1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2020 EDF R&D
5 # This library is free software; you can redistribute it and/or
6 # modify it under the terms of the GNU Lesser General Public
7 # License as published by the Free Software Foundation; either
8 # version 2.1 of the License.
10 # This library is distributed in the hope that it will be useful,
11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
13 # Lesser General Public License for more details.
15 # You should have received a copy of the GNU Lesser General Public
16 # License along with this library; if not, write to the Free Software
17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 Définit les outils généraux élémentaires.
26 __author__ = "Jean-Philippe ARGAUD"
34 from functools import partial
35 from daCore import Persistence, PlatformInfo, Interfaces
36 from daCore import Templates
38 # ==============================================================================
39 class CacheManager(object):
41 Classe générale de gestion d'un cache de calculs
44 toleranceInRedundancy = 1.e-18,
45 lenghtOfRedundancy = -1,
48 Les caractéristiques de tolérance peuvent être modifiées à la création.
50 self.__tolerBP = float(toleranceInRedundancy)
51 self.__lenghtOR = int(lenghtOfRedundancy)
52 self.__initlnOR = self.__lenghtOR
59 self.__listOPCV = [] # Previous Calculated Points, Results, Point Norms, Operator
61 # logging.debug("CM Tolerance de determination des doublons : %.2e", self.__tolerBP)
63 def wasCalculatedIn(self, xValue, oName="" ): #, info="" ):
64 "Vérifie l'existence d'un calcul correspondant à la valeur"
68 for i in range(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1):
69 if not hasattr(xValue, 'size') or (str(oName) != self.__listOPCV[i][3]) or (xValue.size != self.__listOPCV[i][0].size):
70 # 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)
72 elif numpy.linalg.norm(numpy.ravel(xValue) - self.__listOPCV[i][0]) < self.__tolerBP * self.__listOPCV[i][2]:
74 __HxV = self.__listOPCV[i][1]
75 # logging.debug("CM Cas%s déja calculé, portant le numéro %i", info, i)
79 def storeValueInX(self, xValue, HxValue, oName="" ):
80 "Stocke pour un opérateur o un calcul Hx correspondant à la valeur x"
81 if self.__lenghtOR < 0:
82 self.__lenghtOR = 2 * xValue.size + 2
83 self.__initlnOR = self.__lenghtOR
84 self.__seenNames.append(str(oName))
85 if str(oName) not in self.__seenNames: # Etend la liste si nouveau
86 self.__lenghtOR += 2 * xValue.size + 2
87 self.__initlnOR += self.__lenghtOR
88 self.__seenNames.append(str(oName))
89 while len(self.__listOPCV) > self.__lenghtOR:
90 # logging.debug("CM Réduction de la liste des cas à %i éléments par suppression du premier", self.__lenghtOR)
91 self.__listOPCV.pop(0)
92 self.__listOPCV.append( (
93 copy.copy(numpy.ravel(xValue)),
95 numpy.linalg.norm(xValue),
101 self.__initlnOR = self.__lenghtOR
103 self.__enabled = False
107 self.__lenghtOR = self.__initlnOR
108 self.__enabled = True
110 # ==============================================================================
111 class Operator(object):
113 Classe générale d'interface de type opérateur simple
121 name = "GenericOperator",
124 avoidingRedundancy = True,
125 inputAsMultiFunction = False,
126 enableMultiProcess = False,
127 extraArguments = None,
130 On construit un objet de ce type en fournissant, à l'aide de l'un des
131 deux mots-clé, soit une fonction ou un multi-fonction python, soit une
134 - name : nom d'opérateur
135 - fromMethod : argument de type fonction Python
136 - fromMatrix : argument adapté au constructeur numpy.matrix
137 - avoidingRedundancy : booléen évitant (ou pas) les calculs redondants
138 - inputAsMultiFunction : booléen indiquant une fonction explicitement
139 définie (ou pas) en multi-fonction
140 - extraArguments : arguments supplémentaires passés à la fonction de
141 base et ses dérivées (tuple ou dictionnaire)
143 self.__name = str(name)
144 self.__NbCallsAsMatrix, self.__NbCallsAsMethod, self.__NbCallsOfCached = 0, 0, 0
145 self.__AvoidRC = bool( avoidingRedundancy )
146 self.__inputAsMF = bool( inputAsMultiFunction )
147 self.__mpEnabled = bool( enableMultiProcess )
148 self.__extraArgs = extraArguments
149 if fromMethod is not None and self.__inputAsMF:
150 self.__Method = fromMethod # logtimer(fromMethod)
152 self.__Type = "Method"
153 elif fromMethod is not None and not self.__inputAsMF:
154 self.__Method = partial( MultiFonction, _sFunction=fromMethod, _mpEnabled=self.__mpEnabled)
156 self.__Type = "Method"
157 elif fromMatrix is not None:
159 self.__Matrix = numpy.matrix( fromMatrix, numpy.float )
160 self.__Type = "Matrix"
166 def disableAvoidingRedundancy(self):
168 Operator.CM.disable()
170 def enableAvoidingRedundancy(self):
175 Operator.CM.disable()
181 def appliedTo(self, xValue, HValue = None, argsAsSerie = False):
183 Permet de restituer le résultat de l'application de l'opérateur à une
184 série d'arguments xValue. Cette méthode se contente d'appliquer, chaque
185 argument devant a priori être du bon type.
187 - les arguments par série sont :
188 - xValue : argument adapté pour appliquer l'opérateur
189 - HValue : valeur précalculée de l'opérateur en ce point
190 - argsAsSerie : indique si les arguments sont une mono ou multi-valeur
197 if HValue is not None:
201 PlatformInfo.isIterable( _xValue, True, " in Operator.appliedTo" )
203 if _HValue is not None:
204 assert len(_xValue) == len(_HValue), "Incompatible number of elements in xValue and HValue"
206 for i in range(len(_HValue)):
207 HxValue.append( numpy.asmatrix( numpy.ravel( _HValue[i] ) ).T )
209 Operator.CM.storeValueInX(_xValue[i],HxValue[-1],self.__name)
214 for i, xv in enumerate(_xValue):
216 __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xv,self.__name)
218 __alreadyCalculated = False
220 if __alreadyCalculated:
221 self.__addOneCacheCall()
224 if self.__Matrix is not None:
225 self.__addOneMatrixCall()
226 _hv = self.__Matrix * xv
228 self.__addOneMethodCall()
232 HxValue.append( _hv )
234 if len(_xserie)>0 and self.__Matrix is None:
235 if self.__extraArgs is None:
236 _hserie = self.__Method( _xserie ) # Calcul MF
238 _hserie = self.__Method( _xserie, self.__extraArgs ) # Calcul MF
239 if not hasattr(_hserie, "pop"):
240 raise TypeError("The user input multi-function doesn't seem to return sequence results, behaving like a mono-function. It has to be checked.")
246 Operator.CM.storeValueInX(_xv,_hv,self.__name)
248 if argsAsSerie: return HxValue
249 else: return HxValue[-1]
251 def appliedControledFormTo(self, paires, argsAsSerie = False):
253 Permet de restituer le résultat de l'application de l'opérateur à des
254 paires (xValue, uValue). Cette méthode se contente d'appliquer, son
255 argument devant a priori être du bon type. Si la uValue est None,
256 on suppose que l'opérateur ne s'applique qu'à xValue.
258 - paires : les arguments par paire sont :
259 - xValue : argument X adapté pour appliquer l'opérateur
260 - uValue : argument U adapté pour appliquer l'opérateur
261 - argsAsSerie : indique si l'argument est une mono ou multi-valeur
263 if argsAsSerie: _xuValue = paires
264 else: _xuValue = (paires,)
265 PlatformInfo.isIterable( _xuValue, True, " in Operator.appliedControledFormTo" )
267 if self.__Matrix is not None:
269 for paire in _xuValue:
270 _xValue, _uValue = paire
271 self.__addOneMatrixCall()
272 HxValue.append( self.__Matrix * _xValue )
275 for paire in _xuValue:
277 _xValue, _uValue = paire
278 if _uValue is not None:
279 _xuValue.append( paire )
281 _xuValue.append( _xValue )
282 self.__addOneMethodCall( len(_xuValue) )
283 if self.__extraArgs is None:
284 HxValue = self.__Method( _xuValue ) # Calcul MF
286 HxValue = self.__Method( _xuValue, self.__extraArgs ) # Calcul MF
288 if argsAsSerie: return HxValue
289 else: return HxValue[-1]
291 def appliedInXTo(self, paires, argsAsSerie = False):
293 Permet de restituer le résultat de l'application de l'opérateur à une
294 série d'arguments xValue, sachant que l'opérateur est valable en
295 xNominal. Cette méthode se contente d'appliquer, son argument devant a
296 priori être du bon type. Si l'opérateur est linéaire car c'est une
297 matrice, alors il est valable en tout point nominal et xNominal peut
298 être quelconque. Il n'y a qu'une seule paire par défaut, et argsAsSerie
299 permet d'indiquer que l'argument est multi-paires.
301 - paires : les arguments par paire sont :
302 - xNominal : série d'arguments permettant de donner le point où
303 l'opérateur est construit pour être ensuite appliqué
304 - xValue : série d'arguments adaptés pour appliquer l'opérateur
305 - argsAsSerie : indique si l'argument est une mono ou multi-valeur
307 if argsAsSerie: _nxValue = paires
308 else: _nxValue = (paires,)
309 PlatformInfo.isIterable( _nxValue, True, " in Operator.appliedInXTo" )
311 if self.__Matrix is not None:
313 for paire in _nxValue:
314 _xNominal, _xValue = paire
315 self.__addOneMatrixCall()
316 HxValue.append( self.__Matrix * _xValue )
318 self.__addOneMethodCall( len(_nxValue) )
319 if self.__extraArgs is None:
320 HxValue = self.__Method( _nxValue ) # Calcul MF
322 HxValue = self.__Method( _nxValue, self.__extraArgs ) # Calcul MF
324 if argsAsSerie: return HxValue
325 else: return HxValue[-1]
327 def asMatrix(self, ValueForMethodForm = "UnknownVoidValue", argsAsSerie = False):
329 Permet de renvoyer l'opérateur sous la forme d'une matrice
331 if self.__Matrix is not None:
332 self.__addOneMatrixCall()
333 mValue = [self.__Matrix,]
334 elif not isinstance(ValueForMethodForm,str) or ValueForMethodForm != "UnknownVoidValue": # Ne pas utiliser "None"
337 self.__addOneMethodCall( len(ValueForMethodForm) )
338 for _vfmf in ValueForMethodForm:
339 mValue.append( numpy.matrix( self.__Method(((_vfmf, None),)) ) )
341 self.__addOneMethodCall()
342 mValue = self.__Method(((ValueForMethodForm, None),))
344 raise ValueError("Matrix form of the operator defined as a function/method requires to give an operating point.")
346 if argsAsSerie: return mValue
347 else: return mValue[-1]
351 Renvoie la taille sous forme numpy si l'opérateur est disponible sous
352 la forme d'une matrice
354 if self.__Matrix is not None:
355 return self.__Matrix.shape
357 raise ValueError("Matrix form of the operator is not available, nor the shape")
359 def nbcalls(self, which=None):
361 Renvoie les nombres d'évaluations de l'opérateur
364 self.__NbCallsAsMatrix+self.__NbCallsAsMethod,
365 self.__NbCallsAsMatrix,
366 self.__NbCallsAsMethod,
367 self.__NbCallsOfCached,
368 Operator.NbCallsAsMatrix+Operator.NbCallsAsMethod,
369 Operator.NbCallsAsMatrix,
370 Operator.NbCallsAsMethod,
371 Operator.NbCallsOfCached,
373 if which is None: return __nbcalls
374 else: return __nbcalls[which]
376 def __addOneMatrixCall(self):
377 "Comptabilise un appel"
378 self.__NbCallsAsMatrix += 1 # Decompte local
379 Operator.NbCallsAsMatrix += 1 # Decompte global
381 def __addOneMethodCall(self, nb = 1):
382 "Comptabilise un appel"
383 self.__NbCallsAsMethod += nb # Decompte local
384 Operator.NbCallsAsMethod += nb # Decompte global
386 def __addOneCacheCall(self):
387 "Comptabilise un appel"
388 self.__NbCallsOfCached += 1 # Decompte local
389 Operator.NbCallsOfCached += 1 # Decompte global
391 # ==============================================================================
392 class FullOperator(object):
394 Classe générale d'interface de type opérateur complet
395 (Direct, Linéaire Tangent, Adjoint)
398 name = "GenericFullOperator",
400 asOneFunction = None, # 1 Fonction
401 asThreeFunctions = None, # 3 Fonctions in a dictionary
402 asScript = None, # 1 or 3 Fonction(s) by script
403 asDict = None, # Parameters
405 extraArguments = None,
407 inputAsMF = False,# Fonction(s) as Multi-Functions
412 self.__name = str(name)
413 self.__check = bool(toBeChecked)
414 self.__extraArgs = extraArguments
419 if (asDict is not None) and isinstance(asDict, dict):
420 __Parameters.update( asDict )
421 # Priorité à EnableMultiProcessingInDerivatives=True
422 if "EnableMultiProcessing" in __Parameters and __Parameters["EnableMultiProcessing"]:
423 __Parameters["EnableMultiProcessingInDerivatives"] = True
424 __Parameters["EnableMultiProcessingInEvaluation"] = False
425 if "EnableMultiProcessingInDerivatives" not in __Parameters:
426 __Parameters["EnableMultiProcessingInDerivatives"] = False
427 if __Parameters["EnableMultiProcessingInDerivatives"]:
428 __Parameters["EnableMultiProcessingInEvaluation"] = False
429 if "EnableMultiProcessingInEvaluation" not in __Parameters:
430 __Parameters["EnableMultiProcessingInEvaluation"] = False
431 if "withIncrement" in __Parameters: # Temporaire
432 __Parameters["DifferentialIncrement"] = __Parameters["withIncrement"]
434 if asScript is not None:
435 __Matrix, __Function = None, None
437 __Matrix = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
439 __Function = { "Direct":Interfaces.ImportFromScript(asScript).getvalue( "DirectOperator" ) }
440 __Function.update({"useApproximatedDerivatives":True})
441 __Function.update(__Parameters)
442 elif asThreeFunctions:
444 "Direct" :Interfaces.ImportFromScript(asScript).getvalue( "DirectOperator" ),
445 "Tangent":Interfaces.ImportFromScript(asScript).getvalue( "TangentOperator" ),
446 "Adjoint":Interfaces.ImportFromScript(asScript).getvalue( "AdjointOperator" ),
448 __Function.update(__Parameters)
451 if asOneFunction is not None:
452 if isinstance(asOneFunction, dict) and "Direct" in asOneFunction:
453 if asOneFunction["Direct"] is not None:
454 __Function = asOneFunction
456 raise ValueError("The function has to be given in a dictionnary which have 1 key (\"Direct\")")
458 __Function = { "Direct":asOneFunction }
459 __Function.update({"useApproximatedDerivatives":True})
460 __Function.update(__Parameters)
461 elif asThreeFunctions is not None:
462 if isinstance(asThreeFunctions, dict) and \
463 ("Tangent" in asThreeFunctions) and (asThreeFunctions["Tangent"] is not None) and \
464 ("Adjoint" in asThreeFunctions) and (asThreeFunctions["Adjoint"] is not None) and \
465 (("useApproximatedDerivatives" not in asThreeFunctions) or not bool(asThreeFunctions["useApproximatedDerivatives"])):
466 __Function = asThreeFunctions
467 elif isinstance(asThreeFunctions, dict) and \
468 ("Direct" in asThreeFunctions) and (asThreeFunctions["Direct"] is not None):
469 __Function = asThreeFunctions
470 __Function.update({"useApproximatedDerivatives":True})
472 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\")")
473 if "Direct" not in asThreeFunctions:
474 __Function["Direct"] = asThreeFunctions["Tangent"]
475 __Function.update(__Parameters)
479 # if sys.version_info[0] < 3 and isinstance(__Function, dict):
480 # for k in ("Direct", "Tangent", "Adjoint"):
481 # if k in __Function and hasattr(__Function[k],"__class__"):
482 # if type(__Function[k]) is type(self.__init__):
483 # 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))
485 if appliedInX is not None and isinstance(appliedInX, dict):
486 __appliedInX = appliedInX
487 elif appliedInX is not None:
488 __appliedInX = {"HXb":appliedInX}
492 if scheduledBy is not None:
493 self.__T = scheduledBy
495 if isinstance(__Function, dict) and \
496 ("useApproximatedDerivatives" in __Function) and bool(__Function["useApproximatedDerivatives"]) and \
497 ("Direct" in __Function) and (__Function["Direct"] is not None):
498 if "CenteredFiniteDifference" not in __Function: __Function["CenteredFiniteDifference"] = False
499 if "DifferentialIncrement" not in __Function: __Function["DifferentialIncrement"] = 0.01
500 if "withdX" not in __Function: __Function["withdX"] = None
501 if "withAvoidingRedundancy" not in __Function: __Function["withAvoidingRedundancy"] = avoidRC
502 if "withToleranceInRedundancy" not in __Function: __Function["withToleranceInRedundancy"] = 1.e-18
503 if "withLenghtOfRedundancy" not in __Function: __Function["withLenghtOfRedundancy"] = -1
504 if "NumberOfProcesses" not in __Function: __Function["NumberOfProcesses"] = None
505 if "withmfEnabled" not in __Function: __Function["withmfEnabled"] = inputAsMF
506 from daCore import NumericObjects
507 FDA = NumericObjects.FDApproximation(
509 Function = __Function["Direct"],
510 centeredDF = __Function["CenteredFiniteDifference"],
511 increment = __Function["DifferentialIncrement"],
512 dX = __Function["withdX"],
513 avoidingRedundancy = __Function["withAvoidingRedundancy"],
514 toleranceInRedundancy = __Function["withToleranceInRedundancy"],
515 lenghtOfRedundancy = __Function["withLenghtOfRedundancy"],
516 mpEnabled = __Function["EnableMultiProcessingInDerivatives"],
517 mpWorkers = __Function["NumberOfProcesses"],
518 mfEnabled = __Function["withmfEnabled"],
520 self.__FO["Direct"] = Operator( name = self.__name, fromMethod = FDA.DirectOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
521 self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = FDA.TangentOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
522 self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = FDA.AdjointOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
523 elif isinstance(__Function, dict) and \
524 ("Direct" in __Function) and ("Tangent" in __Function) and ("Adjoint" in __Function) and \
525 (__Function["Direct"] is not None) and (__Function["Tangent"] is not None) and (__Function["Adjoint"] is not None):
526 self.__FO["Direct"] = Operator( name = self.__name, fromMethod = __Function["Direct"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
527 self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = __Function["Tangent"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
528 self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = __Function["Adjoint"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
529 elif asMatrix is not None:
530 __matrice = numpy.matrix( __Matrix, numpy.float )
531 self.__FO["Direct"] = Operator( name = self.__name, fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
532 self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF )
533 self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMatrix = __matrice.T, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF )
536 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)
538 if __appliedInX is not None:
539 self.__FO["AppliedInX"] = {}
540 for key in list(__appliedInX.keys()):
541 if type( __appliedInX[key] ) is type( numpy.matrix([]) ):
542 # Pour le cas où l'on a une vraie matrice
543 self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key].A1, numpy.float ).T
544 elif type( __appliedInX[key] ) is type( numpy.array([]) ) and len(__appliedInX[key].shape) > 1:
545 # Pour le cas où l'on a un vecteur représenté en array avec 2 dimensions
546 self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key].reshape(len(__appliedInX[key]),), numpy.float ).T
548 self.__FO["AppliedInX"][key] = numpy.matrix( __appliedInX[key], numpy.float ).T
550 self.__FO["AppliedInX"] = None
556 "x.__repr__() <==> repr(x)"
557 return repr(self.__FO)
560 "x.__str__() <==> str(x)"
561 return str(self.__FO)
563 # ==============================================================================
564 class Algorithm(object):
566 Classe générale d'interface de type algorithme
568 Elle donne un cadre pour l'écriture d'une classe élémentaire d'algorithme
569 d'assimilation, en fournissant un container (dictionnaire) de variables
570 persistantes initialisées, et des méthodes d'accès à ces variables stockées.
572 Une classe élémentaire d'algorithme doit implémenter la méthode "run".
574 def __init__(self, name):
576 L'initialisation présente permet de fabriquer des variables de stockage
577 disponibles de manière générique dans les algorithmes élémentaires. Ces
578 variables de stockage sont ensuite conservées dans un dictionnaire
579 interne à l'objet, mais auquel on accède par la méthode "get".
581 Les variables prévues sont :
582 - APosterioriCorrelations : matrice de corrélations de la matrice A
583 - APosterioriCovariance : matrice de covariances a posteriori : A
584 - APosterioriStandardDeviations : vecteur des écart-types de la matrice A
585 - APosterioriVariances : vecteur des variances de la matrice A
586 - Analysis : vecteur d'analyse : Xa
587 - BMA : Background moins Analysis : Xa - Xb
588 - CostFunctionJ : fonction-coût globale, somme des deux parties suivantes Jb et Jo
589 - CostFunctionJAtCurrentOptimum : fonction-coût globale à l'état optimal courant lors d'itérations
590 - CostFunctionJb : partie ébauche ou background de la fonction-coût : Jb
591 - CostFunctionJbAtCurrentOptimum : partie ébauche à l'état optimal courant lors d'itérations
592 - CostFunctionJo : partie observations de la fonction-coût : Jo
593 - CostFunctionJoAtCurrentOptimum : partie observations à l'état optimal courant lors d'itérations
594 - CurrentOptimum : état optimal courant lors d'itérations
595 - CurrentState : état courant lors d'itérations
596 - GradientOfCostFunctionJ : gradient de la fonction-coût globale
597 - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-coût
598 - GradientOfCostFunctionJo : gradient de la partie observations de la fonction-coût
599 - IndexOfOptimum : index de l'état optimal courant lors d'itérations
600 - Innovation : l'innovation : d = Y - H(X)
601 - InnovationAtCurrentState : l'innovation à l'état courant : dn = Y - H(Xn)
602 - JacobianMatrixAtBackground : matrice jacobienne à l'état d'ébauche
603 - JacobianMatrixAtCurrentState : matrice jacobienne à l'état courant
604 - JacobianMatrixAtOptimum : matrice jacobienne à l'optimum
605 - KalmanGainAtOptimum : gain de Kalman à l'optimum
606 - MahalanobisConsistency : indicateur de consistance des covariances
607 - OMA : Observation moins Analyse : Y - Xa
608 - OMB : Observation moins Background : Y - Xb
609 - ForecastState : état prédit courant lors d'itérations
610 - Residu : dans le cas des algorithmes de vérification
611 - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche
612 - SigmaObs2 : indicateur de correction optimale des erreurs d'observation
613 - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche
614 - SimulatedObservationAtCurrentOptimum : l'état observé H(X) à l'état optimal courant
615 - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant
616 - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum
617 - SimulationQuantiles : états observés H(X) pour les quantiles demandés
618 On peut rajouter des variables à stocker dans l'initialisation de
619 l'algorithme élémentaire qui va hériter de cette classe
621 logging.debug("%s Initialisation", str(name))
622 self._m = PlatformInfo.SystemUsage()
624 self._name = str( name )
625 self._parameters = {"StoreSupplementaryCalculations":[]}
626 self.__required_parameters = {}
627 self.__required_inputs = {
628 "RequiredInputValues":{"mandatory":(), "optional":()},
629 "ClassificationTags":[],
631 self.__variable_names_not_public = {"nextStep":False} # Duplication dans AlgorithmAndParameters
632 self.__canonical_parameter_name = {} # Correspondance "lower"->"correct"
633 self.__canonical_stored_name = {} # Correspondance "lower"->"correct"
635 self.StoredVariables = {}
636 self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations")
637 self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance")
638 self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations")
639 self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances")
640 self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
641 self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA")
642 self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ")
643 self.StoredVariables["CostFunctionJAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJAtCurrentOptimum")
644 self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
645 self.StoredVariables["CostFunctionJbAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJbAtCurrentOptimum")
646 self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
647 self.StoredVariables["CostFunctionJoAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJoAtCurrentOptimum")
648 self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum")
649 self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState")
650 self.StoredVariables["ForecastState"] = Persistence.OneVector(name = "ForecastState")
651 self.StoredVariables["GradientOfCostFunctionJ"] = Persistence.OneVector(name = "GradientOfCostFunctionJ")
652 self.StoredVariables["GradientOfCostFunctionJb"] = Persistence.OneVector(name = "GradientOfCostFunctionJb")
653 self.StoredVariables["GradientOfCostFunctionJo"] = Persistence.OneVector(name = "GradientOfCostFunctionJo")
654 self.StoredVariables["IndexOfOptimum"] = Persistence.OneIndex(name = "IndexOfOptimum")
655 self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation")
656 self.StoredVariables["InnovationAtCurrentAnalysis"] = Persistence.OneVector(name = "InnovationAtCurrentAnalysis")
657 self.StoredVariables["InnovationAtCurrentState"] = Persistence.OneVector(name = "InnovationAtCurrentState")
658 self.StoredVariables["JacobianMatrixAtBackground"] = Persistence.OneMatrix(name = "JacobianMatrixAtBackground")
659 self.StoredVariables["JacobianMatrixAtCurrentState"] = Persistence.OneMatrix(name = "JacobianMatrixAtCurrentState")
660 self.StoredVariables["JacobianMatrixAtOptimum"] = Persistence.OneMatrix(name = "JacobianMatrixAtOptimum")
661 self.StoredVariables["KalmanGainAtOptimum"] = Persistence.OneMatrix(name = "KalmanGainAtOptimum")
662 self.StoredVariables["MahalanobisConsistency"] = Persistence.OneScalar(name = "MahalanobisConsistency")
663 self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA")
664 self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB")
665 self.StoredVariables["Residu"] = Persistence.OneScalar(name = "Residu")
666 self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2")
667 self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2")
668 self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground")
669 self.StoredVariables["SimulatedObservationAtCurrentAnalysis"]= Persistence.OneVector(name = "SimulatedObservationAtCurrentAnalysis")
670 self.StoredVariables["SimulatedObservationAtCurrentOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentOptimum")
671 self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState")
672 self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum")
673 self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles")
675 for k in self.StoredVariables:
676 self.__canonical_stored_name[k.lower()] = k
678 for k, v in self.__variable_names_not_public.items():
679 self.__canonical_parameter_name[k.lower()] = k
680 self.__canonical_parameter_name["algorithm"] = "Algorithm"
681 self.__canonical_parameter_name["storesupplementarycalculations"] = "StoreSupplementaryCalculations"
683 def _pre_run(self, Parameters, Xb=None, Y=None, R=None, B=None, Q=None ):
685 logging.debug("%s Lancement", self._name)
686 logging.debug("%s Taille mémoire utilisée de %.0f Mio"%(self._name, self._m.getUsedMemory("Mio")))
688 # Mise a jour des paramètres internes avec le contenu de Parameters, en
689 # reprenant les valeurs par défauts pour toutes celles non définies
690 self.__setParameters(Parameters, reset=True)
691 for k, v in self.__variable_names_not_public.items():
692 if k not in self._parameters: self.__setParameters( {k:v} )
694 # Corrections et compléments
695 def __test_vvalue(argument, variable, argname):
697 if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
698 raise ValueError("%s %s vector %s has to be properly defined!"%(self._name,argname,variable))
699 elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
700 logging.debug("%s %s vector %s is not set, but is optional."%(self._name,argname,variable))
702 logging.debug("%s %s vector %s is not set, but is not required."%(self._name,argname,variable))
704 logging.debug("%s %s vector %s is set, and its size is %i."%(self._name,argname,variable,numpy.array(argument).size))
706 __test_vvalue( Xb, "Xb", "Background or initial state" )
707 __test_vvalue( Y, "Y", "Observation" )
709 def __test_cvalue(argument, variable, argname):
711 if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
712 raise ValueError("%s %s error covariance matrix %s has to be properly defined!"%(self._name,argname,variable))
713 elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
714 logging.debug("%s %s error covariance matrix %s is not set, but is optional."%(self._name,argname,variable))
716 logging.debug("%s %s error covariance matrix %s is not set, but is not required."%(self._name,argname,variable))
718 logging.debug("%s %s error covariance matrix %s is set."%(self._name,argname,variable))
720 __test_cvalue( R, "R", "Observation" )
721 __test_cvalue( B, "B", "Background" )
722 __test_cvalue( Q, "Q", "Evolution" )
724 if ("Bounds" in self._parameters) and isinstance(self._parameters["Bounds"], (list, tuple)) and (len(self._parameters["Bounds"]) > 0):
725 logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
727 self._parameters["Bounds"] = None
729 if logging.getLogger().level < logging.WARNING:
730 self._parameters["optiprint"], self._parameters["optdisp"] = 1, 1
731 if PlatformInfo.has_scipy:
732 import scipy.optimize
733 self._parameters["optmessages"] = scipy.optimize.tnc.MSG_ALL
735 self._parameters["optmessages"] = 15
737 self._parameters["optiprint"], self._parameters["optdisp"] = -1, 0
738 if PlatformInfo.has_scipy:
739 import scipy.optimize
740 self._parameters["optmessages"] = scipy.optimize.tnc.MSG_NONE
742 self._parameters["optmessages"] = 15
746 def _post_run(self,_oH=None):
748 if ("StoreSupplementaryCalculations" in self._parameters) and \
749 "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
750 for _A in self.StoredVariables["APosterioriCovariance"]:
751 if "APosterioriVariances" in self._parameters["StoreSupplementaryCalculations"]:
752 self.StoredVariables["APosterioriVariances"].store( numpy.diag(_A) )
753 if "APosterioriStandardDeviations" in self._parameters["StoreSupplementaryCalculations"]:
754 self.StoredVariables["APosterioriStandardDeviations"].store( numpy.sqrt(numpy.diag(_A)) )
755 if "APosterioriCorrelations" in self._parameters["StoreSupplementaryCalculations"]:
756 _EI = numpy.diag(1./numpy.sqrt(numpy.diag(_A)))
757 _C = numpy.dot(_EI, numpy.dot(_A, _EI))
758 self.StoredVariables["APosterioriCorrelations"].store( _C )
759 if _oH is not None and "Direct" in _oH and "Tangent" in _oH and "Adjoint" in _oH:
760 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))
761 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))
762 logging.debug("%s Taille mémoire utilisée de %.0f Mio", self._name, self._m.getUsedMemory("Mio"))
763 logging.debug("%s Terminé", self._name)
766 def _toStore(self, key):
767 "True if in StoreSupplementaryCalculations, else False"
768 return key in self._parameters["StoreSupplementaryCalculations"]
770 def get(self, key=None):
772 Renvoie l'une des variables stockées identifiée par la clé, ou le
773 dictionnaire de l'ensemble des variables disponibles en l'absence de
774 clé. Ce sont directement les variables sous forme objet qui sont
775 renvoyées, donc les méthodes d'accès à l'objet individuel sont celles
776 des classes de persistance.
779 return self.StoredVariables[self.__canonical_stored_name[key.lower()]]
781 return self.StoredVariables
783 def __contains__(self, key=None):
784 "D.__contains__(k) -> True if D has a key k, else False"
785 if key is None or key.lower() not in self.__canonical_stored_name:
788 return self.__canonical_stored_name[key.lower()] in self.StoredVariables
791 "D.keys() -> list of D's keys"
792 if hasattr(self, "StoredVariables"):
793 return self.StoredVariables.keys()
798 "D.pop(k[,d]) -> v, remove specified key and return the corresponding value"
799 if hasattr(self, "StoredVariables") and k.lower() in self.__canonical_stored_name:
800 return self.StoredVariables.pop(self.__canonical_stored_name[k.lower()], d)
805 raise TypeError("pop expected at least 1 arguments, got 0")
806 "If key is not found, d is returned if given, otherwise KeyError is raised"
812 def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
814 Doit implémenter l'opération élémentaire de calcul d'assimilation sous
815 sa forme mathématique la plus naturelle possible.
817 raise NotImplementedError("Mathematical assimilation calculation has not been implemented!")
819 def defineRequiredParameter(self, name = None, default = None, typecast = None, message = None, minval = None, maxval = None, listval = None):
821 Permet de définir dans l'algorithme des paramètres requis et leurs
822 caractéristiques par défaut.
825 raise ValueError("A name is mandatory to define a required parameter.")
827 self.__required_parameters[name] = {
829 "typecast" : typecast,
835 self.__canonical_parameter_name[name.lower()] = name
836 logging.debug("%s %s (valeur par défaut = %s)", self._name, message, self.setParameterValue(name))
838 def getRequiredParameters(self, noDetails=True):
840 Renvoie la liste des noms de paramètres requis ou directement le
841 dictionnaire des paramètres requis.
844 return sorted(self.__required_parameters.keys())
846 return self.__required_parameters
848 def setParameterValue(self, name=None, value=None):
850 Renvoie la valeur d'un paramètre requis de manière contrôlée
852 __k = self.__canonical_parameter_name[name.lower()]
853 default = self.__required_parameters[__k]["default"]
854 typecast = self.__required_parameters[__k]["typecast"]
855 minval = self.__required_parameters[__k]["minval"]
856 maxval = self.__required_parameters[__k]["maxval"]
857 listval = self.__required_parameters[__k]["listval"]
859 if value is None and default is None:
861 elif value is None and default is not None:
862 if typecast is None: __val = default
863 else: __val = typecast( default )
865 if typecast is None: __val = value
868 __val = typecast( value )
870 raise ValueError("The value '%s' for the parameter named '%s' can not be correctly evaluated with type '%s'."%(value, __k, typecast))
872 if minval is not None and (numpy.array(__val, float) < minval).any():
873 raise ValueError("The parameter named '%s' of value '%s' can not be less than %s."%(__k, __val, minval))
874 if maxval is not None and (numpy.array(__val, float) > maxval).any():
875 raise ValueError("The parameter named '%s' of value '%s' can not be greater than %s."%(__k, __val, maxval))
876 if listval is not None:
877 if typecast is list or typecast is tuple or isinstance(__val,list) or isinstance(__val,tuple):
880 raise ValueError("The value '%s' is not allowed for the parameter named '%s', it has to be in the list %s."%(v, __k, listval))
881 elif __val not in listval:
882 raise ValueError("The value '%s' is not allowed for the parameter named '%s', it has to be in the list %s."%( __val, __k,listval))
886 def requireInputArguments(self, mandatory=(), optional=()):
888 Permet d'imposer des arguments de calcul requis en entrée.
890 self.__required_inputs["RequiredInputValues"]["mandatory"] = tuple( mandatory )
891 self.__required_inputs["RequiredInputValues"]["optional"] = tuple( optional )
893 def getInputArguments(self):
895 Permet d'obtenir les listes des arguments de calcul requis en entrée.
897 return self.__required_inputs["RequiredInputValues"]["mandatory"], self.__required_inputs["RequiredInputValues"]["optional"]
899 def setAttributes(self, tags=()):
901 Permet d'adjoindre des attributs comme les tags de classification.
902 Renvoie la liste actuelle dans tous les cas.
904 self.__required_inputs["ClassificationTags"].extend( tags )
905 return self.__required_inputs["ClassificationTags"]
907 def __setParameters(self, fromDico={}, reset=False):
909 Permet de stocker les paramètres reçus dans le dictionnaire interne.
911 self._parameters.update( fromDico )
912 __inverse_fromDico_keys = {}
913 for k in fromDico.keys():
914 if k.lower() in self.__canonical_parameter_name:
915 __inverse_fromDico_keys[self.__canonical_parameter_name[k.lower()]] = k
916 #~ __inverse_fromDico_keys = dict([(self.__canonical_parameter_name[k.lower()],k) for k in fromDico.keys()])
917 __canonic_fromDico_keys = __inverse_fromDico_keys.keys()
918 for k in self.__required_parameters.keys():
919 if k in __canonic_fromDico_keys:
920 self._parameters[k] = self.setParameterValue(k,fromDico[__inverse_fromDico_keys[k]])
922 self._parameters[k] = self.setParameterValue(k)
925 logging.debug("%s %s : %s", self._name, self.__required_parameters[k]["message"], self._parameters[k])
927 # ==============================================================================
928 class AlgorithmAndParameters(object):
930 Classe générale d'interface d'action pour l'algorithme et ses paramètres
933 name = "GenericAlgorithm",
940 self.__name = str(name)
944 self.__algorithm = {}
945 self.__algorithmFile = None
946 self.__algorithmName = None
948 self.updateParameters( asDict, asScript )
950 if asAlgorithm is None and asScript is not None:
951 __Algo = Interfaces.ImportFromScript(asScript).getvalue( "Algorithm" )
955 if __Algo is not None:
956 self.__A = str(__Algo)
957 self.__P.update( {"Algorithm":self.__A} )
959 self.__setAlgorithm( self.__A )
961 self.__variable_names_not_public = {"nextStep":False} # Duplication dans Algorithm
963 def updateParameters(self,
967 "Mise a jour des parametres"
968 if asDict is None and asScript is not None:
969 __Dict = Interfaces.ImportFromScript(asScript).getvalue( self.__name, "Parameters" )
973 if __Dict is not None:
974 self.__P.update( dict(__Dict) )
976 def executePythonScheme(self, asDictAO = None):
977 "Permet de lancer le calcul d'assimilation"
978 Operator.CM.clearCache()
980 if not isinstance(asDictAO, dict):
981 raise ValueError("The objects for algorithm calculation have to be given together as a dictionnary, and they are not")
982 if hasattr(asDictAO["Background"],"getO"): self.__Xb = asDictAO["Background"].getO()
983 elif hasattr(asDictAO["CheckingPoint"],"getO"): self.__Xb = asDictAO["CheckingPoint"].getO()
984 else: self.__Xb = None
985 if hasattr(asDictAO["Observation"],"getO"): self.__Y = asDictAO["Observation"].getO()
986 else: self.__Y = asDictAO["Observation"]
987 if hasattr(asDictAO["ControlInput"],"getO"): self.__U = asDictAO["ControlInput"].getO()
988 else: self.__U = asDictAO["ControlInput"]
989 if hasattr(asDictAO["ObservationOperator"],"getO"): self.__HO = asDictAO["ObservationOperator"].getO()
990 else: self.__HO = asDictAO["ObservationOperator"]
991 if hasattr(asDictAO["EvolutionModel"],"getO"): self.__EM = asDictAO["EvolutionModel"].getO()
992 else: self.__EM = asDictAO["EvolutionModel"]
993 if hasattr(asDictAO["ControlModel"],"getO"): self.__CM = asDictAO["ControlModel"].getO()
994 else: self.__CM = asDictAO["ControlModel"]
995 self.__B = asDictAO["BackgroundError"]
996 self.__R = asDictAO["ObservationError"]
997 self.__Q = asDictAO["EvolutionError"]
999 self.__shape_validate()
1001 self.__algorithm.run(
1011 Parameters = self.__P,
1015 def executeYACSScheme(self, FileName=None):
1016 "Permet de lancer le calcul d'assimilation"
1017 if FileName is None or not os.path.exists(FileName):
1018 raise ValueError("a YACS file name has to be given for YACS execution.\n")
1020 __file = os.path.abspath(FileName)
1021 logging.debug("The YACS file name is \"%s\"."%__file)
1022 if not PlatformInfo.has_salome or \
1023 not PlatformInfo.has_yacs or \
1024 not PlatformInfo.has_adao:
1025 raise ImportError("\n\n"+\
1026 "Unable to get SALOME, YACS or ADAO environnement variables.\n"+\
1027 "Please load the right environnement before trying to use it.\n")
1030 import SALOMERuntime
1032 SALOMERuntime.RuntimeSALOME_setRuntime()
1034 r = pilot.getRuntime()
1035 xmlLoader = loader.YACSLoader()
1036 xmlLoader.registerProcCataLoader()
1038 catalogAd = r.loadCatalog("proc", __file)
1039 r.addCatalog(catalogAd)
1044 p = xmlLoader.load(__file)
1045 except IOError as ex:
1046 print("The YACS XML schema file can not be loaded: %s"%(ex,))
1048 logger = p.getLogger("parser")
1049 if not logger.isEmpty():
1050 print("The imported YACS XML schema has errors on parsing:")
1051 print(logger.getStr())
1054 print("The YACS XML schema is not valid and will not be executed:")
1055 print(p.getErrorReport())
1057 info=pilot.LinkInfo(pilot.LinkInfo.ALL_DONT_STOP)
1058 p.checkConsistency(info)
1059 if info.areWarningsOrErrors():
1060 print("The YACS XML schema is not coherent and will not be executed:")
1061 print(info.getGlobalRepr())
1063 e = pilot.ExecutorSwig()
1065 if p.getEffectiveState() != pilot.DONE:
1066 print(p.getErrorReport())
1070 def get(self, key = None):
1071 "Vérifie l'existence d'une clé de variable ou de paramètres"
1072 if key in self.__algorithm:
1073 return self.__algorithm.get( key )
1074 elif key in self.__P:
1075 return self.__P[key]
1077 allvariables = self.__P
1078 for k in self.__variable_names_not_public: allvariables.pop(k, None)
1081 def pop(self, k, d):
1082 "Necessaire pour le pickling"
1083 return self.__algorithm.pop(k, d)
1085 def getAlgorithmRequiredParameters(self, noDetails=True):
1086 "Renvoie la liste des paramètres requis selon l'algorithme"
1087 return self.__algorithm.getRequiredParameters(noDetails)
1089 def getAlgorithmInputArguments(self):
1090 "Renvoie la liste des entrées requises selon l'algorithme"
1091 return self.__algorithm.getInputArguments()
1093 def getAlgorithmAttributes(self):
1094 "Renvoie la liste des attributs selon l'algorithme"
1095 return self.__algorithm.setAttributes()
1097 def setObserver(self, __V, __O, __I, __S):
1098 if self.__algorithm is None \
1099 or isinstance(self.__algorithm, dict) \
1100 or not hasattr(self.__algorithm,"StoredVariables"):
1101 raise ValueError("No observer can be build before choosing an algorithm.")
1102 if __V not in self.__algorithm:
1103 raise ValueError("An observer requires to be set on a variable named %s which does not exist."%__V)
1105 self.__algorithm.StoredVariables[ __V ].setDataObserver(
1108 HookParameters = __I,
1111 def removeObserver(self, __V, __O, __A = False):
1112 if self.__algorithm is None \
1113 or isinstance(self.__algorithm, dict) \
1114 or not hasattr(self.__algorithm,"StoredVariables"):
1115 raise ValueError("No observer can be removed before choosing an algorithm.")
1116 if __V not in self.__algorithm:
1117 raise ValueError("An observer requires to be removed on a variable named %s which does not exist."%__V)
1119 return self.__algorithm.StoredVariables[ __V ].removeDataObserver(
1124 def hasObserver(self, __V):
1125 if self.__algorithm is None \
1126 or isinstance(self.__algorithm, dict) \
1127 or not hasattr(self.__algorithm,"StoredVariables"):
1129 if __V not in self.__algorithm:
1131 return self.__algorithm.StoredVariables[ __V ].hasDataObserver()
1134 __allvariables = list(self.__algorithm.keys()) + list(self.__P.keys())
1135 for k in self.__variable_names_not_public:
1136 if k in __allvariables: __allvariables.remove(k)
1137 return __allvariables
1139 def __contains__(self, key=None):
1140 "D.__contains__(k) -> True if D has a key k, else False"
1141 return key in self.__algorithm or key in self.__P
1144 "x.__repr__() <==> repr(x)"
1145 return repr(self.__A)+", "+repr(self.__P)
1148 "x.__str__() <==> str(x)"
1149 return str(self.__A)+", "+str(self.__P)
1151 def __setAlgorithm(self, choice = None ):
1153 Permet de sélectionner l'algorithme à utiliser pour mener à bien l'étude
1154 d'assimilation. L'argument est un champ caractère se rapportant au nom
1155 d'un algorithme réalisant l'opération sur les arguments fixes.
1158 raise ValueError("Error: algorithm choice has to be given")
1159 if self.__algorithmName is not None:
1160 raise ValueError("Error: algorithm choice has already been done as \"%s\", it can't be changed."%self.__algorithmName)
1161 daDirectory = "daAlgorithms"
1163 # Recherche explicitement le fichier complet
1164 # ------------------------------------------
1166 for directory in sys.path:
1167 if os.path.isfile(os.path.join(directory, daDirectory, str(choice)+'.py')):
1168 module_path = os.path.abspath(os.path.join(directory, daDirectory))
1169 if module_path is None:
1170 raise ImportError("No algorithm module named \"%s\" has been found in the search path.\n The search path is %s"%(choice, sys.path))
1172 # Importe le fichier complet comme un module
1173 # ------------------------------------------
1175 sys_path_tmp = sys.path ; sys.path.insert(0,module_path)
1176 self.__algorithmFile = __import__(str(choice), globals(), locals(), [])
1177 if not hasattr(self.__algorithmFile, "ElementaryAlgorithm"):
1178 raise ImportError("this module does not define a valid elementary algorithm.")
1179 self.__algorithmName = str(choice)
1180 sys.path = sys_path_tmp ; del sys_path_tmp
1181 except ImportError as e:
1182 raise ImportError("The module named \"%s\" was found, but is incorrect at the import stage.\n The import error message is: %s"%(choice,e))
1184 # Instancie un objet du type élémentaire du fichier
1185 # -------------------------------------------------
1186 self.__algorithm = self.__algorithmFile.ElementaryAlgorithm()
1189 def __shape_validate(self):
1191 Validation de la correspondance correcte des tailles des variables et
1192 des matrices s'il y en a.
1194 if self.__Xb is None: __Xb_shape = (0,)
1195 elif hasattr(self.__Xb,"size"): __Xb_shape = (self.__Xb.size,)
1196 elif hasattr(self.__Xb,"shape"):
1197 if isinstance(self.__Xb.shape, tuple): __Xb_shape = self.__Xb.shape
1198 else: __Xb_shape = self.__Xb.shape()
1199 else: raise TypeError("The background (Xb) has no attribute of shape: problem !")
1201 if self.__Y is None: __Y_shape = (0,)
1202 elif hasattr(self.__Y,"size"): __Y_shape = (self.__Y.size,)
1203 elif hasattr(self.__Y,"shape"):
1204 if isinstance(self.__Y.shape, tuple): __Y_shape = self.__Y.shape
1205 else: __Y_shape = self.__Y.shape()
1206 else: raise TypeError("The observation (Y) has no attribute of shape: problem !")
1208 if self.__U is None: __U_shape = (0,)
1209 elif hasattr(self.__U,"size"): __U_shape = (self.__U.size,)
1210 elif hasattr(self.__U,"shape"):
1211 if isinstance(self.__U.shape, tuple): __U_shape = self.__U.shape
1212 else: __U_shape = self.__U.shape()
1213 else: raise TypeError("The control (U) has no attribute of shape: problem !")
1215 if self.__B is None: __B_shape = (0,0)
1216 elif hasattr(self.__B,"shape"):
1217 if isinstance(self.__B.shape, tuple): __B_shape = self.__B.shape
1218 else: __B_shape = self.__B.shape()
1219 else: raise TypeError("The a priori errors covariance matrix (B) has no attribute of shape: problem !")
1221 if self.__R is None: __R_shape = (0,0)
1222 elif hasattr(self.__R,"shape"):
1223 if isinstance(self.__R.shape, tuple): __R_shape = self.__R.shape
1224 else: __R_shape = self.__R.shape()
1225 else: raise TypeError("The observation errors covariance matrix (R) has no attribute of shape: problem !")
1227 if self.__Q is None: __Q_shape = (0,0)
1228 elif hasattr(self.__Q,"shape"):
1229 if isinstance(self.__Q.shape, tuple): __Q_shape = self.__Q.shape
1230 else: __Q_shape = self.__Q.shape()
1231 else: raise TypeError("The evolution errors covariance matrix (Q) has no attribute of shape: problem !")
1233 if len(self.__HO) == 0: __HO_shape = (0,0)
1234 elif isinstance(self.__HO, dict): __HO_shape = (0,0)
1235 elif hasattr(self.__HO["Direct"],"shape"):
1236 if isinstance(self.__HO["Direct"].shape, tuple): __HO_shape = self.__HO["Direct"].shape
1237 else: __HO_shape = self.__HO["Direct"].shape()
1238 else: raise TypeError("The observation operator (H) has no attribute of shape: problem !")
1240 if len(self.__EM) == 0: __EM_shape = (0,0)
1241 elif isinstance(self.__EM, dict): __EM_shape = (0,0)
1242 elif hasattr(self.__EM["Direct"],"shape"):
1243 if isinstance(self.__EM["Direct"].shape, tuple): __EM_shape = self.__EM["Direct"].shape
1244 else: __EM_shape = self.__EM["Direct"].shape()
1245 else: raise TypeError("The evolution model (EM) has no attribute of shape: problem !")
1247 if len(self.__CM) == 0: __CM_shape = (0,0)
1248 elif isinstance(self.__CM, dict): __CM_shape = (0,0)
1249 elif hasattr(self.__CM["Direct"],"shape"):
1250 if isinstance(self.__CM["Direct"].shape, tuple): __CM_shape = self.__CM["Direct"].shape
1251 else: __CM_shape = self.__CM["Direct"].shape()
1252 else: raise TypeError("The control model (CM) has no attribute of shape: problem !")
1254 # Vérification des conditions
1255 # ---------------------------
1256 if not( len(__Xb_shape) == 1 or min(__Xb_shape) == 1 ):
1257 raise ValueError("Shape characteristic of background (Xb) is incorrect: \"%s\"."%(__Xb_shape,))
1258 if not( len(__Y_shape) == 1 or min(__Y_shape) == 1 ):
1259 raise ValueError("Shape characteristic of observation (Y) is incorrect: \"%s\"."%(__Y_shape,))
1261 if not( min(__B_shape) == max(__B_shape) ):
1262 raise ValueError("Shape characteristic of a priori errors covariance matrix (B) is incorrect: \"%s\"."%(__B_shape,))
1263 if not( min(__R_shape) == max(__R_shape) ):
1264 raise ValueError("Shape characteristic of observation errors covariance matrix (R) is incorrect: \"%s\"."%(__R_shape,))
1265 if not( min(__Q_shape) == max(__Q_shape) ):
1266 raise ValueError("Shape characteristic of evolution errors covariance matrix (Q) is incorrect: \"%s\"."%(__Q_shape,))
1267 if not( min(__EM_shape) == max(__EM_shape) ):
1268 raise ValueError("Shape characteristic of evolution operator (EM) is incorrect: \"%s\"."%(__EM_shape,))
1270 if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and not( __HO_shape[1] == max(__Xb_shape) ):
1271 raise ValueError("Shape characteristic of observation operator (H) \"%s\" and state (X) \"%s\" are incompatible."%(__HO_shape,__Xb_shape))
1272 if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and not( __HO_shape[0] == max(__Y_shape) ):
1273 raise ValueError("Shape characteristic of observation operator (H) \"%s\" and observation (Y) \"%s\" are incompatible."%(__HO_shape,__Y_shape))
1274 if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and len(self.__B) > 0 and not( __HO_shape[1] == __B_shape[0] ):
1275 raise ValueError("Shape characteristic of observation operator (H) \"%s\" and a priori errors covariance matrix (B) \"%s\" are incompatible."%(__HO_shape,__B_shape))
1276 if len(self.__HO) > 0 and not isinstance(self.__HO, dict) and len(self.__R) > 0 and not( __HO_shape[0] == __R_shape[1] ):
1277 raise ValueError("Shape characteristic of observation operator (H) \"%s\" and observation errors covariance matrix (R) \"%s\" are incompatible."%(__HO_shape,__R_shape))
1279 if self.__B is not None and len(self.__B) > 0 and not( __B_shape[1] == max(__Xb_shape) ):
1280 if self.__algorithmName in ["EnsembleBlue",]:
1281 asPersistentVector = self.__Xb.reshape((-1,min(__B_shape)))
1282 self.__Xb = Persistence.OneVector("Background", basetype=numpy.matrix)
1283 for member in asPersistentVector:
1284 self.__Xb.store( numpy.matrix( numpy.ravel(member), numpy.float ).T )
1285 __Xb_shape = min(__B_shape)
1287 raise ValueError("Shape characteristic of a priori errors covariance matrix (B) \"%s\" and background (Xb) \"%s\" are incompatible."%(__B_shape,__Xb_shape))
1289 if self.__R is not None and len(self.__R) > 0 and not( __R_shape[1] == max(__Y_shape) ):
1290 raise ValueError("Shape characteristic of observation errors covariance matrix (R) \"%s\" and observation (Y) \"%s\" are incompatible."%(__R_shape,__Y_shape))
1292 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) ):
1293 raise ValueError("Shape characteristic of evolution model (EM) \"%s\" and state (X) \"%s\" are incompatible."%(__EM_shape,__Xb_shape))
1295 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) ):
1296 raise ValueError("Shape characteristic of control model (CM) \"%s\" and control (U) \"%s\" are incompatible."%(__CM_shape,__U_shape))
1298 if ("Bounds" in self.__P) \
1299 and (isinstance(self.__P["Bounds"], list) or isinstance(self.__P["Bounds"], tuple)) \
1300 and (len(self.__P["Bounds"]) != max(__Xb_shape)):
1301 raise ValueError("The number \"%s\" of bound pairs for the state (X) components is different of the size \"%s\" of the state itself." \
1302 %(len(self.__P["Bounds"]),max(__Xb_shape)))
1306 # ==============================================================================
1307 class RegulationAndParameters(object):
1309 Classe générale d'interface d'action pour la régulation et ses paramètres
1312 name = "GenericRegulation",
1319 self.__name = str(name)
1322 if asAlgorithm is None and asScript is not None:
1323 __Algo = Interfaces.ImportFromScript(asScript).getvalue( "Algorithm" )
1325 __Algo = asAlgorithm
1327 if asDict is None and asScript is not None:
1328 __Dict = Interfaces.ImportFromScript(asScript).getvalue( self.__name, "Parameters" )
1332 if __Dict is not None:
1333 self.__P.update( dict(__Dict) )
1335 if __Algo is not None:
1336 self.__P.update( {"Algorithm":str(__Algo)} )
1338 def get(self, key = None):
1339 "Vérifie l'existence d'une clé de variable ou de paramètres"
1341 return self.__P[key]
1345 # ==============================================================================
1346 class DataObserver(object):
1348 Classe générale d'interface de type observer
1351 name = "GenericObserver",
1363 self.__name = str(name)
1368 if onVariable is None:
1369 raise ValueError("setting an observer has to be done over a variable name or a list of variable names, not over None.")
1370 elif type(onVariable) in (tuple, list):
1371 self.__V = tuple(map( str, onVariable ))
1372 if withInfo is None:
1375 self.__I = (str(withInfo),)*len(self.__V)
1376 elif isinstance(onVariable, str):
1377 self.__V = (onVariable,)
1378 if withInfo is None:
1379 self.__I = (onVariable,)
1381 self.__I = (str(withInfo),)
1383 raise ValueError("setting an observer has to be done over a variable name or a list of variable names.")
1385 if asString is not None:
1386 __FunctionText = asString
1387 elif (asTemplate is not None) and (asTemplate in Templates.ObserverTemplates):
1388 __FunctionText = Templates.ObserverTemplates[asTemplate]
1389 elif asScript is not None:
1390 __FunctionText = Interfaces.ImportFromScript(asScript).getstring()
1393 __Function = ObserverF(__FunctionText)
1395 if asObsObject is not None:
1396 self.__O = asObsObject
1398 self.__O = __Function.getfunc()
1400 for k in range(len(self.__V)):
1403 if ename not in withAlgo:
1404 raise ValueError("An observer is asked to be set on a variable named %s which does not exist."%ename)
1406 withAlgo.setObserver(ename, self.__O, einfo, scheduledBy)
1409 "x.__repr__() <==> repr(x)"
1410 return repr(self.__V)+"\n"+repr(self.__O)
1413 "x.__str__() <==> str(x)"
1414 return str(self.__V)+"\n"+str(self.__O)
1416 # ==============================================================================
1417 class State(object):
1419 Classe générale d'interface de type état
1422 name = "GenericVector",
1424 asPersistentVector = None,
1430 toBeChecked = False,
1433 Permet de définir un vecteur :
1434 - asVector : entrée des données, comme un vecteur compatible avec le
1435 constructeur de numpy.matrix, ou "True" si entrée par script.
1436 - asPersistentVector : entrée des données, comme une série de vecteurs
1437 compatible avec le constructeur de numpy.matrix, ou comme un objet de
1438 type Persistence, ou "True" si entrée par script.
1439 - asScript : si un script valide est donné contenant une variable
1440 nommée "name", la variable est de type "asVector" (par défaut) ou
1441 "asPersistentVector" selon que l'une de ces variables est placée à
1443 - asDataFile : si un ou plusieurs fichiers valides sont donnés
1444 contenant des valeurs en colonnes, elles-mêmes nommées "colNames"
1445 (s'il n'y a pas de nom de colonne indiquée, on cherche une colonne
1446 nommée "name"), on récupère les colonnes et on les range ligne après
1447 ligne (colMajor=False, par défaut) ou colonne après colonne
1448 (colMajor=True). La variable résultante est de type "asVector" (par
1449 défaut) ou "asPersistentVector" selon que l'une de ces variables est
1452 self.__name = str(name)
1453 self.__check = bool(toBeChecked)
1457 self.__is_vector = False
1458 self.__is_series = False
1460 if asScript is not None:
1461 __Vector, __Series = None, None
1462 if asPersistentVector:
1463 __Series = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
1465 __Vector = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
1466 elif asDataFile is not None:
1467 __Vector, __Series = None, None
1468 if asPersistentVector:
1469 if colNames is not None:
1470 __Series = Interfaces.ImportFromFile(asDataFile).getvalue( colNames )[1]
1472 __Series = Interfaces.ImportFromFile(asDataFile).getvalue( [self.__name,] )[1]
1473 if bool(colMajor) and not Interfaces.ImportFromFile(asDataFile).getformat() == "application/numpy.npz":
1474 __Series = numpy.transpose(__Series)
1475 elif not bool(colMajor) and Interfaces.ImportFromFile(asDataFile).getformat() == "application/numpy.npz":
1476 __Series = numpy.transpose(__Series)
1478 if colNames is not None:
1479 __Vector = Interfaces.ImportFromFile(asDataFile).getvalue( colNames )[1]
1481 __Vector = Interfaces.ImportFromFile(asDataFile).getvalue( [self.__name,] )[1]
1483 __Vector = numpy.ravel(__Vector, order = "F")
1485 __Vector = numpy.ravel(__Vector, order = "C")
1487 __Vector, __Series = asVector, asPersistentVector
1489 if __Vector is not None:
1490 self.__is_vector = True
1491 self.__V = numpy.matrix( numpy.asmatrix(__Vector).A1, numpy.float ).T
1492 self.shape = self.__V.shape
1493 self.size = self.__V.size
1494 elif __Series is not None:
1495 self.__is_series = True
1496 if isinstance(__Series, (tuple, list, numpy.ndarray, numpy.matrix, str)):
1497 self.__V = Persistence.OneVector(self.__name, basetype=numpy.matrix)
1498 if isinstance(__Series, str): __Series = eval(__Series)
1499 for member in __Series:
1500 self.__V.store( numpy.matrix( numpy.asmatrix(member).A1, numpy.float ).T )
1503 if isinstance(self.__V.shape, (tuple, list)):
1504 self.shape = self.__V.shape
1506 self.shape = self.__V.shape()
1507 if len(self.shape) == 1:
1508 self.shape = (self.shape[0],1)
1509 self.size = self.shape[0] * self.shape[1]
1511 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)
1513 if scheduledBy is not None:
1514 self.__T = scheduledBy
1516 def getO(self, withScheduler=False):
1518 return self.__V, self.__T
1519 elif self.__T is None:
1525 "Vérification du type interne"
1526 return self.__is_vector
1529 "Vérification du type interne"
1530 return self.__is_series
1533 "x.__repr__() <==> repr(x)"
1534 return repr(self.__V)
1537 "x.__str__() <==> str(x)"
1538 return str(self.__V)
1540 # ==============================================================================
1541 class Covariance(object):
1543 Classe générale d'interface de type covariance
1546 name = "GenericCovariance",
1547 asCovariance = None,
1548 asEyeByScalar = None,
1549 asEyeByVector = None,
1552 toBeChecked = False,
1555 Permet de définir une covariance :
1556 - asCovariance : entrée des données, comme une matrice compatible avec
1557 le constructeur de numpy.matrix
1558 - asEyeByScalar : entrée des données comme un seul scalaire de variance,
1559 multiplicatif d'une matrice de corrélation identité, aucune matrice
1560 n'étant donc explicitement à donner
1561 - asEyeByVector : entrée des données comme un seul vecteur de variance,
1562 à mettre sur la diagonale d'une matrice de corrélation, aucune matrice
1563 n'étant donc explicitement à donner
1564 - asCovObject : entrée des données comme un objet python, qui a les
1565 methodes obligatoires "getT", "getI", "diag", "trace", "__add__",
1566 "__sub__", "__neg__", "__mul__", "__rmul__" et facultatives "shape",
1567 "size", "cholesky", "choleskyI", "asfullmatrix", "__repr__", "__str__"
1568 - toBeChecked : booléen indiquant si le caractère SDP de la matrice
1569 pleine doit être vérifié
1571 self.__name = str(name)
1572 self.__check = bool(toBeChecked)
1575 self.__is_scalar = False
1576 self.__is_vector = False
1577 self.__is_matrix = False
1578 self.__is_object = False
1580 if asScript is not None:
1581 __Matrix, __Scalar, __Vector, __Object = None, None, None, None
1583 __Scalar = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
1585 __Vector = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
1587 __Object = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
1589 __Matrix = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
1591 __Matrix, __Scalar, __Vector, __Object = asCovariance, asEyeByScalar, asEyeByVector, asCovObject
1593 if __Scalar is not None:
1594 if numpy.matrix(__Scalar).size != 1:
1595 raise ValueError(' The diagonal multiplier given to define a sparse matrix is not a unique scalar value.\n Its actual measured size is %i. Please check your scalar input.'%numpy.matrix(__Scalar).size)
1596 self.__is_scalar = True
1597 self.__C = numpy.abs( float(__Scalar) )
1600 elif __Vector is not None:
1601 self.__is_vector = True
1602 self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(__Vector, float ) ) ) )
1603 self.shape = (self.__C.size,self.__C.size)
1604 self.size = self.__C.size**2
1605 elif __Matrix is not None:
1606 self.__is_matrix = True
1607 self.__C = numpy.matrix( __Matrix, float )
1608 self.shape = self.__C.shape
1609 self.size = self.__C.size
1610 elif __Object is not None:
1611 self.__is_object = True
1613 for at in ("getT","getI","diag","trace","__add__","__sub__","__neg__","__mul__","__rmul__"):
1614 if not hasattr(self.__C,at):
1615 raise ValueError("The matrix given for %s as an object has no attribute \"%s\". Please check your object input."%(self.__name,at))
1616 if hasattr(self.__C,"shape"):
1617 self.shape = self.__C.shape
1620 if hasattr(self.__C,"size"):
1621 self.size = self.__C.size
1626 # raise ValueError("The %s covariance matrix has to be specified either as a matrix, a vector for its diagonal or a scalar multiplying an identity matrix."%self.__name)
1630 def __validate(self):
1632 if self.__C is None:
1633 raise UnboundLocalError("%s covariance matrix value has not been set!"%(self.__name,))
1634 if self.ismatrix() and min(self.shape) != max(self.shape):
1635 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))
1636 if self.isobject() and min(self.shape) != max(self.shape):
1637 raise ValueError("The matrix given for \"%s\" is not a square one, its shape is %s. Please check your object input."%(self.__name,self.shape))
1638 if self.isscalar() and self.__C <= 0:
1639 raise ValueError("The \"%s\" covariance matrix is not positive-definite. Please check your scalar input %s."%(self.__name,self.__C))
1640 if self.isvector() and (self.__C <= 0).any():
1641 raise ValueError("The \"%s\" covariance matrix is not positive-definite. Please check your vector input."%(self.__name,))
1642 if self.ismatrix() and (self.__check or logging.getLogger().level < logging.WARNING):
1644 L = numpy.linalg.cholesky( self.__C )
1646 raise ValueError("The %s covariance matrix is not symmetric positive-definite. Please check your matrix input."%(self.__name,))
1647 if self.isobject() and (self.__check or logging.getLogger().level < logging.WARNING):
1649 L = self.__C.cholesky()
1651 raise ValueError("The %s covariance object is not symmetric positive-definite. Please check your matrix input."%(self.__name,))
1654 "Vérification du type interne"
1655 return self.__is_scalar
1658 "Vérification du type interne"
1659 return self.__is_vector
1662 "Vérification du type interne"
1663 return self.__is_matrix
1666 "Vérification du type interne"
1667 return self.__is_object
1672 return Covariance(self.__name+"I", asCovariance = self.__C.I )
1673 elif self.isvector():
1674 return Covariance(self.__name+"I", asEyeByVector = 1. / self.__C )
1675 elif self.isscalar():
1676 return Covariance(self.__name+"I", asEyeByScalar = 1. / self.__C )
1677 elif self.isobject():
1678 return Covariance(self.__name+"I", asCovObject = self.__C.getI() )
1680 return None # Indispensable
1685 return Covariance(self.__name+"T", asCovariance = self.__C.T )
1686 elif self.isvector():
1687 return Covariance(self.__name+"T", asEyeByVector = self.__C )
1688 elif self.isscalar():
1689 return Covariance(self.__name+"T", asEyeByScalar = self.__C )
1690 elif self.isobject():
1691 return Covariance(self.__name+"T", asCovObject = self.__C.getT() )
1694 "Décomposition de Cholesky"
1696 return Covariance(self.__name+"C", asCovariance = numpy.linalg.cholesky(self.__C) )
1697 elif self.isvector():
1698 return Covariance(self.__name+"C", asEyeByVector = numpy.sqrt( self.__C ) )
1699 elif self.isscalar():
1700 return Covariance(self.__name+"C", asEyeByScalar = numpy.sqrt( self.__C ) )
1701 elif self.isobject() and hasattr(self.__C,"cholesky"):
1702 return Covariance(self.__name+"C", asCovObject = self.__C.cholesky() )
1704 def choleskyI(self):
1705 "Inversion de la décomposition de Cholesky"
1707 return Covariance(self.__name+"H", asCovariance = numpy.linalg.cholesky(self.__C).I )
1708 elif self.isvector():
1709 return Covariance(self.__name+"H", asEyeByVector = 1.0 / numpy.sqrt( self.__C ) )
1710 elif self.isscalar():
1711 return Covariance(self.__name+"H", asEyeByScalar = 1.0 / numpy.sqrt( self.__C ) )
1712 elif self.isobject() and hasattr(self.__C,"choleskyI"):
1713 return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() )
1715 def diag(self, msize=None):
1716 "Diagonale de la matrice"
1718 return numpy.diag(self.__C)
1719 elif self.isvector():
1721 elif self.isscalar():
1723 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,))
1725 return self.__C * numpy.ones(int(msize))
1726 elif self.isobject():
1727 return self.__C.diag()
1729 def asfullmatrix(self, msize=None):
1733 elif self.isvector():
1734 return numpy.matrix( numpy.diag(self.__C), float )
1735 elif self.isscalar():
1737 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,))
1739 return numpy.matrix( self.__C * numpy.eye(int(msize)), float )
1740 elif self.isobject() and hasattr(self.__C,"asfullmatrix"):
1741 return self.__C.asfullmatrix()
1743 def trace(self, msize=None):
1744 "Trace de la matrice"
1746 return numpy.trace(self.__C)
1747 elif self.isvector():
1748 return float(numpy.sum(self.__C))
1749 elif self.isscalar():
1751 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,))
1753 return self.__C * int(msize)
1754 elif self.isobject():
1755 return self.__C.trace()
1761 "x.__repr__() <==> repr(x)"
1762 return repr(self.__C)
1765 "x.__str__() <==> str(x)"
1766 return str(self.__C)
1768 def __add__(self, other):
1769 "x.__add__(y) <==> x+y"
1770 if self.ismatrix() or self.isobject():
1771 return self.__C + numpy.asmatrix(other)
1772 elif self.isvector() or self.isscalar():
1773 _A = numpy.asarray(other)
1774 _A.reshape(_A.size)[::_A.shape[1]+1] += self.__C
1775 return numpy.asmatrix(_A)
1777 def __radd__(self, other):
1778 "x.__radd__(y) <==> y+x"
1779 raise NotImplementedError("%s covariance matrix __radd__ method not available for %s type!"%(self.__name,type(other)))
1781 def __sub__(self, other):
1782 "x.__sub__(y) <==> x-y"
1783 if self.ismatrix() or self.isobject():
1784 return self.__C - numpy.asmatrix(other)
1785 elif self.isvector() or self.isscalar():
1786 _A = numpy.asarray(other)
1787 _A.reshape(_A.size)[::_A.shape[1]+1] = self.__C - _A.reshape(_A.size)[::_A.shape[1]+1]
1788 return numpy.asmatrix(_A)
1790 def __rsub__(self, other):
1791 "x.__rsub__(y) <==> y-x"
1792 raise NotImplementedError("%s covariance matrix __rsub__ method not available for %s type!"%(self.__name,type(other)))
1795 "x.__neg__() <==> -x"
1798 def __mul__(self, other):
1799 "x.__mul__(y) <==> x*y"
1800 if self.ismatrix() and isinstance(other, (int, numpy.matrix, float)):
1801 return self.__C * other
1802 elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)):
1803 if numpy.ravel(other).size == self.shape[1]: # Vecteur
1804 return self.__C * numpy.asmatrix(numpy.ravel(other)).T
1805 elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice
1806 return self.__C * numpy.asmatrix(other)
1808 raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.asmatrix(other).shape,self.__name))
1809 elif self.isvector() and isinstance(other, (list, numpy.matrix, numpy.ndarray, tuple)):
1810 if numpy.ravel(other).size == self.shape[1]: # Vecteur
1811 return numpy.asmatrix(self.__C * numpy.ravel(other)).T
1812 elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice
1813 return numpy.asmatrix((self.__C * (numpy.asarray(other).transpose())).transpose())
1815 raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.ravel(other).shape,self.__name))
1816 elif self.isscalar() and isinstance(other,numpy.matrix):
1817 return self.__C * other
1818 elif self.isscalar() and isinstance(other, (list, numpy.ndarray, tuple)):
1819 if len(numpy.asarray(other).shape) == 1 or numpy.asarray(other).shape[1] == 1 or numpy.asarray(other).shape[0] == 1:
1820 return self.__C * numpy.asmatrix(numpy.ravel(other)).T
1822 return self.__C * numpy.asmatrix(other)
1823 elif self.isobject():
1824 return self.__C.__mul__(other)
1826 raise NotImplementedError("%s covariance matrix __mul__ method not available for %s type!"%(self.__name,type(other)))
1828 def __rmul__(self, other):
1829 "x.__rmul__(y) <==> y*x"
1830 if self.ismatrix() and isinstance(other, (int, numpy.matrix, float)):
1831 return other * self.__C
1832 elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)):
1833 if numpy.ravel(other).size == self.shape[1]: # Vecteur
1834 return numpy.asmatrix(numpy.ravel(other)) * self.__C
1835 elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice
1836 return numpy.asmatrix(other) * self.__C
1838 raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.asmatrix(other).shape,self.shape,self.__name))
1839 elif self.isvector() and isinstance(other,numpy.matrix):
1840 if numpy.ravel(other).size == self.shape[0]: # Vecteur
1841 return numpy.asmatrix(numpy.ravel(other) * self.__C)
1842 elif numpy.asmatrix(other).shape[1] == self.shape[0]: # Matrice
1843 return numpy.asmatrix(numpy.array(other) * self.__C)
1845 raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.ravel(other).shape,self.shape,self.__name))
1846 elif self.isscalar() and isinstance(other,numpy.matrix):
1847 return other * self.__C
1848 elif self.isobject():
1849 return self.__C.__rmul__(other)
1851 raise NotImplementedError("%s covariance matrix __rmul__ method not available for %s type!"%(self.__name,type(other)))
1854 "x.__len__() <==> len(x)"
1855 return self.shape[0]
1857 # ==============================================================================
1858 class ObserverF(object):
1860 Creation d'une fonction d'observateur a partir de son texte
1862 def __init__(self, corps=""):
1863 self.__corps = corps
1864 def func(self,var,info):
1865 "Fonction d'observation"
1868 "Restitution du pointeur de fonction dans l'objet"
1871 # ==============================================================================
1872 class CaseLogger(object):
1874 Conservation des commandes de creation d'un cas
1876 def __init__(self, __name="", __objname="case", __addViewers=None, __addLoaders=None):
1877 self.__name = str(__name)
1878 self.__objname = str(__objname)
1879 self.__logSerie = []
1880 self.__switchoff = False
1882 "TUI" :Interfaces._TUIViewer,
1883 "SCD" :Interfaces._SCDViewer,
1884 "YACS":Interfaces._YACSViewer,
1887 "TUI" :Interfaces._TUIViewer,
1888 "COM" :Interfaces._COMViewer,
1890 if __addViewers is not None:
1891 self.__viewers.update(dict(__addViewers))
1892 if __addLoaders is not None:
1893 self.__loaders.update(dict(__addLoaders))
1895 def register(self, __command=None, __keys=None, __local=None, __pre=None, __switchoff=False):
1896 "Enregistrement d'une commande individuelle"
1897 if __command is not None and __keys is not None and __local is not None and not self.__switchoff:
1898 if "self" in __keys: __keys.remove("self")
1899 self.__logSerie.append( (str(__command), __keys, __local, __pre, __switchoff) )
1901 self.__switchoff = True
1903 self.__switchoff = False
1905 def dump(self, __filename=None, __format="TUI", __upa=""):
1906 "Restitution normalisée des commandes"
1907 if __format in self.__viewers:
1908 __formater = self.__viewers[__format](self.__name, self.__objname, self.__logSerie)
1910 raise ValueError("Dumping as \"%s\" is not available"%__format)
1911 return __formater.dump(__filename, __upa)
1913 def load(self, __filename=None, __content=None, __object=None, __format="TUI"):
1914 "Chargement normalisé des commandes"
1915 if __format in self.__loaders:
1916 __formater = self.__loaders[__format]()
1918 raise ValueError("Loading as \"%s\" is not available"%__format)
1919 return __formater.load(__filename, __content, __object)
1921 # ==============================================================================
1924 _extraArguments = None,
1925 _sFunction = lambda x: x,
1930 Pour une liste ordonnée de vecteurs en entrée, renvoie en sortie la liste
1931 correspondante de valeurs de la fonction en argument
1933 # Vérifications et définitions initiales
1934 # logging.debug("MULTF Internal multifonction calculations begin with function %s"%(_sFunction.__name__,))
1935 if not PlatformInfo.isIterable( __xserie ):
1936 raise TypeError("MultiFonction not iterable unkown input type: %s"%(type(__xserie),))
1938 if (_mpWorkers is None) or (_mpWorkers is not None and _mpWorkers < 1):
1941 __mpWorkers = int(_mpWorkers)
1943 import multiprocessing
1954 if _extraArguments is None:
1956 elif _extraArguments is not None and isinstance(_extraArguments, (list, tuple, map)):
1957 for __xvalue in __xserie:
1958 _jobs.append( [__xvalue, ] + list(_extraArguments) )
1960 raise TypeError("MultiFonction extra arguments unkown input type: %s"%(type(_extraArguments),))
1961 # logging.debug("MULTF Internal multiprocessing calculations begin : evaluation of %i point(s)"%(len(_jobs),))
1962 import multiprocessing
1963 with multiprocessing.Pool(__mpWorkers) as pool:
1964 __multiHX = pool.map( _sFunction, _jobs )
1967 # logging.debug("MULTF Internal multiprocessing calculation end")
1969 # logging.debug("MULTF Internal monoprocessing calculation begin")
1971 if _extraArguments is None:
1972 for __xvalue in __xserie:
1973 __multiHX.append( _sFunction( __xvalue ) )
1974 elif _extraArguments is not None and isinstance(_extraArguments, (list, tuple, map)):
1975 for __xvalue in __xserie:
1976 __multiHX.append( _sFunction( __xvalue, *_extraArguments ) )
1977 elif _extraArguments is not None and isinstance(_extraArguments, dict):
1978 for __xvalue in __xserie:
1979 __multiHX.append( _sFunction( __xvalue, **_extraArguments ) )
1981 raise TypeError("MultiFonction extra arguments unkown input type: %s"%(type(_extraArguments),))
1982 # logging.debug("MULTF Internal monoprocessing calculation end")
1984 # logging.debug("MULTF Internal multifonction calculations end")
1987 # ==============================================================================
1988 def CostFunction3D(_x,
1989 _Hm = None, # Pour simuler Hm(x) : HO["Direct"].appliedTo
1990 _HmX = None, # Simulation déjà faite de Hm(x)
1991 _arg = None, # Arguments supplementaires pour Hm, sous la forme d'un tuple
1996 _SIV = False, # A résorber pour la 8.0
1997 _SSC = [], # self._parameters["StoreSupplementaryCalculations"]
1998 _nPS = 0, # nbPreviousSteps
1999 _QM = "DA", # QualityMeasure
2000 _SSV = {}, # Entrée et/ou sortie : self.StoredVariables
2001 _fRt = False, # Restitue ou pas la sortie étendue
2002 _sSc = True, # Stocke ou pas les SSC
2005 Fonction-coût générale utile pour les algorithmes statiques/3D : 3DVAR, BLUE
2006 et dérivés, Kalman et dérivés, LeastSquares, SamplingTest, PSO, SA, Tabu,
2007 DFO, QuantileRegression
2013 for k in ["CostFunctionJ",
2019 "SimulatedObservationAtCurrentOptimum",
2020 "SimulatedObservationAtCurrentState",
2024 if hasattr(_SSV[k],"store"):
2025 _SSV[k].append = _SSV[k].store # Pour utiliser "append" au lieu de "store"
2027 _X = numpy.asmatrix(numpy.ravel( _x )).T
2028 if _SIV or "CurrentState" in _SSC or "CurrentOptimum" in _SSC:
2029 _SSV["CurrentState"].append( _X )
2031 if _HmX is not None:
2035 raise ValueError("COSTFUNCTION3D Operator has to be defined.")
2039 _HX = _Hm( _X, *_arg )
2040 _HX = numpy.asmatrix(numpy.ravel( _HX )).T
2042 if "SimulatedObservationAtCurrentState" in _SSC or \
2043 "SimulatedObservationAtCurrentOptimum" in _SSC:
2044 _SSV["SimulatedObservationAtCurrentState"].append( _HX )
2046 if numpy.any(numpy.isnan(_HX)):
2047 Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan
2049 _Y = numpy.asmatrix(numpy.ravel( _Y )).T
2050 if _QM in ["AugmentedWeightedLeastSquares", "AWLS", "AugmentedPonderatedLeastSquares", "APLS", "DA"]:
2051 if _BI is None or _RI is None:
2052 raise ValueError("Background and Observation error covariance matrix has to be properly defined!")
2053 _Xb = numpy.asmatrix(numpy.ravel( _Xb )).T
2054 Jb = 0.5 * (_X - _Xb).T * _BI * (_X - _Xb)
2055 Jo = 0.5 * (_Y - _HX).T * _RI * (_Y - _HX)
2056 elif _QM in ["WeightedLeastSquares", "WLS", "PonderatedLeastSquares", "PLS"]:
2058 raise ValueError("Observation error covariance matrix has to be properly defined!")
2060 Jo = 0.5 * (_Y - _HX).T * _RI * (_Y - _HX)
2061 elif _QM in ["LeastSquares", "LS", "L2"]:
2063 Jo = 0.5 * (_Y - _HX).T * (_Y - _HX)
2064 elif _QM in ["AbsoluteValue", "L1"]:
2066 Jo = numpy.sum( numpy.abs(_Y - _HX) )
2067 elif _QM in ["MaximumError", "ME"]:
2069 Jo = numpy.max( numpy.abs(_Y - _HX) )
2070 elif _QM in ["QR", "Null"]:
2074 raise ValueError("Unknown asked quality measure!")
2076 J = float( Jb ) + float( Jo )
2079 _SSV["CostFunctionJb"].append( Jb )
2080 _SSV["CostFunctionJo"].append( Jo )
2081 _SSV["CostFunctionJ" ].append( J )
2083 if "IndexOfOptimum" in _SSC or \
2084 "CurrentOptimum" in _SSC or \
2085 "SimulatedObservationAtCurrentOptimum" in _SSC:
2086 IndexMin = numpy.argmin( _SSV["CostFunctionJ"][_nPS:] ) + _nPS
2087 if "IndexOfOptimum" in _SSC:
2088 _SSV["IndexOfOptimum"].append( IndexMin )
2089 if "CurrentOptimum" in _SSC:
2090 _SSV["CurrentOptimum"].append( _SSV["CurrentState"][IndexMin] )
2091 if "SimulatedObservationAtCurrentOptimum" in _SSC:
2092 _SSV["SimulatedObservationAtCurrentOptimum"].append( _SSV["SimulatedObservationAtCurrentState"][IndexMin] )
2097 if _QM in ["QR"]: # Pour le QuantileRegression
2102 # ==============================================================================
2103 if __name__ == "__main__":
2104 print('\n AUTODIAGNOSTIC\n')