : l'analyse Xa
- - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche
- - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant
- - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum
+ - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche
+ - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant
+ - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum
- Innovation : l'innovation : d = Y - H(X)
+ - InnovationAtCurrentState : l'innovation à l'état courant : dn = Y - H(Xn)
- SigmaObs2 : indicateur de correction optimale des erreurs d'observation
- - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche
+ - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche
- MahalanobisConsistency : indicateur de consistance des covariances
- OMA : Observation moins Analysis : Y - Xa
- OMB : Observation moins Background : Y - Xb
- AMB : Analysis moins Background : Xa - Xb
- APosterioriCovariance : matrice A
- APosterioriVariances : variances de la matrice A
- - APosterioriStandardDeviations : écart-types de la matrice A
+ - APosterioriStandardDeviations : écart-types de la matrice A
- APosterioriCorrelations : correlations de la matrice A
- On peut rajouter des variables à stocker dans l'initialisation de
- l'algorithme élémentaire qui va hériter de cette classe
+ - Residu : dans le cas des algorithmes de vérification
+ On peut rajouter des variables à stocker dans l'initialisation de
+ l'algorithme élémentaire qui va hériter de cette classe
"""
- logging.debug("%s Initialisation"%str(name))
+ logging.debug("%s Initialisation", str(name))
self._m = PlatformInfo.SystemUsage()
#
self._name = str( name )
self._parameters = {"StoreSupplementaryCalculations":[]}
self.__required_parameters = {}
+ self.__required_inputs = {"RequiredInputValues":{"mandatory":(), "optional":()}}
+ #
self.StoredVariables = {}
+ self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ")
+ self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
+ self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
+ self.StoredVariables["CostFunctionJAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJAtCurrentOptimum")
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJbAtCurrentOptimum")
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJoAtCurrentOptimum")
+ self.StoredVariables["GradientOfCostFunctionJ"] = Persistence.OneVector(name = "GradientOfCostFunctionJ")
+ self.StoredVariables["GradientOfCostFunctionJb"] = Persistence.OneVector(name = "GradientOfCostFunctionJb")
+ self.StoredVariables["GradientOfCostFunctionJo"] = Persistence.OneVector(name = "GradientOfCostFunctionJo")
+ self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState")
+ self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
+ self.StoredVariables["IndexOfOptimum"] = Persistence.OneIndex(name = "IndexOfOptimum")
+ self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum")
+ self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground")
+ self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState")
+ self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum")
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentOptimum")
+ self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation")
+ self.StoredVariables["InnovationAtCurrentState"] = Persistence.OneVector(name = "InnovationAtCurrentState")
+ self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2")
+ self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2")
+ self.StoredVariables["MahalanobisConsistency"] = Persistence.OneScalar(name = "MahalanobisConsistency")
+ self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA")
+ self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB")
+ self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA")
+ self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance")
+ self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances")
+ self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations")
+ self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations")
+ self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles")
+ self.StoredVariables["Residu"] = Persistence.OneScalar(name = "Residu")
+
+ def _pre_run(self, Parameters, Xb=None, Y=None, R=None, B=None, Q=None ):
+ "Pré-calcul"
+ logging.debug("%s Lancement", self._name)
+ logging.debug("%s Taille mémoire utilisée de %.0f Mio", self._name, self._m.getUsedMemory("Mio"))
+ #
+ # Mise a jour de self._parameters avec Parameters
+ self.__setParameters(Parameters)
+ #
+ # Corrections et complements
+ def __test_vvalue( argument, variable, argname):
+ if argument is None:
+ if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
+ raise ValueError("%s %s vector %s has to be properly defined!"%(self._name,argname,variable))
+ elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
+ logging.debug("%s %s vector %s is not set, but is optional."%(self._name,argname,variable))
+ else:
+ logging.debug("%s %s vector %s is not set, but is not required."%(self._name,argname,variable))
+ else:
+ logging.debug("%s %s vector %s is set, and its size is %i."%(self._name,argname,variable,numpy.array(argument).size))
+ __test_vvalue( Xb, "Xb", "Background or initial state" )
+ __test_vvalue( Y, "Y", "Observation" )
+ def __test_cvalue( argument, variable, argname):
+ if argument is None:
+ if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
+ raise ValueError("%s %s error covariance matrix %s has to be properly defined!"%(self._name,argname,variable))
+ elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
+ logging.debug("%s %s error covariance matrix %s is not set, but is optional."%(self._name,argname,variable))
+ else:
+ logging.debug("%s %s error covariance matrix %s is not set, but is not required."%(self._name,argname,variable))
+ else:
+ logging.debug("%s %s error covariance matrix %s is set."%(self._name,argname,variable))
+ __test_cvalue( R, "R", "Observation" )
+ __test_cvalue( B, "B", "Background" )
+ __test_cvalue( Q, "Q", "Evolution" )
+ #
+ if ("Bounds" in self._parameters) and isinstance(self._parameters["Bounds"], (list, tuple)) and (len(self._parameters["Bounds"]) > 0):
+ logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
+ else:
+ self._parameters["Bounds"] = None
+ #
+ if logging.getLogger().level < logging.WARNING:
+ self._parameters["optiprint"], self._parameters["optdisp"] = 1, 1
+ if PlatformInfo.has_scipy:
+ import scipy.optimize
+ self._parameters["optmessages"] = scipy.optimize.tnc.MSG_ALL
+ else:
+ self._parameters["optmessages"] = 15
+ else:
+ self._parameters["optiprint"], self._parameters["optdisp"] = -1, 0
+ if PlatformInfo.has_scipy:
+ import scipy.optimize
+ self._parameters["optmessages"] = scipy.optimize.tnc.MSG_NONE
+ else:
+ self._parameters["optmessages"] = 15
#
- self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ")
- self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
- self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
- self.StoredVariables["GradientOfCostFunctionJ"] = Persistence.OneVector(name = "GradientOfCostFunctionJ")
- self.StoredVariables["GradientOfCostFunctionJb"] = Persistence.OneVector(name = "GradientOfCostFunctionJb")
- self.StoredVariables["GradientOfCostFunctionJo"] = Persistence.OneVector(name = "GradientOfCostFunctionJo")
- self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState")
- self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
- self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground")
- self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState")
- self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum")
- self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation")
- self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2")
- self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2")
- self.StoredVariables["MahalanobisConsistency"] = Persistence.OneScalar(name = "MahalanobisConsistency")
- self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA")
- self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB")
- self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA")
- self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance")
- self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances")
- self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations")
- self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations")
- self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles")
-
- def _pre_run(self):
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mio"%(self._name, self._m.getUsedMemory("Mio")))
return 0
def _post_run(self,_oH=None):
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ "Post-calcul"
+ if ("StoreSupplementaryCalculations" in self._parameters) and \
+ "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
for _A in self.StoredVariables["APosterioriCovariance"]:
if "APosterioriVariances" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["APosterioriVariances"].store( numpy.diag(_A) )
_C = numpy.dot(_EI, numpy.dot(_A, _EI))
self.StoredVariables["APosterioriCorrelations"].store( _C )
if _oH is not None:
- logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i"%(self._name, _oH["Direct"].nbcalls(0),_oH["Tangent"].nbcalls(0),_oH["Adjoint"].nbcalls(0)))
- logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i"%(self._name, _oH["Direct"].nbcalls(3),_oH["Tangent"].nbcalls(3),_oH["Adjoint"].nbcalls(3)))
- logging.debug("%s Taille mémoire utilisée de %.1f Mio"%(self._name, self._m.getUsedMemory("Mio")))
- logging.debug("%s Terminé"%self._name)
+ logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i", self._name, _oH["Direct"].nbcalls(0),_oH["Tangent"].nbcalls(0),_oH["Adjoint"].nbcalls(0))
+ logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i", self._name, _oH["Direct"].nbcalls(3),_oH["Tangent"].nbcalls(3),_oH["Adjoint"].nbcalls(3))
+ logging.debug("%s Taille mémoire utilisée de %.0f Mio", self._name, self._m.getUsedMemory("Mio"))
+ logging.debug("%s Terminé", self._name)
return 0
def get(self, key=None):
"""
- Renvoie l'une des variables stockées identifiée par la clé, ou le
+ Renvoie l'une des variables stockées identifiée par la clé, ou le
dictionnaire de l'ensemble des variables disponibles en l'absence de
- clé. Ce sont directement les variables sous forme objet qui sont
- renvoyées, donc les méthodes d'accès à l'objet individuel sont celles
+ clé. Ce sont directement les variables sous forme objet qui sont
+ renvoyées, donc les méthodes d'accès à l'objet individuel sont celles
des classes de persistance.
"""
if key is not None:
else:
return self.StoredVariables
- def has_key(self, key=None):
- """
- Vérifie si l'une des variables stockées est identifiée par la clé.
- """
- return self.StoredVariables.has_key(key)
+ def __contains__(self, key=None):
+ "D.__contains__(k) -> True if D has a key k, else False"
+ return key in self.StoredVariables
def keys(self):
- """
- Renvoie la liste des clés de variables stockées.
- """
- return self.StoredVariables.keys()
+ "D.keys() -> list of D's keys"
+ if hasattr(self, "StoredVariables"):
+ return self.StoredVariables.keys()
+ else:
+ return []
+
+ def pop(self, k, d):
+ "D.pop(k[,d]) -> v, remove specified key and return the corresponding value"
+ if hasattr(self, "StoredVariables"):
+ return self.StoredVariables.pop(k, d)
+ else:
+ try:
+ msg = "'%s'"%k
+ except:
+ raise TypeError("pop expected at least 1 arguments, got 0")
+ "If key is not found, d is returned if given, otherwise KeyError is raised"
+ try:
+ return d
+ except:
+ raise KeyError(msg)
def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
"""
- Doit implémenter l'opération élémentaire de calcul d'assimilation sous
- sa forme mathématique la plus naturelle possible.
+ Doit implémenter l'opération élémentaire de calcul d'assimilation sous
+ sa forme mathématique la plus naturelle possible.
"""
raise NotImplementedError("Mathematical assimilation calculation has not been implemented!")
def defineRequiredParameter(self, name = None, default = None, typecast = None, message = None, minval = None, maxval = None, listval = None):
"""
- Permet de définir dans l'algorithme des paramètres requis et leurs
- caractéristiques par défaut.
+ Permet de définir dans l'algorithme des paramètres requis et leurs
+ caractéristiques par défaut.
"""
if name is None:
raise ValueError("A name is mandatory to define a required parameter.")
"listval" : listval,
"message" : message,
}
- logging.debug("%s %s (valeur par défaut = %s)"%(self._name, message, self.setParameterValue(name)))
+ logging.debug("%s %s (valeur par défaut = %s)", self._name, message, self.setParameterValue(name))
def getRequiredParameters(self, noDetails=True):
"""
- Renvoie la liste des noms de paramètres requis ou directement le
- dictionnaire des paramètres requis.
+ Renvoie la liste des noms de paramètres requis ou directement le
+ dictionnaire des paramètres requis.
"""
if noDetails:
- ks = self.__required_parameters.keys()
- ks.sort()
- return ks
+ return sorted(self.__required_parameters.keys())
else:
return self.__required_parameters
def setParameterValue(self, name=None, value=None):
"""
- Renvoie la valeur d'un paramètre requis de manière contrôlée
+ Renvoie la valeur d'un paramètre requis de manière contrôlée
"""
default = self.__required_parameters[name]["default"]
typecast = self.__required_parameters[name]["typecast"]
if typecast is None: __val = value
else: __val = typecast( value )
#
- if minval is not None and (numpy.array(__val) < minval).any():
+ if minval is not None and (numpy.array(__val, float) < minval).any():
raise ValueError("The parameter named \"%s\" of value \"%s\" can not be less than %s."%(name, __val, minval))
- if maxval is not None and (numpy.array(__val) > maxval).any():
+ if maxval is not None and (numpy.array(__val, float) > maxval).any():
raise ValueError("The parameter named \"%s\" of value \"%s\" can not be greater than %s."%(name, __val, maxval))
if listval is not None:
- if typecast is list or typecast is tuple or type(__val) is list or type(__val) is tuple:
+ if typecast is list or typecast is tuple or isinstance(__val,list) or isinstance(__val,tuple):
for v in __val:
if v not in listval:
raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%(v, name, listval))
raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%( __val, name,listval))
return __val
- def setParameters(self, fromDico={}):
+ def requireInputArguments(self, mandatory=(), optional=()):
+ """
+ Permet d'imposer des arguments requises en entrée
+ """
+ self.__required_inputs["RequiredInputValues"]["mandatory"] = tuple( mandatory )
+ self.__required_inputs["RequiredInputValues"]["optional"] = tuple( optional )
+
+ def __setParameters(self, fromDico={}):
"""
- Permet de stocker les paramètres reçus dans le dictionnaire interne.
+ Permet de stocker les paramètres reçus dans le dictionnaire interne.
"""
self._parameters.update( fromDico )
for k in self.__required_parameters.keys():
self._parameters[k] = self.setParameterValue(k,fromDico[k])
else:
self._parameters[k] = self.setParameterValue(k)
- logging.debug("%s %s : %s"%(self._name, self.__required_parameters[k]["message"], self._parameters[k]))
+ logging.debug("%s %s : %s", self._name, self.__required_parameters[k]["message"], self._parameters[k])
# ==============================================================================
-class Diagnostic:
+class AlgorithmAndParameters(object):
"""
- Classe générale d'interface de type diagnostic
-
- Ce template s'utilise de la manière suivante : il sert de classe "patron" en
- même temps que l'une des classes de persistance, comme "OneScalar" par
- exemple.
-
- Une classe élémentaire de diagnostic doit implémenter ses deux méthodes, la
- méthode "_formula" pour écrire explicitement et proprement la formule pour
- l'écriture mathématique du calcul du diagnostic (méthode interne non
- publique), et "calculate" pour activer la précédente tout en ayant vérifié
- et préparé les données, et pour stocker les résultats à chaque pas (méthode
- externe d'activation).
+ Classe générale d'interface d'action pour l'algorithme et ses paramètres
"""
- def __init__(self, name = "", parameters = {}):
- self.name = str(name)
- self.parameters = dict( parameters )
+ def __init__(self,
+ name = "GenericAlgorithm",
+ asAlgorithm = None,
+ asDict = None,
+ asScript = None,
+ ):
+ """
+ """
+ self.__name = str(name)
+ self.__A = None
+ self.__P = {}
+ #
+ self.__algorithm = {}
+ self.__algorithmFile = None
+ self.__algorithmName = None
+ #
+ self.updateParameters( asDict, asScript )
+ #
+ if asAlgorithm is None and asScript is not None:
+ __Algo = ImportFromScript(asScript).getvalue( "Algorithm" )
+ else:
+ __Algo = asAlgorithm
+ #
+ if __Algo is not None:
+ self.__A = str(__Algo)
+ self.__P.update( {"Algorithm":self.__A} )
+ #
+ self.__setAlgorithm( self.__A )
+
+ def updateParameters(self,
+ asDict = None,
+ asScript = None,
+ ):
+ "Mise a jour des parametres"
+ if asDict is None and asScript is not None:
+ __Dict = ImportFromScript(asScript).getvalue( self.__name, "Parameters" )
+ else:
+ __Dict = asDict
+ #
+ if __Dict is not None:
+ self.__P.update( dict(__Dict) )
+
+ def executePythonScheme(self, asDictAO = None):
+ "Permet de lancer le calcul d'assimilation"
+ Operator.CM.clearCache()
+ #
+ if not isinstance(asDictAO, dict):
+ raise ValueError("The objects for algorithm calculation have to be given together as a dictionnary, and they are not")
+ if hasattr(asDictAO["Background"],"getO"): self.__Xb = asDictAO["Background"].getO()
+ elif hasattr(asDictAO["CheckingPoint"],"getO"): self.__Xb = asDictAO["CheckingPoint"].getO()
+ else: self.__Xb = None
+ if hasattr(asDictAO["Observation"],"getO"): self.__Y = asDictAO["Observation"].getO()
+ else: self.__Y = asDictAO["Observation"]
+ if hasattr(asDictAO["ControlInput"],"getO"): self.__U = asDictAO["ControlInput"].getO()
+ else: self.__U = asDictAO["ControlInput"]
+ if hasattr(asDictAO["ObservationOperator"],"getO"): self.__HO = asDictAO["ObservationOperator"].getO()
+ else: self.__HO = asDictAO["ObservationOperator"]
+ if hasattr(asDictAO["EvolutionModel"],"getO"): self.__EM = asDictAO["EvolutionModel"].getO()
+ else: self.__EM = asDictAO["EvolutionModel"]
+ if hasattr(asDictAO["ControlModel"],"getO"): self.__CM = asDictAO["ControlModel"].getO()
+ else: self.__CM = asDictAO["ControlModel"]
+ self.__B = asDictAO["BackgroundError"]
+ self.__R = asDictAO["ObservationError"]
+ self.__Q = asDictAO["EvolutionError"]
+ #
+ self.__shape_validate()
+ #
+ self.__algorithm.run(
+ Xb = self.__Xb,
+ Y = self.__Y,
+ U = self.__U,
+ HO = self.__HO,
+ EM = self.__EM,
+ CM = self.__CM,
+ R = self.__R,
+ B = self.__B,
+ Q = self.__Q,
+ Parameters = self.__P,
+ )
+ return 0
+
+ def executeYACSScheme(self, FileName=None):
+ "Permet de lancer le calcul d'assimilation"
+ if FileName is None or not os.path.exists(FileName):
+ raise ValueError("a YACS file name has to be given for YACS execution.\n")
+ if not PlatformInfo.has_salome or \
+ not PlatformInfo.has_yacs or \
+ not PlatformInfo.has_adao:
+ raise ImportError("\n\n"+\
+ "Unable to get SALOME, YACS or ADAO environnement variables.\n"+\
+ "Please load the right environnement before trying to use it.\n")
+ #
+ import pilot
+ import SALOMERuntime
+ import loader
+ SALOMERuntime.RuntimeSALOME_setRuntime()
+
+ r = pilot.getRuntime()
+ xmlLoader = loader.YACSLoader()
+ xmlLoader.registerProcCataLoader()
+ try:
+ catalogAd = r.loadCatalog("proc", os.path.abspath(FileName))
+ r.addCatalog(catalogAd)
+ except:
+ pass
+
+ try:
+ p = xmlLoader.load(os.path.abspath(FileName))
+ except IOError as ex:
+ print("The YACS XML schema file can not be loaded: %s"%(ex,))
+
+ logger = p.getLogger("parser")
+ if not logger.isEmpty():
+ print("The imported YACS XML schema has errors on parsing:")
+ print(logger.getStr())
+
+ if not p.isValid():
+ print("The YACS XML schema is not valid and will not be executed:")
+ print(p.getErrorReport())
+
+ info=pilot.LinkInfo(pilot.LinkInfo.ALL_DONT_STOP)
+ p.checkConsistency(info)
+ if info.areWarningsOrErrors():
+ print("The YACS XML schema is not coherent and will not be executed:")
+ print(info.getGlobalRepr())
+
+ e = pilot.ExecutorSwig()
+ e.RunW(p)
+ if p.getEffectiveState() != pilot.DONE:
+ print(p.getErrorReport())
+ #
+ return 0
+
+ def get(self, key = None):
+ "Vérifie l'existence d'une clé de variable ou de paramètres"
+ if key in self.__algorithm:
+ return self.__algorithm.get( key )
+ elif key in self.__P:
+ return self.__P[key]
+ else:
+ return self.__P
+
+ def pop(self, k, d):
+ "Necessaire pour le pickling"
+ return self.__algorithm.pop(k, d)
+
+ def getAlgorithmRequiredParameters(self, noDetails=True):
+ "Renvoie la liste des paramètres requis selon l'algorithme"
+ return self.__algorithm.getRequiredParameters(noDetails)
+
+ def setObserver(self, __V, __O, __I, __S):
+ if self.__algorithm is None \
+ or isinstance(self.__algorithm, dict) \
+ or not hasattr(self.__algorithm,"StoredVariables"):
+ raise ValueError("No observer can be build before choosing an algorithm.")
+ if __V not in self.__algorithm:
+ raise ValueError("An observer requires to be set on a variable named %s which does not exist."%__V)
+ else:
+ self.__algorithm.StoredVariables[ __V ].setDataObserver(
+ Scheduler = __S,
+ HookFunction = __O,
+ HookParameters = __I,
+ )
+
+ def removeObserver(self, __V, __O, __A = False):
+ if self.__algorithm is None \
+ or isinstance(self.__algorithm, dict) \
+ or not hasattr(self.__algorithm,"StoredVariables"):
+ raise ValueError("No observer can be removed before choosing an algorithm.")
+ if __V not in self.__algorithm:
+ raise ValueError("An observer requires to be removed on a variable named %s which does not exist."%__V)
+ else:
+ return self.__algorithm.StoredVariables[ __V ].removeDataObserver(
+ HookFunction = __O,
+ AllObservers = __A,
+ )
+
+ def hasObserver(self, __V):
+ if self.__algorithm is None \
+ or isinstance(self.__algorithm, dict) \
+ or not hasattr(self.__algorithm,"StoredVariables"):
+ return False
+ if __V not in self.__algorithm:
+ return False
+ return self.__algorithm.StoredVariables[ __V ].hasDataObserver()
+
+ def keys(self):
+ return list(self.__algorithm.keys()) + list(self.__P.keys())
+
+ def __contains__(self, key=None):
+ "D.__contains__(k) -> True if D has a key k, else False"
+ return key in self.__algorithm or key in self.__P
+
+ def __repr__(self):
+ "x.__repr__() <==> repr(x)"
+ return repr(self.__A)+", "+repr(self.__P)
+
+ def __str__(self):
+ "x.__str__() <==> str(x)"
+ return str(self.__A)+", "+str(self.__P)
- def _formula(self, *args):
+ def __setAlgorithm(self, choice = None ):
"""
- Doit implémenter l'opération élémentaire de diagnostic sous sa forme
- mathématique la plus naturelle possible.
+ Permet de sélectionner l'algorithme à utiliser pour mener à bien l'étude
+ d'assimilation. L'argument est un champ caractère se rapportant au nom
+ d'un algorithme réalisant l'opération sur les arguments fixes.
"""
- raise NotImplementedError("Diagnostic mathematical formula has not been implemented!")
+ if choice is None:
+ raise ValueError("Error: algorithm choice has to be given")
+ if self.__algorithmName is not None:
+ raise ValueError("Error: algorithm choice has already been done as \"%s\", it can't be changed."%self.__algorithmName)
+ daDirectory = "daAlgorithms"
+ #
+ # Recherche explicitement le fichier complet
+ # ------------------------------------------
+ module_path = None
+ for directory in sys.path:
+ if os.path.isfile(os.path.join(directory, daDirectory, str(choice)+'.py')):
+ module_path = os.path.abspath(os.path.join(directory, daDirectory))
+ if module_path is None:
+ raise ImportError("No algorithm module named \"%s\" has been found in the search path.\n The search path is %s"%(choice, sys.path))
+ #
+ # Importe le fichier complet comme un module
+ # ------------------------------------------
+ try:
+ sys_path_tmp = sys.path ; sys.path.insert(0,module_path)
+ self.__algorithmFile = __import__(str(choice), globals(), locals(), [])
+ if not hasattr(self.__algorithmFile, "ElementaryAlgorithm"):
+ raise ImportError("this module does not define a valid elementary algorithm.")
+ self.__algorithmName = str(choice)
+ sys.path = sys_path_tmp ; del sys_path_tmp
+ except ImportError as e:
+ raise ImportError("The module named \"%s\" was found, but is incorrect at the import stage.\n The import error message is: %s"%(choice,e))
+ #
+ # Instancie un objet du type élémentaire du fichier
+ # -------------------------------------------------
+ self.__algorithm = self.__algorithmFile.ElementaryAlgorithm()
+ return 0
+
+ def __shape_validate(self):
+ """
+ Validation de la correspondance correcte des tailles des variables et
+ des matrices s'il y en a.
+ """
+ if self.__Xb is None: __Xb_shape = (0,)
+ elif hasattr(self.__Xb,"size"): __Xb_shape = (self.__Xb.size,)
+ elif hasattr(self.__Xb,"shape"):
+ if isinstance(self.__Xb.shape, tuple): __Xb_shape = self.__Xb.shape
+ else: __Xb_shape = self.__Xb.shape()
+ else: raise TypeError("The background (Xb) has no attribute of shape: problem !")
+ #
+ if self.__Y is None: __Y_shape = (0,)
+ elif hasattr(self.__Y,"size"): __Y_shape = (self.__Y.size,)
+ elif hasattr(self.__Y,"shape"):
+ if isinstance(self.__Y.shape, tuple): __Y_shape = self.__Y.shape
+ else: __Y_shape = self.__Y.shape()
+ else: raise TypeError("The observation (Y) has no attribute of shape: problem !")
+ #
+ if self.__U is None: __U_shape = (0,)
+ elif hasattr(self.__U,"size"): __U_shape = (self.__U.size,)
+ elif hasattr(self.__U,"shape"):
+ if isinstance(self.__U.shape, tuple): __U_shape = self.__U.shape
+ else: __U_shape = self.__U.shape()
+ else: raise TypeError("The control (U) has no attribute of shape: problem !")
+ #
+ if self.__B is None: __B_shape = (0,0)
+ elif hasattr(self.__B,"shape"):
+ if isinstance(self.__B.shape, tuple): __B_shape = self.__B.shape
+ else: __B_shape = self.__B.shape()
+ else: raise TypeError("The a priori errors covariance matrix (B) has no attribute of shape: problem !")
+ #
+ if self.__R is None: __R_shape = (0,0)
+ elif hasattr(self.__R,"shape"):
+ if isinstance(self.__R.shape, tuple): __R_shape = self.__R.shape
+ else: __R_shape = self.__R.shape()
+ else: raise TypeError("The observation errors covariance matrix (R) has no attribute of shape: problem !")
+ #
+ if self.__Q is None: __Q_shape = (0,0)
+ elif hasattr(self.__Q,"shape"):
+ if isinstance(self.__Q.shape, tuple): __Q_shape = self.__Q.shape
+ else: __Q_shape = self.__Q.shape()
+ else: raise TypeError("The evolution errors covariance matrix (Q) has no attribute of shape: problem !")
+ #
+ if len(self.__HO) == 0: __HO_shape = (0,0)
+ elif isinstance(self.__HO, dict): __HO_shape = (0,0)
+ elif hasattr(self.__HO["Direct"],"shape"):
+ if isinstance(self.__HO["Direct"].shape, tuple): __HO_shape = self.__HO["Direct"].shape
+ else: __HO_shape = self.__HO["Direct"].shape()
+ else: raise TypeError("The observation operator (H) has no attribute of shape: problem !")
+ #
+ if len(self.__EM) == 0: __EM_shape = (0,0)
+ elif isinstance(self.__EM, dict): __EM_shape = (0,0)
+ elif hasattr(self.__EM["Direct"],"shape"):
+ if isinstance(self.__EM["Direct"].shape, tuple): __EM_shape = self.__EM["Direct"].shape
+ else: __EM_shape = self.__EM["Direct"].shape()
+ else: raise TypeError("The evolution model (EM) has no attribute of shape: problem !")
+ #
+ if len(self.__CM) == 0: __CM_shape = (0,0)
+ elif isinstance(self.__CM, dict): __CM_shape = (0,0)
+ elif hasattr(self.__CM["Direct"],"shape"):
+ if isinstance(self.__CM["Direct"].shape, tuple): __CM_shape = self.__CM["Direct"].shape
+ else: __CM_shape = self.__CM["Direct"].shape()
+ else: raise TypeError("The control model (CM) has no attribute of shape: problem !")
+ #
+ # Vérification des conditions
+ # ---------------------------
+ if not( len(__Xb_shape) == 1 or min(__Xb_shape) == 1 ):
+ raise ValueError("Shape characteristic of background (Xb) is incorrect: \"%s\"."%(__Xb_shape,))
+ if not( len(__Y_shape) == 1 or min(__Y_shape) == 1 ):
+ raise ValueError("Shape characteristic of observation (Y) is incorrect: \"%s\"."%(__Y_shape,))
+ #
+ if not( min(__B_shape) == max(__B_shape) ):
+ raise ValueError("Shape characteristic of a priori errors covariance matrix (B) is incorrect: \"%s\"."%(__B_shape,))
+ if not( min(__R_shape) == max(__R_shape) ):
+ raise ValueError("Shape characteristic of observation errors covariance matrix (R) is incorrect: \"%s\"."%(__R_shape,))
+ if not( min(__Q_shape) == max(__Q_shape) ):
+ raise ValueError("Shape characteristic of evolution errors covariance matrix (Q) is incorrect: \"%s\"."%(__Q_shape,))
+ if not( min(__EM_shape) == max(__EM_shape) ):
+ raise ValueError("Shape characteristic of evolution operator (EM) is incorrect: \"%s\"."%(__EM_shape,))
+ #
+ if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and not( __HO_shape[1] == max(__Xb_shape) ):
+ raise ValueError("Shape characteristic of observation operator (H) \"%s\" and state (X) \"%s\" are incompatible."%(__HO_shape,__Xb_shape))
+ if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and not( __HO_shape[0] == max(__Y_shape) ):
+ raise ValueError("Shape characteristic of observation operator (H) \"%s\" and observation (Y) \"%s\" are incompatible."%(__HO_shape,__Y_shape))
+ if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and len(self.__B) > 0 and not( __HO_shape[1] == __B_shape[0] ):
+ raise ValueError("Shape characteristic of observation operator (H) \"%s\" and a priori errors covariance matrix (B) \"%s\" are incompatible."%(__HO_shape,__B_shape))
+ if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and len(self.__R) > 0 and not( __HO_shape[0] == __R_shape[1] ):
+ raise ValueError("Shape characteristic of observation operator (H) \"%s\" and observation errors covariance matrix (R) \"%s\" are incompatible."%(__HO_shape,__R_shape))
+ #
+ if self.__B is not None and len(self.__B) > 0 and not( __B_shape[1] == max(__Xb_shape) ):
+ if self.__algorithmName in ["EnsembleBlue",]:
+ asPersistentVector = self.__Xb.reshape((-1,min(__B_shape)))
+ self.__Xb = Persistence.OneVector("Background", basetype=numpy.matrix)
+ for member in asPersistentVector:
+ self.__Xb.store( numpy.matrix( numpy.ravel(member), numpy.float ).T )
+ __Xb_shape = min(__B_shape)
+ else:
+ raise ValueError("Shape characteristic of a priori errors covariance matrix (B) \"%s\" and background (Xb) \"%s\" are incompatible."%(__B_shape,__Xb_shape))
+ #
+ if self.__R is not None and len(self.__R) > 0 and not( __R_shape[1] == max(__Y_shape) ):
+ raise ValueError("Shape characteristic of observation errors covariance matrix (R) \"%s\" and observation (Y) \"%s\" are incompatible."%(__R_shape,__Y_shape))
+ #
+ if self.__EM is not None and len(self.__EM) > 0 and not isinstance(self.__EM, dict) and not( __EM_shape[1] == max(__Xb_shape) ):
+ raise ValueError("Shape characteristic of evolution model (EM) \"%s\" and state (X) \"%s\" are incompatible."%(__EM_shape,__Xb_shape))
+ #
+ if self.__CM is not None and len(self.__CM) > 0 and not isinstance(self.__CM, dict) and not( __CM_shape[1] == max(__U_shape) ):
+ raise ValueError("Shape characteristic of control model (CM) \"%s\" and control (U) \"%s\" are incompatible."%(__CM_shape,__U_shape))
+ #
+ if ("Bounds" in self.__P) \
+ and (isinstance(self.__P["Bounds"], list) or isinstance(self.__P["Bounds"], tuple)) \
+ and (len(self.__P["Bounds"]) != max(__Xb_shape)):
+ raise ValueError("The number \"%s\" of bound pairs for the state (X) components is different of the size \"%s\" of the state itself." \
+ %(len(self.__P["Bounds"]),max(__Xb_shape)))
+ #
+ return 1
- def calculate(self, *args):
+# ==============================================================================
+class DataObserver(object):
+ """
+ Classe générale d'interface de type observer
+ """
+ def __init__(self,
+ name = "GenericObserver",
+ onVariable = None,
+ asTemplate = None,
+ asString = None,
+ asScript = None,
+ asObsObject = None,
+ withInfo = None,
+ scheduledBy = None,
+ withAlgo = None,
+ ):
"""
- Active la formule de calcul avec les arguments correctement rangés
"""
- raise NotImplementedError("Diagnostic activation method has not been implemented!")
+ self.__name = str(name)
+ self.__V = None
+ self.__O = None
+ self.__I = None
+ #
+ if onVariable is None:
+ raise ValueError("setting an observer has to be done over a variable name or a list of variable names, not over None.")
+ elif type(onVariable) in (tuple, list):
+ self.__V = tuple(map( str, onVariable ))
+ if withInfo is None:
+ self.__I = self.__V
+ else:
+ self.__I = (str(withInfo),)*len(self.__V)
+ elif isinstance(onVariable, str):
+ self.__V = (onVariable,)
+ if withInfo is None:
+ self.__I = (onVariable,)
+ else:
+ self.__I = (str(withInfo),)
+ else:
+ raise ValueError("setting an observer has to be done over a variable name or a list of variable names.")
+ #
+ if asString is not None:
+ __FunctionText = asString
+ elif (asTemplate is not None) and (asTemplate in Templates.ObserverTemplates):
+ __FunctionText = Templates.ObserverTemplates[asTemplate]
+ elif asScript is not None:
+ __FunctionText = ImportFromScript(asScript).getstring()
+ else:
+ __FunctionText = ""
+ __Function = ObserverF(__FunctionText)
+ #
+ if asObsObject is not None:
+ self.__O = asObsObject
+ else:
+ self.__O = __Function.getfunc()
+ #
+ for k in range(len(self.__V)):
+ ename = self.__V[k]
+ einfo = self.__I[k]
+ if ename not in withAlgo:
+ raise ValueError("An observer is asked to be set on a variable named %s which does not exist."%ename)
+ else:
+ withAlgo.setObserver(ename, self.__O, einfo, scheduledBy)
+
+ def __repr__(self):
+ "x.__repr__() <==> repr(x)"
+ return repr(self.__V)+"\n"+repr(self.__O)
+
+ def __str__(self):
+ "x.__str__() <==> str(x)"
+ return str(self.__V)+"\n"+str(self.__O)
# ==============================================================================
-class Covariance:
+class State(object):
"""
- Classe générale d'interface de type covariance
+ Classe générale d'interface de type état
"""
def __init__(self,
- name = "GenericCovariance",
- asCovariance = None,
- asEyeByScalar = None,
- asEyeByVector = None,
- asCovObject = None,
- ):
- """
- Permet de définir une covariance :
- - asCovariance : entrée des données, comme une matrice compatible avec
+ name = "GenericVector",
+ asVector = None,
+ asPersistentVector = None,
+ asScript = None,
+ scheduledBy = None,
+ toBeChecked = False,
+ ):
+ """
+ Permet de définir un vecteur :
+ - asVector : entrée des données, comme un vecteur compatible avec le
+ constructeur de numpy.matrix, ou "True" si entrée par script.
+ - asPersistentVector : entrée des données, comme une série de vecteurs
+ compatible avec le constructeur de numpy.matrix, ou comme un objet de
+ type Persistence, ou "True" si entrée par script.
+ - asScript : si un script valide est donné contenant une variable
+ nommée "name", la variable est de type "asVector" (par défaut) ou
+ "asPersistentVector" selon que l'une de ces variables est placée à
+ "True".
+ """
+ self.__name = str(name)
+ self.__check = bool(toBeChecked)
+ #
+ self.__V = None
+ self.__T = None
+ self.__is_vector = False
+ self.__is_series = False
+ #
+ if asScript is not None:
+ __Vector, __Series = None, None
+ if asPersistentVector:
+ __Series = ImportFromScript(asScript).getvalue( self.__name )
+ else:
+ __Vector = ImportFromScript(asScript).getvalue( self.__name )
+ else:
+ __Vector, __Series = asVector, asPersistentVector
+ #
+ if __Vector is not None:
+ self.__is_vector = True
+ self.__V = numpy.matrix( numpy.asmatrix(__Vector).A1, numpy.float ).T
+ self.shape = self.__V.shape
+ self.size = self.__V.size
+ elif __Series is not None:
+ self.__is_series = True
+ if isinstance(__Series, (tuple, list, numpy.ndarray, numpy.matrix, str)):
+ self.__V = Persistence.OneVector(self.__name, basetype=numpy.matrix)
+ if isinstance(__Series, str): __Series = eval(__Series)
+ for member in __Series:
+ self.__V.store( numpy.matrix( numpy.asmatrix(member).A1, numpy.float ).T )
+ import sys ; sys.stdout.flush()
+ else:
+ self.__V = __Series
+ if isinstance(self.__V.shape, (tuple, list)):
+ self.shape = self.__V.shape
+ else:
+ self.shape = self.__V.shape()
+ if len(self.shape) == 1:
+ self.shape = (self.shape[0],1)
+ self.size = self.shape[0] * self.shape[1]
+ else:
+ raise ValueError("The %s object is improperly defined, it requires at minima either a vector, a list/tuple of vectors or a persistent object. Please check your vector input."%self.__name)
+ #
+ if scheduledBy is not None:
+ self.__T = scheduledBy
+
+ def getO(self, withScheduler=False):
+ if withScheduler:
+ return self.__V, self.__T
+ elif self.__T is None:
+ return self.__V
+ else:
+ return self.__V
+
+ def isvector(self):
+ "Vérification du type interne"
+ return self.__is_vector
+
+ def isseries(self):
+ "Vérification du type interne"
+ return self.__is_series
+
+ def __repr__(self):
+ "x.__repr__() <==> repr(x)"
+ return repr(self.__V)
+
+ def __str__(self):
+ "x.__str__() <==> str(x)"
+ return str(self.__V)
+
+# ==============================================================================
+class Covariance(object):
+ """
+ Classe générale d'interface de type covariance
+ """
+ def __init__(self,
+ name = "GenericCovariance",
+ asCovariance = None,
+ asEyeByScalar = None,
+ asEyeByVector = None,
+ asCovObject = None,
+ asScript = None,
+ toBeChecked = False,
+ ):
+ """
+ Permet de définir une covariance :
+ - asCovariance : entrée des données, comme une matrice compatible avec
le constructeur de numpy.matrix
- - asEyeByScalar : entrée des données comme un seul scalaire de variance,
- multiplicatif d'une matrice de corrélation identité, aucune matrice
- n'étant donc explicitement à donner
- - asEyeByVector : entrée des données comme un seul vecteur de variance,
- à mettre sur la diagonale d'une matrice de corrélation, aucune matrice
- n'étant donc explicitement à donner
- - asCovObject : entrée des données comme un objet python, qui a les
+ - asEyeByScalar : entrée des données comme un seul scalaire de variance,
+ multiplicatif d'une matrice de corrélation identité, aucune matrice
+ n'étant donc explicitement à donner
+ - asEyeByVector : entrée des données comme un seul vecteur de variance,
+ à mettre sur la diagonale d'une matrice de corrélation, aucune matrice
+ n'étant donc explicitement à donner
+ - asCovObject : entrée des données comme un objet python, qui a les
methodes obligatoires "getT", "getI", "diag", "trace", "__add__",
"__sub__", "__neg__", "__mul__", "__rmul__" et facultatives "shape",
"size", "cholesky", "choleskyI", "asfullmatrix", "__repr__", "__str__"
+ - toBeChecked : booléen indiquant si le caractère SDP de la matrice
+ pleine doit être vérifié
"""
self.__name = str(name)
+ self.__check = bool(toBeChecked)
#
self.__C = None
self.__is_scalar = False
self.__is_vector = False
self.__is_matrix = False
self.__is_object = False
- if asEyeByScalar is not None:
+ #
+ if asScript is not None:
+ __Matrix, __Scalar, __Vector, __Object = None, None, None, None
+ if asEyeByScalar:
+ __Scalar = ImportFromScript(asScript).getvalue( self.__name )
+ elif asEyeByVector:
+ __Vector = ImportFromScript(asScript).getvalue( self.__name )
+ elif asCovObject:
+ __Object = ImportFromScript(asScript).getvalue( self.__name )
+ else:
+ __Matrix = ImportFromScript(asScript).getvalue( self.__name )
+ else:
+ __Matrix, __Scalar, __Vector, __Object = asCovariance, asEyeByScalar, asEyeByVector, asCovObject
+ #
+ if __Scalar is not None:
+ if numpy.matrix(__Scalar).size != 1:
+ raise ValueError(' The diagonal multiplier given to define a sparse matrix is not a unique scalar value.\n Its actual measured size is %i. Please check your scalar input.'%numpy.matrix(__Scalar).size)
self.__is_scalar = True
- self.__C = numpy.abs( float(asEyeByScalar) )
+ self.__C = numpy.abs( float(__Scalar) )
self.shape = (0,0)
self.size = 0
- elif asEyeByVector is not None:
+ elif __Vector is not None:
self.__is_vector = True
- self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(asEyeByVector, float ) ) ) )
+ self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(__Vector, float ) ) ) )
self.shape = (self.__C.size,self.__C.size)
self.size = self.__C.size**2
- elif asCovariance is not None:
+ elif __Matrix is not None:
self.__is_matrix = True
- self.__C = numpy.matrix( asCovariance, float )
+ self.__C = numpy.matrix( __Matrix, float )
self.shape = self.__C.shape
self.size = self.__C.size
- elif asCovObject is not None:
+ elif __Object is not None:
self.__is_object = True
- self.__C = asCovObject
+ self.__C = __Object
for at in ("getT","getI","diag","trace","__add__","__sub__","__neg__","__mul__","__rmul__"):
if not hasattr(self.__C,at):
raise ValueError("The matrix given for %s as an object has no attribute \"%s\". Please check your object input."%(self.__name,at))
self.__validate()
def __validate(self):
+ "Validation"
+ if self.__C is None:
+ raise UnboundLocalError("%s covariance matrix value has not been set!"%(self.__name,))
if self.ismatrix() and min(self.shape) != max(self.shape):
raise ValueError("The given matrix for %s is not a square one, its shape is %s. Please check your matrix input."%(self.__name,self.shape))
if self.isobject() and min(self.shape) != max(self.shape):
raise ValueError("The \"%s\" covariance matrix is not positive-definite. Please check your scalar input %s."%(self.__name,self.__C))
if self.isvector() and (self.__C <= 0).any():
raise ValueError("The \"%s\" covariance matrix is not positive-definite. Please check your vector input."%(self.__name,))
- if self.ismatrix() and logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
+ if self.ismatrix() and (self.__check or logging.getLogger().level < logging.WARNING):
try:
L = numpy.linalg.cholesky( self.__C )
except:
raise ValueError("The %s covariance matrix is not symmetric positive-definite. Please check your matrix input."%(self.__name,))
+ if self.isobject() and (self.__check or logging.getLogger().level < logging.WARNING):
+ try:
+ L = self.__C.cholesky()
+ except:
+ raise ValueError("The %s covariance object is not symmetric positive-definite. Please check your matrix input."%(self.__name,))
def isscalar(self):
+ "Vérification du type interne"
return self.__is_scalar
def isvector(self):
+ "Vérification du type interne"
return self.__is_vector
def ismatrix(self):
+ "Vérification du type interne"
return self.__is_matrix
def isobject(self):
+ "Vérification du type interne"
return self.__is_object
def getI(self):
+ "Inversion"
if self.ismatrix():
return Covariance(self.__name+"I", asCovariance = self.__C.I )
elif self.isvector():
return None # Indispensable
def getT(self):
+ "Transposition"
if self.ismatrix():
return Covariance(self.__name+"T", asCovariance = self.__C.T )
elif self.isvector():
return Covariance(self.__name+"T", asCovObject = self.__C.getT() )
def cholesky(self):
+ "Décomposition de Cholesky"
if self.ismatrix():
return Covariance(self.__name+"C", asCovariance = numpy.linalg.cholesky(self.__C) )
elif self.isvector():
return Covariance(self.__name+"C", asCovObject = self.__C.cholesky() )
def choleskyI(self):
+ "Inversion de la décomposition de Cholesky"
if self.ismatrix():
return Covariance(self.__name+"H", asCovariance = numpy.linalg.cholesky(self.__C).I )
elif self.isvector():
return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() )
def diag(self, msize=None):
+ "Diagonale de la matrice"
if self.ismatrix():
return numpy.diag(self.__C)
elif self.isvector():
return self.__C.diag()
def asfullmatrix(self, msize=None):
+ "Matrice pleine"
if self.ismatrix():
return self.__C
elif self.isvector():
return self.__C.asfullmatrix()
def trace(self, msize=None):
+ "Trace de la matrice"
if self.ismatrix():
return numpy.trace(self.__C)
elif self.isvector():
elif self.isobject():
return self.__C.trace()
+ def getO(self):
+ return self
+
def __repr__(self):
+ "x.__repr__() <==> repr(x)"
return repr(self.__C)
def __str__(self):
+ "x.__str__() <==> str(x)"
return str(self.__C)
def __add__(self, other):
+ "x.__add__(y) <==> x+y"
if self.ismatrix() or self.isobject():
return self.__C + numpy.asmatrix(other)
elif self.isvector() or self.isscalar():
return numpy.asmatrix(_A)
def __radd__(self, other):
+ "x.__radd__(y) <==> y+x"
raise NotImplementedError("%s covariance matrix __radd__ method not available for %s type!"%(self.__name,type(other)))
def __sub__(self, other):
+ "x.__sub__(y) <==> x-y"
if self.ismatrix() or self.isobject():
return self.__C - numpy.asmatrix(other)
elif self.isvector() or self.isscalar():
return numpy.asmatrix(_A)
def __rsub__(self, other):
+ "x.__rsub__(y) <==> y-x"
raise NotImplementedError("%s covariance matrix __rsub__ method not available for %s type!"%(self.__name,type(other)))
def __neg__(self):
+ "x.__neg__() <==> -x"
return - self.__C
def __mul__(self, other):
+ "x.__mul__(y) <==> x*y"
if self.ismatrix() and isinstance(other,numpy.matrix):
return self.__C * other
- elif self.ismatrix() and (isinstance(other,numpy.ndarray) \
- or isinstance(other,list) \
- or isinstance(other,tuple)):
+ elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)):
if numpy.ravel(other).size == self.shape[1]: # Vecteur
return self.__C * numpy.asmatrix(numpy.ravel(other)).T
elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice
return self.__C * numpy.asmatrix(other)
else:
raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.asmatrix(other).shape,self.__name))
- elif self.isvector() and (isinstance(other,numpy.matrix) \
- or isinstance(other,numpy.ndarray) \
- or isinstance(other,list) \
- or isinstance(other,tuple)):
+ elif self.isvector() and isinstance(other, (list, numpy.matrix, numpy.ndarray, tuple)):
if numpy.ravel(other).size == self.shape[1]: # Vecteur
return numpy.asmatrix(self.__C * numpy.ravel(other)).T
elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice
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:
raise NotImplementedError("%s covariance matrix __mul__ method not available for %s type!"%(self.__name,type(other)))
def __rmul__(self, other):
+ "x.__rmul__(y) <==> y*x"
if self.ismatrix() and isinstance(other,numpy.matrix):
return other * self.__C
elif self.isvector() and isinstance(other,numpy.matrix):
raise NotImplementedError("%s covariance matrix __rmul__ method not available for %s type!"%(self.__name,type(other)))
def __len__(self):
+ "x.__len__() <==> len(x)"
return self.shape[0]
+# ==============================================================================
+class ObserverF(object):
+ """
+ Creation d'une fonction d'observateur a partir de son texte
+ """
+ def __init__(self, corps=""):
+ self.__corps = corps
+ def func(self,var,info):
+ "Fonction d'observation"
+ exec(self.__corps)
+ def getfunc(self):
+ "Restitution du pointeur de fonction dans l'objet"
+ return self.func
+
+# ==============================================================================
+class CaseLogger(object):
+ """
+ Conservation des commandes de creation d'un cas
+ """
+ def __init__(self, __name="", __objname="case", __addViewers=None, __addLoaders=None):
+ self.__name = str(__name)
+ self.__objname = str(__objname)
+ self.__logSerie = []
+ self.__switchoff = False
+ self.__viewers = {
+ "TUI":Interfaces._TUIViewer,
+ "DCT":Interfaces._DCTViewer,
+ "SCD":Interfaces._SCDViewer,
+ "YACS":Interfaces._YACSViewer,
+ }
+ self.__loaders = {
+ "TUI":Interfaces._TUIViewer,
+ "EPD":Interfaces._EPDViewer,
+ "DCT":Interfaces._DCTViewer,
+ }
+ if __addViewers is not None:
+ self.__viewers.update(dict(__addViewers))
+ if __addLoaders is not None:
+ self.__loaders.update(dict(__addLoaders))
+
+ def register(self, __command=None, __keys=None, __local=None, __pre=None, __switchoff=False):
+ "Enregistrement d'une commande individuelle"
+ if __command is not None and __keys is not None and __local is not None and not self.__switchoff:
+ if "self" in __keys: __keys.remove("self")
+ self.__logSerie.append( (str(__command), __keys, __local, __pre, __switchoff) )
+ if __switchoff:
+ self.__switchoff = True
+ if not __switchoff:
+ self.__switchoff = False
+
+ def dump(self, __filename=None, __format="TUI", __upa=""):
+ "Restitution normalisée des commandes"
+ if __format in self.__viewers:
+ __formater = self.__viewers[__format](self.__name, self.__objname, self.__logSerie)
+ else:
+ raise ValueError("Dumping as \"%s\" is not available"%__format)
+ return __formater.dump(__filename, __upa)
+
+ def load(self, __filename=None, __content=None, __object=None, __format="TUI"):
+ "Chargement normalisé des commandes"
+ if __format in self.__loaders:
+ __formater = self.__loaders[__format]()
+ else:
+ raise ValueError("Loading as \"%s\" is not available"%__format)
+ return __formater.load(__filename, __content, __object)
+
+# ==============================================================================
+def CostFunction3D(_x,
+ _Hm = None, # Pour simuler Hm(x) : HO["Direct"].appliedTo
+ _HmX = None, # Simulation déjà faite de Hm(x)
+ _arg = None, # Arguments supplementaires pour Hm, sous la forme d'un tuple
+ _BI = None,
+ _RI = None,
+ _Xb = None,
+ _Y = None,
+ _SIV = False, # A résorber pour la 8.0
+ _SSC = [], # self._parameters["StoreSupplementaryCalculations"]
+ _nPS = 0, # nbPreviousSteps
+ _QM = "DA", # QualityMeasure
+ _SSV = {}, # Entrée et/ou sortie : self.StoredVariables
+ _fRt = False, # Restitue ou pas la sortie étendue
+ _sSc = True, # Stocke ou pas les SSC
+ ):
+ """
+ Fonction-coût générale utile pour les algorithmes statiques/3D : 3DVAR, BLUE
+ et dérivés, Kalman et dérivés, LeastSquares, SamplingTest, PSO, SA, Tabu,
+ DFO, QuantileRegression
+ """
+ if not _sSc:
+ _SIV = False
+ _SSC = {}
+ else:
+ for k in ["CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CurrentOptimum",
+ "CurrentState",
+ "IndexOfOptimum",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ ]:
+ if k not in _SSV:
+ _SSV[k] = []
+ if hasattr(_SSV[k],"store"):
+ _SSV[k].append = _SSV[k].store # Pour utiliser "append" au lieu de "store"
+ #
+ _X = numpy.asmatrix(numpy.ravel( _x )).T
+ if _SIV or "CurrentState" in _SSC or "CurrentOptimum" in _SSC:
+ _SSV["CurrentState"].append( _X )
+ #
+ if _HmX is not None:
+ _HX = _HmX
+ else:
+ if _Hm is None:
+ raise ValueError("COSTFUNCTION3D Operator has to be defined.")
+ if _arg is None:
+ _HX = _Hm( _X )
+ else:
+ _HX = _Hm( _X, *_arg )
+ _HX = numpy.asmatrix(numpy.ravel( _HX )).T
+ #
+ if "SimulatedObservationAtCurrentState" in _SSC or \
+ "SimulatedObservationAtCurrentOptimum" in _SSC:
+ _SSV["SimulatedObservationAtCurrentState"].append( _HX )
+ #
+ if numpy.any(numpy.isnan(_HX)):
+ Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan
+ else:
+ _Y = numpy.asmatrix(numpy.ravel( _Y )).T
+ if _QM in ["AugmentedWeightedLeastSquares", "AWLS", "AugmentedPonderatedLeastSquares", "APLS", "DA"]:
+ if _BI is None or _RI is None:
+ raise ValueError("Background and Observation error covariance matrix has to be properly defined!")
+ _Xb = numpy.asmatrix(numpy.ravel( _Xb )).T
+ Jb = 0.5 * (_X - _Xb).T * _BI * (_X - _Xb)
+ Jo = 0.5 * (_Y - _HX).T * _RI * (_Y - _HX)
+ elif _QM in ["WeightedLeastSquares", "WLS", "PonderatedLeastSquares", "PLS"]:
+ if _RI is None:
+ raise ValueError("Observation error covariance matrix has to be properly defined!")
+ Jb = 0.
+ Jo = 0.5 * (_Y - _HX).T * _RI * (_Y - _HX)
+ elif _QM in ["LeastSquares", "LS", "L2"]:
+ Jb = 0.
+ Jo = 0.5 * (_Y - _HX).T * (_Y - _HX)
+ elif _QM in ["AbsoluteValue", "L1"]:
+ Jb = 0.
+ Jo = numpy.sum( numpy.abs(_Y - _HX) )
+ elif _QM in ["MaximumError", "ME"]:
+ Jb = 0.
+ Jo = numpy.max( numpy.abs(_Y - _HX) )
+ elif _QM in ["QR", "Null"]:
+ Jb = 0.
+ Jo = 0.
+ else:
+ raise ValueError("Unknown asked quality measure!")
+ #
+ J = float( Jb ) + float( Jo )
+ #
+ if _sSc:
+ _SSV["CostFunctionJb"].append( Jb )
+ _SSV["CostFunctionJo"].append( Jo )
+ _SSV["CostFunctionJ" ].append( J )
+ #
+ if "IndexOfOptimum" in _SSC or \
+ "CurrentOptimum" in _SSC or \
+ "SimulatedObservationAtCurrentOptimum" in _SSC:
+ IndexMin = numpy.argmin( _SSV["CostFunctionJ"][_nPS:] ) + _nPS
+ if "IndexOfOptimum" in _SSC:
+ _SSV["IndexOfOptimum"].append( IndexMin )
+ if "CurrentOptimum" in _SSC:
+ _SSV["CurrentOptimum"].append( _SSV["CurrentState"][IndexMin] )
+ if "SimulatedObservationAtCurrentOptimum" in _SSC:
+ _SSV["SimulatedObservationAtCurrentOptimum"].append( _SSV["SimulatedObservationAtCurrentState"][IndexMin] )
+ #
+ if _fRt:
+ return _SSV
+ else:
+ if _QM in ["QR"]: # Pour le QuantileRegression
+ return _HX
+ else:
+ return J
+
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC \n')
