-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2015 EDF R&D
+# Copyright (C) 2008-2022 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.
-
- Ce module est destiné à etre appelée par AssimilationStudy pour constituer
- les objets élémentaires de l'algorithme.
+ 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:
+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 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):
- self.__listOPCV = [] # Operator Previous Calculated Points, Results, Point Norms
- # logging.debug("CM Tolerance de determination des doublons : %.2e"%self.__tolerBP)
+ "Vide le cache"
+ self.__listOPCV = []
+ self.__seenNames = []
- def wasCalculatedIn(self, xValue ): #, info="" ):
+ def wasCalculatedIn(self, xValue, oName="" ):
+ "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'):
+ pass
+ elif (str(oName) != self.__listOPCV[i][3]):
+ pass
+ elif (xValue.size != self.__listOPCV[i][0].size):
+ pass
+ elif (numpy.ravel(xValue)[0] - self.__listOPCV[i][0][0]) > (self.__tolerBP * self.__listOPCV[i][2] / self.__listOPCV[i][0].size):
+ 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]
+ break
return __alc, __HxV
- def storeValueInX(self, xValue, HxValue ):
+ 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.__lenghtOR = 2 * min(xValue.size, 50) + 2 # 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 * min(xValue.size, 50) + 2 # 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)
self.__listOPCV.pop(0)
self.__listOPCV.append( (
- copy.copy(numpy.ravel(xValue)),
- copy.copy(HxValue),
- numpy.linalg.norm(xValue),
+ copy.copy(numpy.ravel(xValue)), # 0 Previous point
+ copy.copy(HxValue), # 1 Previous value
+ numpy.linalg.norm(xValue), # 2 Norm
+ str(oName), # 3 Operator name
) )
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:
+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,
+ reducingMemoryUse = False,
+ 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.array/matrix
+ - avoidingRedundancy : booléen évitant (ou pas) les calculs redondants
+ - reducingMemoryUse : booléen forçant (ou pas) des calculs moins
+ gourmands en mémoire
+ - 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.__reduceM = bool( reducingMemoryUse )
+ 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:
self.__Method = None
- self.__Matrix = numpy.matrix( fromMatrix, numpy.float )
+ if isinstance(fromMatrix, str):
+ fromMatrix = PlatformInfo.strmatrix2liststr( fromMatrix )
+ self.__Matrix = numpy.asarray( fromMatrix, dtype=float )
self.__Type = "Matrix"
else:
self.__Method = None
self.__Type = None
def disableAvoidingRedundancy(self):
+ "Inactive le cache"
Operator.CM.disable()
def enableAvoidingRedundancy(self):
- if self.__AvoidRC:
+ "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, 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 self.__AvoidRC:
- __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xValue)
+ if argsAsSerie:
+ _xValue = xValue
+ _HValue = HValue
else:
- __alreadyCalculated = False
+ _xValue = (xValue,)
+ if HValue is not None:
+ _HValue = (HValue,)
+ else:
+ _HValue = HValue
+ PlatformInfo.isIterable( _xValue, True, " in Operator.appliedTo" )
#
- if __alreadyCalculated:
- self.__addOneCacheCall()
- HxValue = __HxV
+ 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( _HValue[i] )
+ if self.__avoidRC:
+ Operator.CM.storeValueInX(_xValue[i],_HxValue[-1],self.__name)
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)
+ _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()
+ _hv = self.__Matrix @ numpy.ravel(xv)
+ else:
+ self.__addOneMethodCall()
+ _xserie.append( xv )
+ _hindex.append( i )
+ _hv = None
+ _HxValue.append( _hv )
+ #
+ if len(_xserie)>0 and self.__Matrix is None:
+ if self.__extraArgs is None:
+ _hserie = self.__Method( _xserie ) # Calcul MF
+ else:
+ _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)
#
- return HxValue
+ if returnSerieAsArrayMatrix:
+ _HxValue = numpy.stack([numpy.ravel(_hv) for _hv in _HxValue], axis=1)
+ #
+ 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
+ self.__addOneMatrixCall()
+ _HxValue.append( self.__Matrix @ numpy.ravel(_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
+ self.__addOneMatrixCall()
+ _HxValue.append( self.__Matrix @ numpy.ravel(_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( 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:
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):
- self.__NbCallsAsMethod += 1 # Decompte local
- Operator.NbCallsAsMethod += 1 # Decompte global
+ def __addOneMethodCall(self, nb = 1):
+ "Comptabilise un appel"
+ 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 Algorithm:
+class FullOperator(object):
"""
- Classe générale d'interface de type algorithme
+ 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,
+ performancePrf = None,
+ 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"]
+ # Le défaut est équivalent à "ReducedOverallRequirements"
+ __reduceM, __avoidRC = True, True
+ if performancePrf is not None:
+ if performancePrf == "ReducedAmountOfCalculation":
+ __reduceM, __avoidRC = False, True
+ elif performancePrf == "ReducedMemoryFootprint":
+ __reduceM, __avoidRC = True, False
+ elif performancePrf == "NoSavings":
+ __reduceM, __avoidRC = False, False
+ #
+ 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 "withReducingMemoryUse" not in __Function: __Function["withReducingMemoryUse"] = __reduceM
+ 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"],
+ extraArguments = self.__extraArgs,
+ reducingMemoryUse = __Function["withReducingMemoryUse"],
+ 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, reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
+ self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = FDA.TangentOperator, reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
+ self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = FDA.AdjointOperator, reducingMemoryUse = __reduceM, 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"], reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
+ self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = __Function["Tangent"], reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
+ self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = __Function["Adjoint"], reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
+ elif asMatrix is not None:
+ if isinstance(__Matrix, str):
+ __Matrix = PlatformInfo.strmatrix2liststr( __Matrix )
+ __matrice = numpy.asarray( __Matrix, dtype=float )
+ self.__FO["Direct"] = Operator( name = self.__name, fromMatrix = __matrice, reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
+ self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMatrix = __matrice, reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF )
+ self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMatrix = __matrice.T, reducingMemoryUse = __reduceM, 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
- Elle donne un cadre pour l'écriture d'une classe élémentaire d'algorithme
+ 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
+
+ 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
- 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))
+ - ForecastCovariance : covariance de l'état prédit courant lors d'itérations
+ - ForecastState : état prédit courant lors d'itérations
+ - Residu : dans le cas des algorithmes de vérification
+ - SampledStateForQuantiles : échantillons d'états pour l'estimation des quantiles
+ - 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()
#
self._name = str( name )
self._parameters = {"StoreSupplementaryCalculations":[]}
+ self.__internal_state = {}
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["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["CurrentEnsembleState"] = Persistence.OneMatrix(name = "CurrentEnsembleState")
+ self.StoredVariables["CurrentIterationNumber"] = Persistence.OneIndex(name = "CurrentIterationNumber")
+ self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum")
+ self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState")
+ self.StoredVariables["ForecastCovariance"] = Persistence.OneMatrix(name = "ForecastCovariance")
+ 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["Residu"] = Persistence.OneScalar(name = "Residu")
+ self.StoredVariables["SampledStateForQuantiles"] = Persistence.OneMatrix(name = "SampledStateForQuantiles")
+ 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")
-
- 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")))
+ #
+ 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, 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 %.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:
+ if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
+ logging.debug("%s %s vector %s is required and set, and its size is %i."%(self._name,argname,symbol,numpy.array(argument).size))
+ elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
+ logging.debug("%s %s vector %s is optional and set, and its size is %i."%(self._name,argname,symbol,numpy.array(argument).size))
+ else:
+ logging.debug("%s %s vector %s is set although neither required nor optional, 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:
+ if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
+ logging.debug("%s %s error covariance matrix %s is required and set."%(self._name,argname,symbol))
+ elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
+ logging.debug("%s %s error covariance matrix %s is optional and set."%(self._name,argname,symbol))
+ else:
+ logging.debug("%s %s error covariance matrix %s is set although neither required nor optional."%(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:
+ if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
+ logging.debug("%s %s operator %s is required and set."%(self._name,argname,symbol))
+ elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
+ logging.debug("%s %s operator %s is optional and set."%(self._name,argname,symbol))
+ else:
+ logging.debug("%s %s operator %s is set although neither required nor optional."%(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" )
+ #
+ # Corrections et compléments des bornes
+ if ("Bounds" in self._parameters) and isinstance(self._parameters["Bounds"], (list, tuple)) and (len(self._parameters["Bounds"]) > 0):
+ logging.debug("%s Bounds taken into account"%(self._name,))
+ else:
+ self._parameters["Bounds"] = None
+ if ("StateBoundsForQuantiles" in self._parameters) and isinstance(self._parameters["StateBoundsForQuantiles"], (list, tuple)) and (len(self._parameters["StateBoundsForQuantiles"]) > 0):
+ logging.debug("%s Bounds for quantiles states taken into account"%(self._name,))
+ # Attention : contrairement à Bounds, pas de défaut à None, sinon on ne peut pas être sans bornes
+ #
+ # Corrections et compléments de l'initialisation en X
+ if "InitializationPoint" in self._parameters:
+ if Xb is not None:
+ if self._parameters["InitializationPoint"] is not None and hasattr(self._parameters["InitializationPoint"],'size'):
+ if self._parameters["InitializationPoint"].size != numpy.ravel(Xb).size:
+ raise ValueError("Incompatible size %i of forced initial point that have to replace the background of size %i" \
+ %(self._parameters["InitializationPoint"].size,numpy.ravel(Xb).size))
+ # Obtenu par typecast : numpy.ravel(self._parameters["InitializationPoint"])
+ else:
+ self._parameters["InitializationPoint"] = numpy.ravel(Xb)
+ else:
+ if self._parameters["InitializationPoint"] is None:
+ raise ValueError("Forced initial point can not be set without any given Background or required value")
+ #
+ # Correction pour pallier a un bug de TNC sur le retour du Minimum
+ if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
+ self.setParameterValue("StoreInternalVariables",True)
+ #
+ # Verbosité et logging
+ 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):
+ "Post-calcul"
if ("StoreSupplementaryCalculations" in self._parameters) and \
"APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
for _A in self.StoredVariables["APosterioriCovariance"]:
_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):
- """
- Vérifie si l'une des variables stockées est identifiée par la clé.
- """
- return (key in self.StoredVariables)
+ "D.__contains__(k) -> True if D has a key k, else False"
+ 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):
- """
- 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") 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.")
"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
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:
- if typecast is list or typecast is tuple or type(__val) is list or type(__val) is tuple:
+ 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)
- logging.debug("%s %s : %s"%(self._name, self.__required_parameters[k]["message"], self._parameters[k]))
+ else:
+ pass
+ if hasattr(self._parameters[k],"__len__") and len(self._parameters[k]) > 100:
+ logging.debug("%s %s de longueur %s", self._name, self.__required_parameters[k]["message"], len(self._parameters[k]))
+ else:
+ logging.debug("%s %s : %s", self._name, self.__required_parameters[k]["message"], self._parameters[k])
+
+ def _setInternalState(self, key=None, value=None, fromDico={}, reset=False):
+ """
+ Permet de stocker des variables nommées constituant l'état interne
+ """
+ if reset: # Vide le dictionnaire préalablement
+ self.__internal_state = {}
+ if key is not None and value is not None:
+ self.__internal_state[key] = value
+ self.__internal_state.update( dict(fromDico) )
+
+ def _getInternalState(self, key=None):
+ """
+ Restitue un état interne sous la forme d'un dictionnaire de variables nommées
+ """
+ if key is not None and key in self.__internal_state:
+ return self.__internal_state[key]
+ else:
+ return self.__internal_state
+
+ 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 PartialAlgorithm(object):
+ """
+ Classe pour mimer "Algorithm" du point de vue stockage, mais sans aucune
+ action avancée comme la vérification . Pour les méthodes reprises ici,
+ le fonctionnement est identique à celles de la classe "Algorithm".
+ """
+ def __init__(self, name):
+ self._name = str( name )
+ self._parameters = {"StoreSupplementaryCalculations":[]}
+ #
+ self.StoredVariables = {}
+ self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
+ self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ")
+ self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
+ self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
+ self.StoredVariables["CurrentIterationNumber"] = Persistence.OneIndex(name = "CurrentIterationNumber")
+ #
+ self.__canonical_stored_name = {}
+ for k in self.StoredVariables:
+ self.__canonical_stored_name[k.lower()] = k
+
+ 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
+ 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
+ des classes de persistance.
+ """
+ if key is not None:
+ return self.StoredVariables[self.__canonical_stored_name[key.lower()]]
+ else:
+ return self.StoredVariables
+
+# ==============================================================================
+class AlgorithmAndParameters(object):
+ """
+ Classe générale d'interface d'action pour l'algorithme et ses paramètres
+ """
+ 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 __str__(self):
+ "x.__str__() <==> str(x)"
+ return str(self.__A)+", "+str(self.__P)
+
+ def __setAlgorithm(self, choice = None ):
+ """
+ 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.
+ """
+ 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")
+ for member in asPersistentVector:
+ self.__Xb.store( numpy.asarray(member, dtype=float) )
+ __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)))
+ #
+ if ("StateBoundsForQuantiles" in self.__P) \
+ and (isinstance(self.__P["StateBoundsForQuantiles"], list) or isinstance(self.__P["StateBoundsForQuantiles"], tuple)) \
+ and (len(self.__P["StateBoundsForQuantiles"]) != max(__Xb_shape)):
+ raise ValueError("The number \"%s\" of bound pairs for the quantile state (X) components is different of the size \"%s\" of the state itself." \
+ %(len(self.__P["StateBoundsForQuantiles"]),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 Diagnostic:
+class DataObserver(object):
"""
- Classe générale d'interface de type diagnostic
+ 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)
- 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.
+ 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)
- 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).
+# ==============================================================================
+class UserScript(object):
"""
- def __init__(self, name = "", parameters = {}):
- self.name = str(name)
- self.parameters = dict( parameters )
+ 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 _formula(self, *args):
+ 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,
+ ):
"""
- Doit implémenter l'opération élémentaire de diagnostic sous sa forme
- mathématique la plus naturelle possible.
"""
- raise NotImplementedError("Diagnostic mathematical formula has not been implemented!")
+ 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 calculate(self, *args):
+ 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,
+ ):
"""
- Active la formule de calcul avec les arguments correctement rangés
+ 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".
"""
- raise NotImplementedError("Diagnostic activation method has not been implemented!")
+ 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
+ if isinstance(__Vector, str):
+ __Vector = PlatformInfo.strvect2liststr( __Vector )
+ self.__V = numpy.ravel(numpy.asarray( __Vector, dtype=float )).reshape((-1,1))
+ 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)
+ self.__V = Persistence.OneVector(self.__name)
+ if isinstance(__Series, str):
+ __Series = PlatformInfo.strmatrix2liststr(__Series)
+ for member in __Series:
+ if isinstance(member, str):
+ member = PlatformInfo.strvect2liststr( member )
+ self.__V.store(numpy.asarray( member, dtype=float ))
+ 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:
+class Covariance(object):
"""
- Classe générale d'interface de type covariance
+ Classe générale d'interface de type covariance
"""
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 = "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 = 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 isinstance(__Scalar, str):
+ __Scalar = PlatformInfo.strvect2liststr( __Scalar )
+ if len(__Scalar) > 0: __Scalar = __Scalar[0]
+ if numpy.array(__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.array(__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:
+ if isinstance(__Vector, str):
+ __Vector = PlatformInfo.strvect2liststr( __Vector )
self.__is_vector = True
- self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(asEyeByVector, float ) ) ) )
+ self.__C = numpy.abs( numpy.ravel(numpy.asarray( __Vector, dtype=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
- for at in ("getT","getI","diag","trace","__add__","__sub__","__neg__","__mul__","__rmul__"):
+ self.__C = __Object
+ for at in ("getT","getI","diag","trace","__add__","__sub__","__neg__","__matmul__","__mul__","__rmatmul__","__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))
if hasattr(self.__C,"shape"):
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 )
+ return Covariance(self.__name+"I", asCovariance = numpy.linalg.inv(self.__C) )
elif self.isvector():
return Covariance(self.__name+"I", asEyeByVector = 1. / self.__C )
elif self.isscalar():
return Covariance(self.__name+"I", asEyeByScalar = 1. / self.__C )
- elif self.isobject():
+ elif self.isobject() and hasattr(self.__C,"getI"):
return Covariance(self.__name+"I", asCovObject = self.__C.getI() )
else:
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", asEyeByVector = self.__C )
elif self.isscalar():
return Covariance(self.__name+"T", asEyeByScalar = self.__C )
- elif self.isobject():
+ elif self.isobject() and hasattr(self.__C,"getT"):
return Covariance(self.__name+"T", asCovObject = self.__C.getT() )
+ else:
+ raise AttributeError("the %s covariance matrix has no getT attribute."%(self.__name,))
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", asEyeByScalar = numpy.sqrt( self.__C ) )
elif self.isobject() and hasattr(self.__C,"cholesky"):
return Covariance(self.__name+"C", asCovObject = self.__C.cholesky() )
+ else:
+ raise AttributeError("the %s covariance matrix has no cholesky attribute."%(self.__name,))
def choleskyI(self):
+ "Inversion de la décomposition de Cholesky"
if self.ismatrix():
- return Covariance(self.__name+"H", asCovariance = numpy.linalg.cholesky(self.__C).I )
+ return Covariance(self.__name+"H", asCovariance = numpy.linalg.inv(numpy.linalg.cholesky(self.__C)) )
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,"choleskyI"):
return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() )
+ else:
+ raise AttributeError("the %s covariance matrix has no choleskyI attribute."%(self.__name,))
+
+ def sqrtm(self):
+ "Racine carrée matricielle"
+ if self.ismatrix():
+ import scipy
+ return Covariance(self.__name+"C", asCovariance = numpy.real(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,"sqrtm"):
+ return Covariance(self.__name+"C", asCovObject = self.__C.sqrtm() )
+ else:
+ raise AttributeError("the %s covariance matrix has no sqrtm attribute."%(self.__name,))
+
+ def sqrtmI(self):
+ "Inversion de la racine carrée matricielle"
+ if self.ismatrix():
+ import scipy
+ return Covariance(self.__name+"H", asCovariance = numpy.linalg.inv(numpy.real(scipy.linalg.sqrtm(self.__C))) )
+ 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,"sqrtmI"):
+ return Covariance(self.__name+"H", asCovObject = self.__C.sqrtmI() )
+ else:
+ raise AttributeError("the %s covariance matrix has no sqrtmI attribute."%(self.__name,))
def diag(self, msize=None):
+ "Diagonale de la matrice"
if self.ismatrix():
return numpy.diag(self.__C)
elif self.isvector():
raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,))
else:
return self.__C * numpy.ones(int(msize))
- elif self.isobject():
+ elif self.isobject() and hasattr(self.__C,"diag"):
return self.__C.diag()
+ else:
+ raise AttributeError("the %s covariance matrix has no diag attribute."%(self.__name,))
- def asfullmatrix(self, msize=None):
+ def trace(self, msize=None):
+ "Trace de la matrice"
if self.ismatrix():
- return self.__C
+ return numpy.trace(self.__C)
elif self.isvector():
- return numpy.matrix( numpy.diag(self.__C), float )
+ return float(numpy.sum(self.__C))
elif self.isscalar():
if msize is None:
raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,))
else:
- return numpy.matrix( self.__C * numpy.eye(int(msize)), float )
- elif self.isobject() and hasattr(self.__C,"asfullmatrix"):
- return self.__C.asfullmatrix()
+ return self.__C * int(msize)
+ elif self.isobject():
+ return self.__C.trace()
+ else:
+ raise AttributeError("the %s covariance matrix has no trace attribute."%(self.__name,))
- def trace(self, msize=None):
+ def asfullmatrix(self, msize=None):
+ "Matrice pleine"
if self.ismatrix():
- return numpy.trace(self.__C)
+ return numpy.asarray(self.__C, dtype=float)
elif self.isvector():
- return float(numpy.sum(self.__C))
+ return numpy.asarray( numpy.diag(self.__C), dtype=float )
elif self.isscalar():
if msize is None:
raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,))
else:
- return self.__C * int(msize)
- elif self.isobject():
- return self.__C.trace()
+ return numpy.asarray( self.__C * numpy.eye(int(msize)), dtype=float )
+ elif self.isobject() and hasattr(self.__C,"asfullmatrix"):
+ return self.__C.asfullmatrix()
+ else:
+ raise AttributeError("the %s covariance matrix has no asfullmatrix attribute."%(self.__name,))
+
+ def assparsematrix(self):
+ "Valeur sparse"
+ return self.__C
+
+ 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 __matmul__(self, other):
+ "x.__mul__(y) <==> x@y"
+ if self.ismatrix() and isinstance(other, (int, float)):
+ return numpy.asarray(self.__C) * other
+ elif self.ismatrix() and isinstance(other, (list, numpy.matrix, numpy.ndarray, tuple)):
+ if numpy.ravel(other).size == self.shape[1]: # Vecteur
+ return numpy.ravel(self.__C @ numpy.ravel(other))
+ elif numpy.asarray(other).shape[0] == self.shape[1]: # Matrice
+ return numpy.asarray(self.__C) @ numpy.asarray(other)
+ else:
+ raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.asarray(other).shape,self.__name))
+ elif self.isvector() and isinstance(other, (list, numpy.matrix, numpy.ndarray, tuple)):
+ if numpy.ravel(other).size == self.shape[1]: # Vecteur
+ return numpy.ravel(self.__C) * numpy.ravel(other)
+ elif numpy.asarray(other).shape[0] == self.shape[1]: # Matrice
+ return numpy.ravel(self.__C).reshape((-1,1)) * numpy.asarray(other)
+ else:
+ 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 numpy.asarray(self.__C * other)
+ 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.ravel(other)
+ else:
+ return self.__C * numpy.asarray(other)
+ elif self.isobject():
+ return self.__C.__matmul__(other)
+ else:
+ raise NotImplementedError("%s covariance matrix __matmul__ method not available for %s type!"%(self.__name,type(other)))
+
def __mul__(self, other):
- if self.ismatrix() and isinstance(other,numpy.matrix):
+ "x.__mul__(y) <==> x*y"
+ 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
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:
else:
raise NotImplementedError("%s covariance matrix __mul__ method not available for %s type!"%(self.__name,type(other)))
+ def __rmatmul__(self, other):
+ "x.__rmul__(y) <==> y@x"
+ 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"%(numpy.ravel(other).shape,self.shape,self.__name))
+ elif self.isscalar() and isinstance(other,numpy.matrix):
+ return other * self.__C
+ elif self.isobject():
+ return self.__C.__rmatmul__(other)
+ else:
+ raise NotImplementedError("%s covariance matrix __rmatmul__ method not available for %s type!"%(self.__name,type(other)))
+
def __rmul__(self, other):
- if self.ismatrix() and isinstance(other,numpy.matrix):
+ "x.__rmul__(y) <==> y*x"
+ 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.isscalar() and isinstance(other,float):
+ return other * self.__C
elif self.isobject():
return self.__C.__rmul__(other)
else:
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]
# ==============================================================================
-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,
+class Observer2Func(object):
+ """
+ Création 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 création 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,
+ "SimpleReportInRst":Interfaces._SimpleReportInRstViewer,
+ "SimpleReportInHtml":Interfaces._SimpleReportInHtmlViewer,
+ "SimpleReportInPlainTxt":Interfaces._SimpleReportInPlainTxtViewer,
+ }
+ 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 = __xserie
+ # 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)
+ _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:
"CostFunctionJb",
"CostFunctionJo",
"CurrentOptimum",
- "CurrentState",
+ "CurrentState",
"IndexOfOptimum",
"SimulatedObservationAtCurrentOptimum",
"SimulatedObservationAtCurrentState",
- ]:
+ ]:
if k not in _SSV:
_SSV[k] = []
if hasattr(_SSV[k],"store"):
_HX = _HmX
else:
if _Hm is None:
- raise ValueError("%s Operator has to be defined."%(self.__name,))
+ raise ValueError("COSTFUNCTION3D Operator has to be defined.")
if _arg is None:
_HX = _Hm( _X )
else:
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')
