src/daComposant/daCore/NumericObjects.py

   1 # -*- coding: utf-8 -*-
   2 #
   3 # Copyright (C) 2008-2021 EDF R&D
   4 #
   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.
   9 #
  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.
  14 #
  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
  18 #
  19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
  20 #
  21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
  22
  23 __doc__ = """
  24     Définit les objets numériques génériques.
  25 """
  26 __author__ = "Jean-Philippe ARGAUD"
  27
  28 import os, time, copy, types, sys, logging
  29 import math, numpy, scipy, scipy.optimize
  30 from daCore.BasicObjects import Operator
  31 from daCore.PlatformInfo import PlatformInfo
  32 mpr = PlatformInfo().MachinePrecision()
  33 mfp = PlatformInfo().MaximumPrecision()
  34 # logging.getLogger().setLevel(logging.DEBUG)
  35
  36 # ==============================================================================
  37 def ExecuteFunction( paire ):
  38     assert len(paire) == 2, "Incorrect number of arguments"
  39     X, funcrepr = paire
  40     __X = numpy.asmatrix(numpy.ravel( X )).T
  41     __sys_path_tmp = sys.path ; sys.path.insert(0,funcrepr["__userFunction__path"])
  42     __module = __import__(funcrepr["__userFunction__modl"], globals(), locals(), [])
  43     __fonction = getattr(__module,funcrepr["__userFunction__name"])
  44     sys.path = __sys_path_tmp ; del __sys_path_tmp
  45     __HX  = __fonction( __X )
  46     return numpy.ravel( __HX )
  47
  48 # ==============================================================================
  49 class FDApproximation(object):
  50     """
  51     Cette classe sert d'interface pour définir les opérateurs approximés. A la
  52     création d'un objet, en fournissant une fonction "Function", on obtient un
  53     objet qui dispose de 3 méthodes "DirectOperator", "TangentOperator" et
  54     "AdjointOperator". On contrôle l'approximation DF avec l'incrément
  55     multiplicatif "increment" valant par défaut 1%, ou avec l'incrément fixe
  56     "dX" qui sera multiplié par "increment" (donc en %), et on effectue de DF
  57     centrées si le booléen "centeredDF" est vrai.
  58     """
  59     def __init__(self,
  60             name                  = "FDApproximation",
  61             Function              = None,
  62             centeredDF            = False,
  63             increment             = 0.01,
  64             dX                    = None,
  65             avoidingRedundancy    = True,
  66             toleranceInRedundancy = 1.e-18,
  67             lenghtOfRedundancy    = -1,
  68             mpEnabled             = False,
  69             mpWorkers             = None,
  70             mfEnabled             = False,
  71             ):
  72         self.__name = str(name)
  73         if mpEnabled:
  74             try:
  75                 import multiprocessing
  76                 self.__mpEnabled = True
  77             except ImportError:
  78                 self.__mpEnabled = False
  79         else:
  80             self.__mpEnabled = False
  81         self.__mpWorkers = mpWorkers
  82         if self.__mpWorkers is not None and self.__mpWorkers < 1:
  83             self.__mpWorkers = None
  84         logging.debug("FDA Calculs en multiprocessing : %s (nombre de processus : %s)"%(self.__mpEnabled,self.__mpWorkers))
  85         #
  86         if mfEnabled:
  87             self.__mfEnabled = True
  88         else:
  89             self.__mfEnabled = False
  90         logging.debug("FDA Calculs en multifonctions : %s"%(self.__mfEnabled,))
  91         #
  92         if avoidingRedundancy:
  93             self.__avoidRC = True
  94             self.__tolerBP = float(toleranceInRedundancy)
  95             self.__lenghtRJ = int(lenghtOfRedundancy)
  96             self.__listJPCP = [] # Jacobian Previous Calculated Points
  97             self.__listJPCI = [] # Jacobian Previous Calculated Increment
  98             self.__listJPCR = [] # Jacobian Previous Calculated Results
  99             self.__listJPPN = [] # Jacobian Previous Calculated Point Norms
 100             self.__listJPIN = [] # Jacobian Previous Calculated Increment Norms
 101         else:
 102             self.__avoidRC = False
 103         #
 104         if self.__mpEnabled:
 105             if isinstance(Function,types.FunctionType):
 106                 logging.debug("FDA Calculs en multiprocessing : FunctionType")
 107                 self.__userFunction__name = Function.__name__
 108                 try:
 109                     mod = os.path.join(Function.__globals__['filepath'],Function.__globals__['filename'])
 110                 except:
 111                     mod = os.path.abspath(Function.__globals__['__file__'])
 112                 if not os.path.isfile(mod):
 113                     raise ImportError("No user defined function or method found with the name %s"%(mod,))
 114                 self.__userFunction__modl = os.path.basename(mod).replace('.pyc','').replace('.pyo','').replace('.py','')
 115                 self.__userFunction__path = os.path.dirname(mod)
 116                 del mod
 117                 self.__userOperator = Operator( name = self.__name, fromMethod = Function, avoidingRedundancy = self.__avoidRC, inputAsMultiFunction = self.__mfEnabled )
 118                 self.__userFunction = self.__userOperator.appliedTo # Pour le calcul Direct
 119             elif isinstance(Function,types.MethodType):
 120                 logging.debug("FDA Calculs en multiprocessing : MethodType")
 121                 self.__userFunction__name = Function.__name__
 122                 try:
 123                     mod = os.path.join(Function.__globals__['filepath'],Function.__globals__['filename'])
 124                 except:
 125                     mod = os.path.abspath(Function.__func__.__globals__['__file__'])
 126                 if not os.path.isfile(mod):
 127                     raise ImportError("No user defined function or method found with the name %s"%(mod,))
 128                 self.__userFunction__modl = os.path.basename(mod).replace('.pyc','').replace('.pyo','').replace('.py','')
 129                 self.__userFunction__path = os.path.dirname(mod)
 130                 del mod
 131                 self.__userOperator = Operator( name = self.__name, fromMethod = Function, avoidingRedundancy = self.__avoidRC, inputAsMultiFunction = self.__mfEnabled )
 132                 self.__userFunction = self.__userOperator.appliedTo # Pour le calcul Direct
 133             else:
 134                 raise TypeError("User defined function or method has to be provided for finite differences approximation.")
 135         else:
 136             self.__userOperator = Operator( name = self.__name, fromMethod = Function, avoidingRedundancy = self.__avoidRC, inputAsMultiFunction = self.__mfEnabled )
 137             self.__userFunction = self.__userOperator.appliedTo
 138         #
 139         self.__centeredDF = bool(centeredDF)
 140         if abs(float(increment)) > 1.e-15:
 141             self.__increment  = float(increment)
 142         else:
 143             self.__increment  = 0.01
 144         if dX is None:
 145             self.__dX     = None
 146         else:
 147             self.__dX     = numpy.asmatrix(numpy.ravel( dX )).T
 148         logging.debug("FDA Reduction des doublons de calcul : %s"%self.__avoidRC)
 149         if self.__avoidRC:
 150             logging.debug("FDA Tolerance de determination des doublons : %.2e"%self.__tolerBP)
 151
 152     # ---------------------------------------------------------
 153     def __doublon__(self, e, l, n, v=None):
 154         __ac, __iac = False, -1
 155         for i in range(len(l)-1,-1,-1):
 156             if numpy.linalg.norm(e - l[i]) < self.__tolerBP * n[i]:
 157                 __ac, __iac = True, i
 158                 if v is not None: logging.debug("FDA Cas%s déja calculé, récupération du doublon %i"%(v,__iac))
 159                 break
 160         return __ac, __iac
 161
 162     # ---------------------------------------------------------
 163     def DirectOperator(self, X ):
 164         """
 165         Calcul du direct à l'aide de la fonction fournie.
 166         """
 167         logging.debug("FDA Calcul DirectOperator (explicite)")
 168         if self.__mfEnabled:
 169             _HX = self.__userFunction( X, argsAsSerie = True )
 170         else:
 171             _X = numpy.asmatrix(numpy.ravel( X )).T
 172             _HX = numpy.ravel(self.__userFunction( _X ))
 173         #
 174         return _HX
 175
 176     # ---------------------------------------------------------
 177     def TangentMatrix(self, X ):
 178         """
 179         Calcul de l'opérateur tangent comme la Jacobienne par différences finies,
 180         c'est-à-dire le gradient de H en X. On utilise des différences finies
 181         directionnelles autour du point X. X est un numpy.matrix.
 182
 183         Différences finies centrées (approximation d'ordre 2):
 184         1/ Pour chaque composante i de X, on ajoute et on enlève la perturbation
 185            dX[i] à la  composante X[i], pour composer X_plus_dXi et X_moins_dXi, et
 186            on calcule les réponses HX_plus_dXi = H( X_plus_dXi ) et HX_moins_dXi =
 187            H( X_moins_dXi )
 188         2/ On effectue les différences (HX_plus_dXi-HX_moins_dXi) et on divise par
 189            le pas 2*dXi
 190         3/ Chaque résultat, par composante, devient une colonne de la Jacobienne
 191
 192         Différences finies non centrées (approximation d'ordre 1):
 193         1/ Pour chaque composante i de X, on ajoute la perturbation dX[i] à la
 194            composante X[i] pour composer X_plus_dXi, et on calcule la réponse
 195            HX_plus_dXi = H( X_plus_dXi )
 196         2/ On calcule la valeur centrale HX = H(X)
 197         3/ On effectue les différences (HX_plus_dXi-HX) et on divise par
 198            le pas dXi
 199         4/ Chaque résultat, par composante, devient une colonne de la Jacobienne
 200
 201         """
 202         logging.debug("FDA Début du calcul de la Jacobienne")
 203         logging.debug("FDA   Incrément de............: %s*X"%float(self.__increment))
 204         logging.debug("FDA   Approximation centrée...: %s"%(self.__centeredDF))
 205         #
 206         if X is None or len(X)==0:
 207             raise ValueError("Nominal point X for approximate derivatives can not be None or void (given X: %s)."%(str(X),))
 208         #
 209         _X = numpy.asmatrix(numpy.ravel( X )).T
 210         #
 211         if self.__dX is None:
 212             _dX  = self.__increment * _X
 213         else:
 214             _dX = numpy.asmatrix(numpy.ravel( self.__dX )).T
 215         #
 216         if (_dX == 0.).any():
 217             moyenne = _dX.mean()
 218             if moyenne == 0.:
 219                 _dX = numpy.where( _dX == 0., float(self.__increment), _dX )
 220             else:
 221                 _dX = numpy.where( _dX == 0., moyenne, _dX )
 222         #
 223         __alreadyCalculated  = False
 224         if self.__avoidRC:
 225             __bidon, __alreadyCalculatedP = self.__doublon__(_X,  self.__listJPCP, self.__listJPPN, None)
 226             __bidon, __alreadyCalculatedI = self.__doublon__(_dX, self.__listJPCI, self.__listJPIN, None)
 227             if __alreadyCalculatedP == __alreadyCalculatedI > -1:
 228                 __alreadyCalculated, __i = True, __alreadyCalculatedP
 229                 logging.debug("FDA Cas J déja calculé, récupération du doublon %i"%__i)
 230         #
 231         if __alreadyCalculated:
 232             logging.debug("FDA   Calcul Jacobienne (par récupération du doublon %i)"%__i)
 233             _Jacobienne = self.__listJPCR[__i]
 234         else:
 235             logging.debug("FDA   Calcul Jacobienne (explicite)")
 236             if self.__centeredDF:
 237                 #
 238                 if self.__mpEnabled and not self.__mfEnabled:
 239                     funcrepr = {
 240                         "__userFunction__path" : self.__userFunction__path,
 241                         "__userFunction__modl" : self.__userFunction__modl,
 242                         "__userFunction__name" : self.__userFunction__name,
 243                     }
 244                     _jobs = []
 245                     for i in range( len(_dX) ):
 246                         _dXi            = _dX[i]
 247                         _X_plus_dXi     = numpy.array( _X.A1, dtype=float )
 248                         _X_plus_dXi[i]  = _X[i] + _dXi
 249                         _X_moins_dXi    = numpy.array( _X.A1, dtype=float )
 250                         _X_moins_dXi[i] = _X[i] - _dXi
 251                         #
 252                         _jobs.append( (_X_plus_dXi,  funcrepr) )
 253                         _jobs.append( (_X_moins_dXi, funcrepr) )
 254                     #
 255                     import multiprocessing
 256                     self.__pool = multiprocessing.Pool(self.__mpWorkers)
 257                     _HX_plusmoins_dX = self.__pool.map( ExecuteFunction, _jobs )
 258                     self.__pool.close()
 259                     self.__pool.join()
 260                     #
 261                     _Jacobienne  = []
 262                     for i in range( len(_dX) ):
 263                         _Jacobienne.append( numpy.ravel( _HX_plusmoins_dX[2*i] - _HX_plusmoins_dX[2*i+1] ) / (2.*_dX[i]) )
 264                     #
 265                 elif self.__mfEnabled:
 266                     _xserie = []
 267                     for i in range( len(_dX) ):
 268                         _dXi            = _dX[i]
 269                         _X_plus_dXi     = numpy.array( _X.A1, dtype=float )
 270                         _X_plus_dXi[i]  = _X[i] + _dXi
 271                         _X_moins_dXi    = numpy.array( _X.A1, dtype=float )
 272                         _X_moins_dXi[i] = _X[i] - _dXi
 273                         #
 274                         _xserie.append( _X_plus_dXi )
 275                         _xserie.append( _X_moins_dXi )
 276                     #
 277                     _HX_plusmoins_dX = self.DirectOperator( _xserie )
 278                      #
 279                     _Jacobienne  = []
 280                     for i in range( len(_dX) ):
 281                         _Jacobienne.append( numpy.ravel( _HX_plusmoins_dX[2*i] - _HX_plusmoins_dX[2*i+1] ) / (2.*_dX[i]) )
 282                     #
 283                 else:
 284                     _Jacobienne  = []
 285                     for i in range( _dX.size ):
 286                         _dXi            = _dX[i]
 287                         _X_plus_dXi     = numpy.array( _X.A1, dtype=float )
 288                         _X_plus_dXi[i]  = _X[i] + _dXi
 289                         _X_moins_dXi    = numpy.array( _X.A1, dtype=float )
 290                         _X_moins_dXi[i] = _X[i] - _dXi
 291                         #
 292                         _HX_plus_dXi    = self.DirectOperator( _X_plus_dXi )
 293                         _HX_moins_dXi   = self.DirectOperator( _X_moins_dXi )
 294                         #
 295                         _Jacobienne.append( numpy.ravel( _HX_plus_dXi - _HX_moins_dXi ) / (2.*_dXi) )
 296                 #
 297             else:
 298                 #
 299                 if self.__mpEnabled and not self.__mfEnabled:
 300                     funcrepr = {
 301                         "__userFunction__path" : self.__userFunction__path,
 302                         "__userFunction__modl" : self.__userFunction__modl,
 303                         "__userFunction__name" : self.__userFunction__name,
 304                     }
 305                     _jobs = []
 306                     _jobs.append( (_X.A1, funcrepr) )
 307                     for i in range( len(_dX) ):
 308                         _X_plus_dXi    = numpy.array( _X.A1, dtype=float )
 309                         _X_plus_dXi[i] = _X[i] + _dX[i]
 310                         #
 311                         _jobs.append( (_X_plus_dXi, funcrepr) )
 312                     #
 313                     import multiprocessing
 314                     self.__pool = multiprocessing.Pool(self.__mpWorkers)
 315                     _HX_plus_dX = self.__pool.map( ExecuteFunction, _jobs )
 316                     self.__pool.close()
 317                     self.__pool.join()
 318                     #
 319                     _HX = _HX_plus_dX.pop(0)
 320                     #
 321                     _Jacobienne = []
 322                     for i in range( len(_dX) ):
 323                         _Jacobienne.append( numpy.ravel(( _HX_plus_dX[i] - _HX ) / _dX[i]) )
 324                     #
 325                 elif self.__mfEnabled:
 326                     _xserie = []
 327                     _xserie.append( _X.A1 )
 328                     for i in range( len(_dX) ):
 329                         _X_plus_dXi    = numpy.array( _X.A1, dtype=float )
 330                         _X_plus_dXi[i] = _X[i] + _dX[i]
 331                         #
 332                         _xserie.append( _X_plus_dXi )
 333                     #
 334                     _HX_plus_dX = self.DirectOperator( _xserie )
 335                     #
 336                     _HX = _HX_plus_dX.pop(0)
 337                     #
 338                     _Jacobienne = []
 339                     for i in range( len(_dX) ):
 340                         _Jacobienne.append( numpy.ravel(( _HX_plus_dX[i] - _HX ) / _dX[i]) )
 341                    #
 342                 else:
 343                     _Jacobienne  = []
 344                     _HX = self.DirectOperator( _X )
 345                     for i in range( _dX.size ):
 346                         _dXi            = _dX[i]
 347                         _X_plus_dXi     = numpy.array( _X.A1, dtype=float )
 348                         _X_plus_dXi[i]  = _X[i] + _dXi
 349                         #
 350                         _HX_plus_dXi = self.DirectOperator( _X_plus_dXi )
 351                         #
 352                         _Jacobienne.append( numpy.ravel(( _HX_plus_dXi - _HX ) / _dXi) )
 353                 #
 354             #
 355             _Jacobienne = numpy.asmatrix( numpy.vstack( _Jacobienne ) ).T
 356             if self.__avoidRC:
 357                 if self.__lenghtRJ < 0: self.__lenghtRJ = 2 * _X.size
 358                 while len(self.__listJPCP) > self.__lenghtRJ:
 359                     self.__listJPCP.pop(0)
 360                     self.__listJPCI.pop(0)
 361                     self.__listJPCR.pop(0)
 362                     self.__listJPPN.pop(0)
 363                     self.__listJPIN.pop(0)
 364                 self.__listJPCP.append( copy.copy(_X) )
 365                 self.__listJPCI.append( copy.copy(_dX) )
 366                 self.__listJPCR.append( copy.copy(_Jacobienne) )
 367                 self.__listJPPN.append( numpy.linalg.norm(_X) )
 368                 self.__listJPIN.append( numpy.linalg.norm(_Jacobienne) )
 369         #
 370         logging.debug("FDA Fin du calcul de la Jacobienne")
 371         #
 372         return _Jacobienne
 373
 374     # ---------------------------------------------------------
 375     def TangentOperator(self, paire ):
 376         """
 377         Calcul du tangent à l'aide de la Jacobienne.
 378         """
 379         if self.__mfEnabled:
 380             assert len(paire) == 1, "Incorrect lenght of arguments"
 381             _paire = paire[0]
 382             assert len(_paire) == 2, "Incorrect number of arguments"
 383         else:
 384             assert len(paire) == 2, "Incorrect number of arguments"
 385             _paire = paire
 386         X, dX = _paire
 387         _Jacobienne = self.TangentMatrix( X )
 388         if dX is None or len(dX) == 0:
 389             #
 390             # Calcul de la forme matricielle si le second argument est None
 391             # -------------------------------------------------------------
 392             if self.__mfEnabled: return [_Jacobienne,]
 393             else:                return _Jacobienne
 394         else:
 395             #
 396             # Calcul de la valeur linéarisée de H en X appliqué à dX
 397             # ------------------------------------------------------
 398             _dX = numpy.asmatrix(numpy.ravel( dX )).T
 399             _HtX = numpy.dot(_Jacobienne, _dX)
 400             if self.__mfEnabled: return [_HtX.A1,]
 401             else:                return _HtX.A1
 402
 403     # ---------------------------------------------------------
 404     def AdjointOperator(self, paire ):
 405         """
 406         Calcul de l'adjoint à l'aide de la Jacobienne.
 407         """
 408         if self.__mfEnabled:
 409             assert len(paire) == 1, "Incorrect lenght of arguments"
 410             _paire = paire[0]
 411             assert len(_paire) == 2, "Incorrect number of arguments"
 412         else:
 413             assert len(paire) == 2, "Incorrect number of arguments"
 414             _paire = paire
 415         X, Y = _paire
 416         _JacobienneT = self.TangentMatrix( X ).T
 417         if Y is None or len(Y) == 0:
 418             #
 419             # Calcul de la forme matricielle si le second argument est None
 420             # -------------------------------------------------------------
 421             if self.__mfEnabled: return [_JacobienneT,]
 422             else:                return _JacobienneT
 423         else:
 424             #
 425             # Calcul de la valeur de l'adjoint en X appliqué à Y
 426             # --------------------------------------------------
 427             _Y = numpy.asmatrix(numpy.ravel( Y )).T
 428             _HaY = numpy.dot(_JacobienneT, _Y)
 429             if self.__mfEnabled: return [_HaY.A1,]
 430             else:                return _HaY.A1
 431
 432 # ==============================================================================
 433 def mmqr(
 434         func     = None,
 435         x0       = None,
 436         fprime   = None,
 437         bounds   = None,
 438         quantile = 0.5,
 439         maxfun   = 15000,
 440         toler    = 1.e-06,
 441         y        = None,
 442         ):
 443     """
 444     Implémentation informatique de l'algorithme MMQR, basée sur la publication :
 445     David R. Hunter, Kenneth Lange, "Quantile Regression via an MM Algorithm",
 446     Journal of Computational and Graphical Statistics, 9, 1, pp.60-77, 2000.
 447     """
 448     #
 449     # Recuperation des donnees et informations initiales
 450     # --------------------------------------------------
 451     variables = numpy.ravel( x0 )
 452     mesures   = numpy.ravel( y )
 453     increment = sys.float_info[0]
 454     p         = variables.size
 455     n         = mesures.size
 456     quantile  = float(quantile)
 457     #
 458     # Calcul des parametres du MM
 459     # ---------------------------
 460     tn      = float(toler) / n
 461     e0      = -tn / math.log(tn)
 462     epsilon = (e0-tn)/(1+math.log(e0))
 463     #
 464     # Calculs d'initialisation
 465     # ------------------------
 466     residus  = mesures - numpy.ravel( func( variables ) )
 467     poids    = 1./(epsilon+numpy.abs(residus))
 468     veps     = 1. - 2. * quantile - residus * poids
 469     lastsurrogate = -numpy.sum(residus*veps) - (1.-2.*quantile)*numpy.sum(residus)
 470     iteration = 0
 471     #
 472     # Recherche iterative
 473     # -------------------
 474     while (increment > toler) and (iteration < maxfun) :
 475         iteration += 1
 476         #
 477         Derivees  = numpy.array(fprime(variables))
 478         Derivees  = Derivees.reshape(n,p) # Necessaire pour remettre en place la matrice si elle passe par des tuyaux YACS
 479         DeriveesT = Derivees.transpose()
 480         M         =   numpy.dot( DeriveesT , (numpy.array(numpy.matrix(p*[poids,]).T)*Derivees) )
 481         SM        =   numpy.transpose(numpy.dot( DeriveesT , veps ))
 482         step      = - numpy.linalg.lstsq( M, SM, rcond=-1 )[0]
 483         #
 484         variables = variables + step
 485         if bounds is not None:
 486             # Attention : boucle infinie à éviter si un intervalle est trop petit
 487             while( (variables < numpy.ravel(numpy.asmatrix(bounds)[:,0])).any() or (variables > numpy.ravel(numpy.asmatrix(bounds)[:,1])).any() ):
 488                 step      = step/2.
 489                 variables = variables - step
 490         residus   = mesures - numpy.ravel( func(variables) )
 491         surrogate = numpy.sum(residus**2 * poids) + (4.*quantile-2.) * numpy.sum(residus)
 492         #
 493         while ( (surrogate > lastsurrogate) and ( max(list(numpy.abs(step))) > 1.e-16 ) ) :
 494             step      = step/2.
 495             variables = variables - step
 496             residus   = mesures - numpy.ravel( func(variables) )
 497             surrogate = numpy.sum(residus**2 * poids) + (4.*quantile-2.) * numpy.sum(residus)
 498         #
 499         increment     = lastsurrogate-surrogate
 500         poids         = 1./(epsilon+numpy.abs(residus))
 501         veps          = 1. - 2. * quantile - residus * poids
 502         lastsurrogate = -numpy.sum(residus * veps) - (1.-2.*quantile)*numpy.sum(residus)
 503     #
 504     # Mesure d'écart
 505     # --------------
 506     Ecart = quantile * numpy.sum(residus) - numpy.sum( residus[residus<0] )
 507     #
 508     return variables, Ecart, [n,p,iteration,increment,0]
 509
 510 # ==============================================================================
 511 def EnsembleOfCenteredPerturbations( _bgcenter, _bgcovariance, _nbmembers ):
 512     "Génération d'un ensemble de taille _nbmembers-1 d'états aléatoires centrés"
 513     #
 514     _bgcenter = numpy.ravel(_bgcenter)[:,None]
 515     if _nbmembers < 1:
 516         raise ValueError("Number of members has to be strictly more than 1 (given number: %s)."%(str(_nbmembers),))
 517     #
 518     if _bgcovariance is None:
 519         BackgroundEnsemble = numpy.tile( _bgcenter, _nbmembers)
 520     else:
 521         _Z = numpy.random.multivariate_normal(numpy.zeros(_bgcenter.size), _bgcovariance, size=_nbmembers).T
 522         BackgroundEnsemble = numpy.tile( _bgcenter, _nbmembers) + _Z
 523     #
 524     return BackgroundEnsemble
 525
 526 # ==============================================================================
 527 def EnsembleOfBackgroundPerturbations( _bgcenter, _bgcovariance, _nbmembers, _withSVD = True):
 528     "Génération d'un ensemble de taille _nbmembers-1 d'états aléatoires centrés"
 529     def __CenteredRandomAnomalies(Zr, N):
 530         """
 531         Génère une matrice de N anomalies aléatoires centrées sur Zr selon les
 532         notes manuscrites de MB et conforme au code de PS avec eps = -1
 533         """
 534         eps = -1
 535         Q = numpy.eye(N-1)-numpy.ones((N-1,N-1))/numpy.sqrt(N)/(numpy.sqrt(N)-eps)
 536         Q = numpy.concatenate((Q, [eps*numpy.ones(N-1)/numpy.sqrt(N)]), axis=0)
 537         R, _ = numpy.linalg.qr(numpy.random.normal(size = (N-1,N-1)))
 538         Q = numpy.dot(Q,R)
 539         Zr = numpy.dot(Q,Zr)
 540         return Zr.T
 541     #
 542     _bgcenter = numpy.ravel(_bgcenter)[:,None]
 543     if _nbmembers < 1:
 544         raise ValueError("Number of members has to be strictly more than 1 (given number: %s)."%(str(_nbmembers),))
 545     if _bgcovariance is None:
 546         BackgroundEnsemble = numpy.tile( _bgcenter, _nbmembers)
 547     else:
 548         if _withSVD:
 549             U, s, V = numpy.linalg.svd(_bgcovariance, full_matrices=False)
 550             _nbctl = _bgcenter.size
 551             if _nbmembers > _nbctl:
 552                 _Z = numpy.concatenate((numpy.dot(
 553                     numpy.diag(numpy.sqrt(s[:_nbctl])), V[:_nbctl]),
 554                     numpy.random.multivariate_normal(numpy.zeros(_nbctl),_bgcovariance,_nbmembers-1-_nbctl)), axis = 0)
 555             else:
 556                 _Z = numpy.dot(numpy.diag(numpy.sqrt(s[:_nbmembers-1])), V[:_nbmembers-1])
 557             _Zca = __CenteredRandomAnomalies(_Z, _nbmembers)
 558             BackgroundEnsemble = _bgcenter + _Zca
 559         else:
 560             if max(abs(_bgcovariance.flatten())) > 0:
 561                 _nbctl = _bgcenter.size
 562                 _Z = numpy.random.multivariate_normal(numpy.zeros(_nbctl),_bgcovariance,_nbmembers-1)
 563                 _Zca = __CenteredRandomAnomalies(_Z, _nbmembers)
 564                 BackgroundEnsemble = _bgcenter + _Zca
 565             else:
 566                 BackgroundEnsemble = numpy.tile( _bgcenter, _nbmembers)
 567     #
 568     return BackgroundEnsemble
 569
 570 # ==============================================================================
 571 def EnsembleOfAnomalies( _ensemble, _optmean = None):
 572     "Renvoie les anomalies centrées à partir d'un ensemble TailleEtat*NbMembres"
 573     if _optmean is None:
 574         Em = numpy.asarray(_ensemble).mean(axis=1, dtype=mfp).astype('float')[:,numpy.newaxis]
 575     else:
 576         Em = numpy.ravel(_optmean)[:,numpy.newaxis]
 577     #
 578     return numpy.asarray(_ensemble) - Em
 579
 580 # ==============================================================================
 581 def CovarianceInflation(
 582         InputCovOrEns,
 583         InflationType   = None,
 584         InflationFactor = None,
 585         BackgroundCov   = None,
 586         ):
 587     """
 588     Inflation applicable soit sur Pb ou Pa, soit sur les ensembles EXb ou EXa
 589
 590     Synthèse : Hunt 2007, section 2.3.5
 591     """
 592     if InflationFactor is None:
 593         return InputCovOrEns
 594     else:
 595         InflationFactor = float(InflationFactor)
 596     #
 597     if InflationType in ["MultiplicativeOnAnalysisCovariance", "MultiplicativeOnBackgroundCovariance"]:
 598         if InflationFactor < 1.:
 599             raise ValueError("Inflation factor for multiplicative inflation has to be greater or equal than 1.")
 600         if InflationFactor < 1.+mpr:
 601             return InputCovOrEns
 602         OutputCovOrEns = InflationFactor**2 * InputCovOrEns
 603     #
 604     elif InflationType in ["MultiplicativeOnAnalysisAnomalies", "MultiplicativeOnBackgroundAnomalies"]:
 605         if InflationFactor < 1.:
 606             raise ValueError("Inflation factor for multiplicative inflation has to be greater or equal than 1.")
 607         if InflationFactor < 1.+mpr:
 608             return InputCovOrEns
 609         InputCovOrEnsMean = InputCovOrEns.mean(axis=1, dtype=mfp).astype('float')
 610         OutputCovOrEns = InputCovOrEnsMean[:,numpy.newaxis] \
 611             + InflationFactor * (InputCovOrEns - InputCovOrEnsMean[:,numpy.newaxis])
 612     #
 613     elif InflationType in ["AdditiveOnAnalysisCovariance", "AdditiveOnBackgroundCovariance"]:
 614         if InflationFactor < 0.:
 615             raise ValueError("Inflation factor for additive inflation has to be greater or equal than 0.")
 616         if InflationFactor < mpr:
 617             return InputCovOrEns
 618         __n, __m = numpy.asarray(InputCovOrEns).shape
 619         if __n != __m:
 620             raise ValueError("Additive inflation can only be applied to squared (covariance) matrix.")
 621         OutputCovOrEns = (1. - InflationFactor) * InputCovOrEns + InflationFactor * numpy.eye(__n)
 622     #
 623     elif InflationType == "HybridOnBackgroundCovariance":
 624         if InflationFactor < 0.:
 625             raise ValueError("Inflation factor for hybrid inflation has to be greater or equal than 0.")
 626         if InflationFactor < mpr:
 627             return InputCovOrEns
 628         __n, __m = numpy.asarray(InputCovOrEns).shape
 629         if __n != __m:
 630             raise ValueError("Additive inflation can only be applied to squared (covariance) matrix.")
 631         if BackgroundCov is None:
 632             raise ValueError("Background covariance matrix B has to be given for hybrid inflation.")
 633         if InputCovOrEns.shape != BackgroundCov.shape:
 634             raise ValueError("Ensemble covariance matrix has to be of same size than background covariance matrix B.")
 635         OutputCovOrEns = (1. - InflationFactor) * InputCovOrEns + InflationFactor * BackgroundCov
 636     #
 637     elif InflationType == "Relaxation":
 638         raise NotImplementedError("InflationType Relaxation")
 639     #
 640     else:
 641         raise ValueError("Error in inflation type, '%s' is not a valid keyword."%InflationType)
 642     #
 643     return OutputCovOrEns
 644
 645 # ==============================================================================
 646 def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
 647     """
 648     Stochastic EnKF (Envensen 1994, Burgers 1998)
 649
 650     selfA est identique au "self" d'algorithme appelant et contient les
 651     valeurs.
 652     """
 653     if selfA._parameters["EstimationOf"] == "Parameters":
 654         selfA._parameters["StoreInternalVariables"] = True
 655     #
 656     # Opérateurs
 657     # ----------
 658     H = HO["Direct"].appliedControledFormTo
 659     #
 660     if selfA._parameters["EstimationOf"] == "State":
 661         M = EM["Direct"].appliedControledFormTo
 662     #
 663     if CM is not None and "Tangent" in CM and U is not None:
 664         Cm = CM["Tangent"].asMatrix(Xb)
 665     else:
 666         Cm = None
 667     #
 668     # Nombre de pas identique au nombre de pas d'observations
 669     # -------------------------------------------------------
 670     if hasattr(Y,"stepnumber"):
 671         duration = Y.stepnumber()
 672         __p = numpy.cumprod(Y.shape())[-1]
 673     else:
 674         duration = 2
 675         __p = numpy.array(Y).size
 676     #
 677     # Précalcul des inversions de B et R
 678     # ----------------------------------
 679     if selfA._parameters["StoreInternalVariables"] \
 680         or selfA._toStore("CostFunctionJ") \
 681         or selfA._toStore("CostFunctionJb") \
 682         or selfA._toStore("CostFunctionJo") \
 683         or selfA._toStore("CurrentOptimum") \
 684         or selfA._toStore("APosterioriCovariance"):
 685         BI = B.getI()
 686         RI = R.getI()
 687     #
 688     # Initialisation
 689     # --------------
 690     __n = Xb.size
 691     __m = selfA._parameters["NumberOfMembers"]
 692     if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(__n)
 693     else:                         Pn = B
 694     if hasattr(R,"asfullmatrix"): Rn = R.asfullmatrix(__p)
 695     else:                         Rn = R
 696     if hasattr(Q,"asfullmatrix"): Qn = Q.asfullmatrix(__n)
 697     else:                         Qn = Q
 698     Xn = EnsembleOfBackgroundPerturbations( Xb, None, __m )
 699     #
 700     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
 701         selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
 702         if selfA._toStore("APosterioriCovariance"):
 703             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
 704             covarianceXa = Pn
 705     #
 706     previousJMinimum = numpy.finfo(float).max
 707     #
 708     for step in range(duration-1):
 709         if hasattr(Y,"store"):
 710             Ynpu = numpy.ravel( Y[step+1] ).reshape((__p,-1))
 711         else:
 712             Ynpu = numpy.ravel( Y ).reshape((__p,-1))
 713         #
 714         if U is not None:
 715             if hasattr(U,"store") and len(U)>1:
 716                 Un = numpy.asmatrix(numpy.ravel( U[step] )).T
 717             elif hasattr(U,"store") and len(U)==1:
 718                 Un = numpy.asmatrix(numpy.ravel( U[0] )).T
 719             else:
 720                 Un = numpy.asmatrix(numpy.ravel( U )).T
 721         else:
 722             Un = None
 723         #
 724         if selfA._parameters["InflationType"] == "MultiplicativeOnBackgroundAnomalies":
 725             Xn = CovarianceInflation( Xn,
 726                 selfA._parameters["InflationType"],
 727                 selfA._parameters["InflationFactor"],
 728                 )
 729         #
 730         if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast
 731             EMX = M( [(Xn[:,i], Un) for i in range(__m)],
 732                 argsAsSerie = True,
 733                 returnSerieAsArrayMatrix = True )
 734             qi = numpy.random.multivariate_normal(numpy.zeros(__n), Qn, size=__m).T
 735             Xn_predicted = EMX + qi
 736             HX_predicted = H( [(Xn_predicted[:,i], Un) for i in range(__m)],
 737                 argsAsSerie = True,
 738                 returnSerieAsArrayMatrix = True )
 739             if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
 740                 Cm = Cm.reshape(__n,Un.size) # ADAO & check shape
 741                 Xn_predicted = Xn_predicted + Cm * Un
 742         elif selfA._parameters["EstimationOf"] == "Parameters": # Observation of forecast
 743             # --- > Par principe, M = Id, Q = 0
 744             Xn_predicted = Xn
 745             HX_predicted = H( [(Xn_predicted[:,i], Un) for i in range(__m)],
 746                 argsAsSerie = True,
 747                 returnSerieAsArrayMatrix = True )
 748         #
 749         # Mean of forecast and observation of forecast
 750         Xfm  = Xn_predicted.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
 751         Hfm  = HX_predicted.mean(axis=1, dtype=mfp).astype('float').reshape((__p,-1))
 752         #
 753         #--------------------------
 754         if VariantM == "KalmanFilterFormula05":
 755             PfHT, HPfHT = 0., 0.
 756             for i in range(__m):
 757                 Exfi = Xn_predicted[:,i].reshape((__n,-1)) - Xfm
 758                 Eyfi = HX_predicted[:,i].reshape((__p,-1)) - Hfm
 759                 PfHT  += Exfi * Eyfi.T
 760                 HPfHT += Eyfi * Eyfi.T
 761             PfHT  = (1./(__m-1)) * PfHT
 762             HPfHT = (1./(__m-1)) * HPfHT
 763             Kn     = PfHT * ( R + HPfHT ).I
 764             del PfHT, HPfHT
 765             #
 766             for i in range(__m):
 767                 ri = numpy.random.multivariate_normal(numpy.zeros(__p), Rn)
 768                 Xn[:,i] = numpy.ravel(Xn_predicted[:,i]) + Kn @ (numpy.ravel(Ynpu) + ri - HX_predicted[:,i])
 769         #--------------------------
 770         elif VariantM == "KalmanFilterFormula16":
 771             EpY   = EnsembleOfCenteredPerturbations(Ynpu, Rn, __m)
 772             EpYm  = EpY.mean(axis=1, dtype=mfp).astype('float').reshape((__p,-1))
 773             #
 774             EaX   = EnsembleOfAnomalies( Xn_predicted ) / numpy.sqrt(__m-1)
 775             EaY = (HX_predicted - Hfm - EpY + EpYm) / numpy.sqrt(__m-1)
 776             #
 777             Kn = EaX @ EaY.T @ numpy.linalg.inv( EaY @ EaY.T)
 778             #
 779             for i in range(__m):
 780                 Xn[:,i] = numpy.ravel(Xn_predicted[:,i]) + Kn @ (numpy.ravel(EpY[:,i]) - HX_predicted[:,i])
 781         #--------------------------
 782         else:
 783             raise ValueError("VariantM has to be chosen in the authorized methods list.")
 784         #
 785         if selfA._parameters["InflationType"] == "MultiplicativeOnAnalysisAnomalies":
 786             Xn = CovarianceInflation( Xn,
 787                 selfA._parameters["InflationType"],
 788                 selfA._parameters["InflationFactor"],
 789                 )
 790         #
 791         Xa = Xn.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
 792         #--------------------------
 793         #
 794         if selfA._parameters["StoreInternalVariables"] \
 795             or selfA._toStore("CostFunctionJ") \
 796             or selfA._toStore("CostFunctionJb") \
 797             or selfA._toStore("CostFunctionJo") \
 798             or selfA._toStore("APosterioriCovariance") \
 799             or selfA._toStore("InnovationAtCurrentAnalysis") \
 800             or selfA._toStore("SimulatedObservationAtCurrentAnalysis") \
 801             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
 802             _HXa = numpy.asmatrix(numpy.ravel( H((Xa, Un)) )).T
 803             _Innovation = Ynpu - _HXa
 804         #
 805         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
 806         # ---> avec analysis
 807         selfA.StoredVariables["Analysis"].store( Xa )
 808         if selfA._toStore("SimulatedObservationAtCurrentAnalysis"):
 809             selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( _HXa )
 810         if selfA._toStore("InnovationAtCurrentAnalysis"):
 811             selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation )
 812         # ---> avec current state
 813         if selfA._parameters["StoreInternalVariables"] \
 814             or selfA._toStore("CurrentState"):
 815             selfA.StoredVariables["CurrentState"].store( Xn )
 816         if selfA._toStore("ForecastState"):
 817             selfA.StoredVariables["ForecastState"].store( EMX )
 818         if selfA._toStore("BMA"):
 819             selfA.StoredVariables["BMA"].store( EMX - Xa.reshape((__n,1)) )
 820         if selfA._toStore("InnovationAtCurrentState"):
 821             selfA.StoredVariables["InnovationAtCurrentState"].store( - HX_predicted + Ynpu )
 822         if selfA._toStore("SimulatedObservationAtCurrentState") \
 823             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
 824             selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( HX_predicted )
 825         # ---> autres
 826         if selfA._parameters["StoreInternalVariables"] \
 827             or selfA._toStore("CostFunctionJ") \
 828             or selfA._toStore("CostFunctionJb") \
 829             or selfA._toStore("CostFunctionJo") \
 830             or selfA._toStore("CurrentOptimum") \
 831             or selfA._toStore("APosterioriCovariance"):
 832             Jb  = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
 833             Jo  = float( 0.5 * _Innovation.T * RI * _Innovation )
 834             J   = Jb + Jo
 835             selfA.StoredVariables["CostFunctionJb"].store( Jb )
 836             selfA.StoredVariables["CostFunctionJo"].store( Jo )
 837             selfA.StoredVariables["CostFunctionJ" ].store( J )
 838             #
 839             if selfA._toStore("IndexOfOptimum") \
 840                 or selfA._toStore("CurrentOptimum") \
 841                 or selfA._toStore("CostFunctionJAtCurrentOptimum") \
 842                 or selfA._toStore("CostFunctionJbAtCurrentOptimum") \
 843                 or selfA._toStore("CostFunctionJoAtCurrentOptimum") \
 844                 or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
 845                 IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
 846             if selfA._toStore("IndexOfOptimum"):
 847                 selfA.StoredVariables["IndexOfOptimum"].store( IndexMin )
 848             if selfA._toStore("CurrentOptimum"):
 849                 selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] )
 850             if selfA._toStore("SimulatedObservationAtCurrentOptimum"):
 851                 selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] )
 852             if selfA._toStore("CostFunctionJbAtCurrentOptimum"):
 853                 selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] )
 854             if selfA._toStore("CostFunctionJoAtCurrentOptimum"):
 855                 selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] )
 856             if selfA._toStore("CostFunctionJAtCurrentOptimum"):
 857                 selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] )
 858         if selfA._toStore("APosterioriCovariance"):
 859             Eai = EnsembleOfAnomalies( Xn ) / numpy.sqrt(__m-1) # Anomalies
 860             Pn = Eai @ Eai.T
 861             Pn = 0.5 * (Pn + Pn.T)
 862             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
 863         if selfA._parameters["EstimationOf"] == "Parameters" \
 864             and J < previousJMinimum:
 865             previousJMinimum    = J
 866             XaMin               = Xa
 867             if selfA._toStore("APosterioriCovariance"):
 868                 covarianceXaMin = Pn
 869     #
 870     # Stockage final supplémentaire de l'optimum en estimation de paramètres
 871     # ----------------------------------------------------------------------
 872     if selfA._parameters["EstimationOf"] == "Parameters":
 873         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
 874         selfA.StoredVariables["Analysis"].store( XaMin )
 875         if selfA._toStore("APosterioriCovariance"):
 876             selfA.StoredVariables["APosterioriCovariance"].store( covarianceXaMin )
 877         if selfA._toStore("BMA"):
 878             selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) )
 879     #
 880     return 0
 881
 882 # ==============================================================================
 883 def etkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
 884     """
 885     Ensemble-Transform EnKF (ETKF or Deterministic EnKF: Bishop 2001, Hunt 2007)
 886
 887     selfA est identique au "self" d'algorithme appelant et contient les
 888     valeurs.
 889     """
 890     if selfA._parameters["EstimationOf"] == "Parameters":
 891         selfA._parameters["StoreInternalVariables"] = True
 892     #
 893     # Opérateurs
 894     # ----------
 895     H = HO["Direct"].appliedControledFormTo
 896     #
 897     if selfA._parameters["EstimationOf"] == "State":
 898         M = EM["Direct"].appliedControledFormTo
 899     #
 900     if CM is not None and "Tangent" in CM and U is not None:
 901         Cm = CM["Tangent"].asMatrix(Xb)
 902     else:
 903         Cm = None
 904     #
 905     # Nombre de pas identique au nombre de pas d'observations
 906     # -------------------------------------------------------
 907     if hasattr(Y,"stepnumber"):
 908         duration = Y.stepnumber()
 909         __p = numpy.cumprod(Y.shape())[-1]
 910     else:
 911         duration = 2
 912         __p = numpy.array(Y).size
 913     #
 914     # Précalcul des inversions de B et R
 915     # ----------------------------------
 916     if selfA._parameters["StoreInternalVariables"] \
 917         or selfA._toStore("CostFunctionJ") \
 918         or selfA._toStore("CostFunctionJb") \
 919         or selfA._toStore("CostFunctionJo") \
 920         or selfA._toStore("CurrentOptimum") \
 921         or selfA._toStore("APosterioriCovariance"):
 922         BI = B.getI()
 923         RI = R.getI()
 924     elif VariantM != "KalmanFilterFormula":
 925         RI = R.getI()
 926     if VariantM == "KalmanFilterFormula":
 927         RIdemi = R.choleskyI()
 928     #
 929     # Initialisation
 930     # --------------
 931     __n = Xb.size
 932     __m = selfA._parameters["NumberOfMembers"]
 933     if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(__n)
 934     else:                         Pn = B
 935     if hasattr(R,"asfullmatrix"): Rn = R.asfullmatrix(__p)
 936     else:                         Rn = R
 937     if hasattr(Q,"asfullmatrix"): Qn = Q.asfullmatrix(__n)
 938     else:                         Qn = Q
 939     Xn = EnsembleOfBackgroundPerturbations( Xb, None, __m )
 940     #
 941     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
 942         selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
 943         if selfA._toStore("APosterioriCovariance"):
 944             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
 945             covarianceXa = Pn
 946     #
 947     previousJMinimum = numpy.finfo(float).max
 948     #
 949     for step in range(duration-1):
 950         if hasattr(Y,"store"):
 951             Ynpu = numpy.ravel( Y[step+1] ).reshape((__p,-1))
 952         else:
 953             Ynpu = numpy.ravel( Y ).reshape((__p,-1))
 954         #
 955         if U is not None:
 956             if hasattr(U,"store") and len(U)>1:
 957                 Un = numpy.asmatrix(numpy.ravel( U[step] )).T
 958             elif hasattr(U,"store") and len(U)==1:
 959                 Un = numpy.asmatrix(numpy.ravel( U[0] )).T
 960             else:
 961                 Un = numpy.asmatrix(numpy.ravel( U )).T
 962         else:
 963             Un = None
 964         #
 965         if selfA._parameters["InflationType"] == "MultiplicativeOnBackgroundAnomalies":
 966             Xn = CovarianceInflation( Xn,
 967                 selfA._parameters["InflationType"],
 968                 selfA._parameters["InflationFactor"],
 969                 )
 970         #
 971         if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast
 972             EMX = M( [(Xn[:,i], Un) for i in range(__m)],
 973                 argsAsSerie = True,
 974                 returnSerieAsArrayMatrix = True )
 975             qi = numpy.random.multivariate_normal(numpy.zeros(__n), Qn, size=__m).T
 976             Xn_predicted = EMX + qi
 977             HX_predicted = H( [(Xn_predicted[:,i], Un) for i in range(__m)],
 978                 argsAsSerie = True,
 979                 returnSerieAsArrayMatrix = True )
 980             if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
 981                 Cm = Cm.reshape(__n,Un.size) # ADAO & check shape
 982                 Xn_predicted = Xn_predicted + Cm * Un
 983         elif selfA._parameters["EstimationOf"] == "Parameters": # Observation of forecast
 984             # --- > Par principe, M = Id, Q = 0
 985             Xn_predicted = Xn
 986             HX_predicted = H( [(Xn_predicted[:,i], Un) for i in range(__m)],
 987                 argsAsSerie = True,
 988                 returnSerieAsArrayMatrix = True )
 989         #
 990         # Mean of forecast and observation of forecast
 991         Xfm  = Xn_predicted.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
 992         Hfm  = HX_predicted.mean(axis=1, dtype=mfp).astype('float').reshape((__p,1))
 993         #
 994         # Anomalies
 995         EaX   = EnsembleOfAnomalies( Xn_predicted )
 996         EaHX  = numpy.array(HX_predicted - Hfm)
 997         #
 998         #--------------------------
 999         if VariantM == "KalmanFilterFormula":
1000             mS    = RIdemi * EaHX / numpy.sqrt(__m-1)
1001             delta = RIdemi * ( Ynpu - Hfm )
1002             mT    = numpy.linalg.inv( numpy.eye(__m) + mS.T @ mS )
1003             vw    = mT @ mS.transpose() @ delta
1004             #
1005             Tdemi = numpy.real(scipy.linalg.sqrtm(mT))
1006             mU    = numpy.eye(__m)
1007             #
1008             EaX   = EaX / numpy.sqrt(__m-1)
1009             Xn    = Xfm + EaX @ ( vw.reshape((__m,-1)) + numpy.sqrt(__m-1) * Tdemi @ mU )
1010         #--------------------------
1011         elif VariantM == "Variational":
1012             HXfm = H((Xfm[:,None], Un)) # Eventuellement Hfm
1013             def CostFunction(w):
1014                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1015                 _Jo = 0.5 * _A.T @ (RI * _A)
1016                 _Jb = 0.5 * (__m-1) * w.T @ w
1017                 _J  = _Jo + _Jb
1018                 return float(_J)
1019             def GradientOfCostFunction(w):
1020                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1021                 _GardJo = - EaHX.T @ (RI * _A)
1022                 _GradJb = (__m-1) * w.reshape((__m,1))
1023                 _GradJ  = _GardJo + _GradJb
1024                 return numpy.ravel(_GradJ)
1025             vw = scipy.optimize.fmin_cg(
1026                 f           = CostFunction,
1027                 x0          = numpy.zeros(__m),
1028                 fprime      = GradientOfCostFunction,
1029                 args        = (),
1030                 disp        = False,
1031                 )
1032             #
1033             Hto = EaHX.T @ (RI * EaHX)
1034             Htb = (__m-1) * numpy.eye(__m)
1035             Hta = Hto + Htb
1036             #
1037             Pta = numpy.linalg.inv( Hta )
1038             EWa = numpy.real(scipy.linalg.sqrtm((__m-1)*Pta)) # Partie imaginaire ~= 10^-18
1039             #
1040             Xn  = Xfm + EaX @ (vw[:,None] + EWa)
1041         #--------------------------
1042         elif VariantM == "FiniteSize11": # Jauge Boc2011
1043             HXfm = H((Xfm[:,None], Un)) # Eventuellement Hfm
1044             def CostFunction(w):
1045                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1046                 _Jo = 0.5 * _A.T @ (RI * _A)
1047                 _Jb = 0.5 * __m * math.log(1 + 1/__m + w.T @ w)
1048                 _J  = _Jo + _Jb
1049                 return float(_J)
1050             def GradientOfCostFunction(w):
1051                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1052                 _GardJo = - EaHX.T @ (RI * _A)
1053                 _GradJb = __m * w.reshape((__m,1)) / (1 + 1/__m + w.T @ w)
1054                 _GradJ  = _GardJo + _GradJb
1055                 return numpy.ravel(_GradJ)
1056             vw = scipy.optimize.fmin_cg(
1057                 f           = CostFunction,
1058                 x0          = numpy.zeros(__m),
1059                 fprime      = GradientOfCostFunction,
1060                 args        = (),
1061                 disp        = False,
1062                 )
1063             #
1064             Hto = EaHX.T @ (RI * EaHX)
1065             Htb = __m * \
1066                 ( (1 + 1/__m + vw.T @ vw) * numpy.eye(__m) - 2 * vw @ vw.T ) \
1067                 / (1 + 1/__m + vw.T @ vw)**2
1068             Hta = Hto + Htb
1069             #
1070             Pta = numpy.linalg.inv( Hta )
1071             EWa = numpy.real(scipy.linalg.sqrtm((__m-1)*Pta)) # Partie imaginaire ~= 10^-18
1072             #
1073             Xn  = Xfm + EaX @ (vw.reshape((__m,-1)) + EWa)
1074         #--------------------------
1075         elif VariantM == "FiniteSize15": # Jauge Boc2015
1076             HXfm = H((Xfm[:,None], Un)) # Eventuellement Hfm
1077             def CostFunction(w):
1078                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1079                 _Jo = 0.5 * _A.T * RI * _A
1080                 _Jb = 0.5 * (__m+1) * math.log(1 + 1/__m + w.T @ w)
1081                 _J  = _Jo + _Jb
1082                 return float(_J)
1083             def GradientOfCostFunction(w):
1084                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1085                 _GardJo = - EaHX.T @ (RI * _A)
1086                 _GradJb = (__m+1) * w.reshape((__m,1)) / (1 + 1/__m + w.T @ w)
1087                 _GradJ  = _GardJo + _GradJb
1088                 return numpy.ravel(_GradJ)
1089             vw = scipy.optimize.fmin_cg(
1090                 f           = CostFunction,
1091                 x0          = numpy.zeros(__m),
1092                 fprime      = GradientOfCostFunction,
1093                 args        = (),
1094                 disp        = False,
1095                 )
1096             #
1097             Hto = EaHX.T @ (RI * EaHX)
1098             Htb = (__m+1) * \
1099                 ( (1 + 1/__m + vw.T @ vw) * numpy.eye(__m) - 2 * vw @ vw.T ) \
1100                 / (1 + 1/__m + vw.T @ vw)**2
1101             Hta = Hto + Htb
1102             #
1103             Pta = numpy.linalg.inv( Hta )
1104             EWa = numpy.real(scipy.linalg.sqrtm((__m-1)*Pta)) # Partie imaginaire ~= 10^-18
1105             #
1106             Xn  = Xfm + EaX @ (vw.reshape((__m,-1)) + EWa)
1107         #--------------------------
1108         elif VariantM == "FiniteSize16": # Jauge Boc2016
1109             HXfm = H((Xfm[:,None], Un)) # Eventuellement Hfm
1110             def CostFunction(w):
1111                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1112                 _Jo = 0.5 * _A.T @ (RI * _A)
1113                 _Jb = 0.5 * (__m+1) * math.log(1 + 1/__m + w.T @ w / (__m-1))
1114                 _J  = _Jo + _Jb
1115                 return float(_J)
1116             def GradientOfCostFunction(w):
1117                 _A  = Ynpu - HXfm.reshape((__p,-1)) - (EaHX @ w).reshape((__p,-1))
1118                 _GardJo = - EaHX.T @ (RI * _A)
1119                 _GradJb = ((__m+1) / (__m-1)) * w.reshape((__m,1)) / (1 + 1/__m + w.T @ w / (__m-1))
1120                 _GradJ  = _GardJo + _GradJb
1121                 return numpy.ravel(_GradJ)
1122             vw = scipy.optimize.fmin_cg(
1123                 f           = CostFunction,
1124                 x0          = numpy.zeros(__m),
1125                 fprime      = GradientOfCostFunction,
1126                 args        = (),
1127                 disp        = False,
1128                 )
1129             #
1130             Hto = EaHX.T @ (RI * EaHX)
1131             Htb = ((__m+1) / (__m-1)) * \
1132                 ( (1 + 1/__m + vw.T @ vw / (__m-1)) * numpy.eye(__m) - 2 * vw @ vw.T / (__m-1) ) \
1133                 / (1 + 1/__m + vw.T @ vw / (__m-1))**2
1134             Hta = Hto + Htb
1135             #
1136             Pta = numpy.linalg.inv( Hta )
1137             EWa = numpy.real(scipy.linalg.sqrtm((__m-1)*Pta)) # Partie imaginaire ~= 10^-18
1138             #
1139             Xn  = Xfm + EaX @ (vw[:,None] + EWa)
1140         #--------------------------
1141         else:
1142             raise ValueError("VariantM has to be chosen in the authorized methods list.")
1143         #
1144         if selfA._parameters["InflationType"] == "MultiplicativeOnAnalysisAnomalies":
1145             Xn = CovarianceInflation( Xn,
1146                 selfA._parameters["InflationType"],
1147                 selfA._parameters["InflationFactor"],
1148                 )
1149         #
1150         Xa = Xn.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
1151         #--------------------------
1152         #
1153         if selfA._parameters["StoreInternalVariables"] \
1154             or selfA._toStore("CostFunctionJ") \
1155             or selfA._toStore("CostFunctionJb") \
1156             or selfA._toStore("CostFunctionJo") \
1157             or selfA._toStore("APosterioriCovariance") \
1158             or selfA._toStore("InnovationAtCurrentAnalysis") \
1159             or selfA._toStore("SimulatedObservationAtCurrentAnalysis") \
1160             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1161             _HXa = numpy.asmatrix(numpy.ravel( H((Xa, Un)) )).T
1162             _Innovation = Ynpu - _HXa
1163         #
1164         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
1165         # ---> avec analysis
1166         selfA.StoredVariables["Analysis"].store( Xa )
1167         if selfA._toStore("SimulatedObservationAtCurrentAnalysis"):
1168             selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( _HXa )
1169         if selfA._toStore("InnovationAtCurrentAnalysis"):
1170             selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation )
1171         # ---> avec current state
1172         if selfA._parameters["StoreInternalVariables"] \
1173             or selfA._toStore("CurrentState"):
1174             selfA.StoredVariables["CurrentState"].store( Xn )
1175         if selfA._toStore("ForecastState"):
1176             selfA.StoredVariables["ForecastState"].store( EMX )
1177         if selfA._toStore("BMA"):
1178             selfA.StoredVariables["BMA"].store( EMX - Xa.reshape((__n,1)) )
1179         if selfA._toStore("InnovationAtCurrentState"):
1180             selfA.StoredVariables["InnovationAtCurrentState"].store( - HX_predicted + Ynpu.reshape((__p,1)) )
1181         if selfA._toStore("SimulatedObservationAtCurrentState") \
1182             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1183             selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( HX_predicted )
1184         # ---> autres
1185         if selfA._parameters["StoreInternalVariables"] \
1186             or selfA._toStore("CostFunctionJ") \
1187             or selfA._toStore("CostFunctionJb") \
1188             or selfA._toStore("CostFunctionJo") \
1189             or selfA._toStore("CurrentOptimum") \
1190             or selfA._toStore("APosterioriCovariance"):
1191             Jb  = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
1192             Jo  = float( 0.5 * _Innovation.T * RI * _Innovation )
1193             J   = Jb + Jo
1194             selfA.StoredVariables["CostFunctionJb"].store( Jb )
1195             selfA.StoredVariables["CostFunctionJo"].store( Jo )
1196             selfA.StoredVariables["CostFunctionJ" ].store( J )
1197             #
1198             if selfA._toStore("IndexOfOptimum") \
1199                 or selfA._toStore("CurrentOptimum") \
1200                 or selfA._toStore("CostFunctionJAtCurrentOptimum") \
1201                 or selfA._toStore("CostFunctionJbAtCurrentOptimum") \
1202                 or selfA._toStore("CostFunctionJoAtCurrentOptimum") \
1203                 or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1204                 IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
1205             if selfA._toStore("IndexOfOptimum"):
1206                 selfA.StoredVariables["IndexOfOptimum"].store( IndexMin )
1207             if selfA._toStore("CurrentOptimum"):
1208                 selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] )
1209             if selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1210                 selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] )
1211             if selfA._toStore("CostFunctionJbAtCurrentOptimum"):
1212                 selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] )
1213             if selfA._toStore("CostFunctionJoAtCurrentOptimum"):
1214                 selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] )
1215             if selfA._toStore("CostFunctionJAtCurrentOptimum"):
1216                 selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] )
1217         if selfA._toStore("APosterioriCovariance"):
1218             Eai = EnsembleOfAnomalies( Xn ) / numpy.sqrt(__m-1) # Anomalies
1219             Pn = Eai @ Eai.T
1220             Pn = 0.5 * (Pn + Pn.T)
1221             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
1222         if selfA._parameters["EstimationOf"] == "Parameters" \
1223             and J < previousJMinimum:
1224             previousJMinimum    = J
1225             XaMin               = Xa
1226             if selfA._toStore("APosterioriCovariance"):
1227                 covarianceXaMin = Pn
1228     #
1229     # Stockage final supplémentaire de l'optimum en estimation de paramètres
1230     # ----------------------------------------------------------------------
1231     if selfA._parameters["EstimationOf"] == "Parameters":
1232         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
1233         selfA.StoredVariables["Analysis"].store( XaMin )
1234         if selfA._toStore("APosterioriCovariance"):
1235             selfA.StoredVariables["APosterioriCovariance"].store( covarianceXaMin )
1236         if selfA._toStore("BMA"):
1237             selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) )
1238     #
1239     return 0
1240
1241 # ==============================================================================
1242 def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="MLEF13",
1243     BnotT=False, _epsilon=1.e-3, _e=1.e-7, _jmax=15000):
1244     """
1245     Maximum Likelihood Ensemble Filter (EnKF/MLEF Zupanski 2005, Bocquet 2013)
1246
1247     selfA est identique au "self" d'algorithme appelant et contient les
1248     valeurs.
1249     """
1250     if selfA._parameters["EstimationOf"] == "Parameters":
1251         selfA._parameters["StoreInternalVariables"] = True
1252     #
1253     # Opérateurs
1254     # ----------
1255     H = HO["Direct"].appliedControledFormTo
1256     #
1257     if selfA._parameters["EstimationOf"] == "State":
1258         M = EM["Direct"].appliedControledFormTo
1259     #
1260     if CM is not None and "Tangent" in CM and U is not None:
1261         Cm = CM["Tangent"].asMatrix(Xb)
1262     else:
1263         Cm = None
1264     #
1265     # Nombre de pas identique au nombre de pas d'observations
1266     # -------------------------------------------------------
1267     if hasattr(Y,"stepnumber"):
1268         duration = Y.stepnumber()
1269         __p = numpy.cumprod(Y.shape())[-1]
1270     else:
1271         duration = 2
1272         __p = numpy.array(Y).size
1273     #
1274     # Précalcul des inversions de B et R
1275     # ----------------------------------
1276     if selfA._parameters["StoreInternalVariables"] \
1277         or selfA._toStore("CostFunctionJ") \
1278         or selfA._toStore("CostFunctionJb") \
1279         or selfA._toStore("CostFunctionJo") \
1280         or selfA._toStore("CurrentOptimum") \
1281         or selfA._toStore("APosterioriCovariance"):
1282         BI = B.getI()
1283     RI = R.getI()
1284     #
1285     # Initialisation
1286     # --------------
1287     __n = Xb.size
1288     __m = selfA._parameters["NumberOfMembers"]
1289     if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(__n)
1290     else:                         Pn = B
1291     if hasattr(R,"asfullmatrix"): Rn = R.asfullmatrix(__p)
1292     else:                         Rn = R
1293     if hasattr(Q,"asfullmatrix"): Qn = Q.asfullmatrix(__n)
1294     else:                         Qn = Q
1295     Xn = EnsembleOfBackgroundPerturbations( Xb, None, __m )
1296     #
1297     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
1298         selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
1299         if selfA._toStore("APosterioriCovariance"):
1300             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
1301             covarianceXa = Pn
1302     #
1303     previousJMinimum = numpy.finfo(float).max
1304     #
1305     for step in range(duration-1):
1306         if hasattr(Y,"store"):
1307             Ynpu = numpy.ravel( Y[step+1] ).reshape((__p,-1))
1308         else:
1309             Ynpu = numpy.ravel( Y ).reshape((__p,-1))
1310         #
1311         if U is not None:
1312             if hasattr(U,"store") and len(U)>1:
1313                 Un = numpy.asmatrix(numpy.ravel( U[step] )).T
1314             elif hasattr(U,"store") and len(U)==1:
1315                 Un = numpy.asmatrix(numpy.ravel( U[0] )).T
1316             else:
1317                 Un = numpy.asmatrix(numpy.ravel( U )).T
1318         else:
1319             Un = None
1320         #
1321         if selfA._parameters["InflationType"] == "MultiplicativeOnBackgroundAnomalies":
1322             Xn = CovarianceInflation( Xn,
1323                 selfA._parameters["InflationType"],
1324                 selfA._parameters["InflationFactor"],
1325                 )
1326         #
1327         if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast
1328             EMX = M( [(Xn[:,i], Un) for i in range(__m)],
1329                 argsAsSerie = True,
1330                 returnSerieAsArrayMatrix = True )
1331             qi = numpy.random.multivariate_normal(numpy.zeros(__n), Qn, size=__m).T
1332             Xn_predicted = EMX + qi
1333             if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
1334                 Cm = Cm.reshape(__n,Un.size) # ADAO & check shape
1335                 Xn_predicted = Xn_predicted + Cm * Un
1336         elif selfA._parameters["EstimationOf"] == "Parameters": # Observation of forecast
1337             # --- > Par principe, M = Id, Q = 0
1338             Xn_predicted = Xn
1339         #
1340         #--------------------------
1341         if VariantM == "MLEF13":
1342             Xfm = numpy.ravel(Xn_predicted.mean(axis=1, dtype=mfp).astype('float'))
1343             EaX = EnsembleOfAnomalies( Xn_predicted ) / numpy.sqrt(__m-1)
1344             Ua  = numpy.eye(__m)
1345             __j = 0
1346             Deltaw = 1
1347             if not BnotT:
1348                 Ta  = numpy.eye(__m)
1349             vw  = numpy.zeros(__m)
1350             while numpy.linalg.norm(Deltaw) >= _e and __j <= _jmax:
1351                 vx1 = (Xfm + EaX @ vw).reshape((__n,-1))
1352                 #
1353                 if BnotT:
1354                     E1 = vx1 + _epsilon * EaX
1355                 else:
1356                     E1 = vx1 + numpy.sqrt(__m-1) * EaX @ Ta
1357                 #
1358                 HE2 = H( [(E1[:,i,numpy.newaxis], Un) for i in range(__m)],
1359                     argsAsSerie = True,
1360                     returnSerieAsArrayMatrix = True )
1361                 vy2 = HE2.mean(axis=1, dtype=mfp).astype('float').reshape((__p,-1))
1362                 #
1363                 if BnotT:
1364                     EaY = (HE2 - vy2) / _epsilon
1365                 else:
1366                     EaY = ( (HE2 - vy2) @ numpy.linalg.inv(Ta) ) / numpy.sqrt(__m-1)
1367                 #
1368                 GradJ = numpy.ravel(vw[:,None] - EaY.transpose() @ (RI * ( Ynpu - vy2 )))
1369                 mH = numpy.eye(__m) + EaY.transpose() @ (RI * EaY)
1370                 Deltaw = - numpy.linalg.solve(mH,GradJ)
1371                 #
1372                 vw = vw + Deltaw
1373                 #
1374                 if not BnotT:
1375                     Ta = numpy.real(scipy.linalg.sqrtm(numpy.linalg.inv( mH )))
1376                 #
1377                 __j = __j + 1
1378             #
1379             if BnotT:
1380                 Ta = numpy.real(scipy.linalg.sqrtm(numpy.linalg.inv( mH )))
1381             #
1382             Xn = vx1 + numpy.sqrt(__m-1) * EaX @ Ta @ Ua
1383         #--------------------------
1384         else:
1385             raise ValueError("VariantM has to be chosen in the authorized methods list.")
1386         #
1387         if selfA._parameters["InflationType"] == "MultiplicativeOnAnalysisAnomalies":
1388             Xn = CovarianceInflation( Xn,
1389                 selfA._parameters["InflationType"],
1390                 selfA._parameters["InflationFactor"],
1391                 )
1392         #
1393         Xa = Xn.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
1394         #--------------------------
1395         #
1396         if selfA._parameters["StoreInternalVariables"] \
1397             or selfA._toStore("CostFunctionJ") \
1398             or selfA._toStore("CostFunctionJb") \
1399             or selfA._toStore("CostFunctionJo") \
1400             or selfA._toStore("APosterioriCovariance") \
1401             or selfA._toStore("InnovationAtCurrentAnalysis") \
1402             or selfA._toStore("SimulatedObservationAtCurrentAnalysis") \
1403             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1404             _HXa = numpy.asmatrix(numpy.ravel( H((Xa, Un)) )).T
1405             _Innovation = Ynpu - _HXa
1406         #
1407         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
1408         # ---> avec analysis
1409         selfA.StoredVariables["Analysis"].store( Xa )
1410         if selfA._toStore("SimulatedObservationAtCurrentAnalysis"):
1411             selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( _HXa )
1412         if selfA._toStore("InnovationAtCurrentAnalysis"):
1413             selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation )
1414         # ---> avec current state
1415         if selfA._parameters["StoreInternalVariables"] \
1416             or selfA._toStore("CurrentState"):
1417             selfA.StoredVariables["CurrentState"].store( Xn )
1418         if selfA._toStore("ForecastState"):
1419             selfA.StoredVariables["ForecastState"].store( EMX )
1420         if selfA._toStore("BMA"):
1421             selfA.StoredVariables["BMA"].store( EMX - Xa.reshape((__n,1)) )
1422         if selfA._toStore("InnovationAtCurrentState"):
1423             selfA.StoredVariables["InnovationAtCurrentState"].store( - HE2 + Ynpu.reshape((__p,-1)) )
1424         if selfA._toStore("SimulatedObservationAtCurrentState") \
1425             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1426             selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( HE2 )
1427         # ---> autres
1428         if selfA._parameters["StoreInternalVariables"] \
1429             or selfA._toStore("CostFunctionJ") \
1430             or selfA._toStore("CostFunctionJb") \
1431             or selfA._toStore("CostFunctionJo") \
1432             or selfA._toStore("CurrentOptimum") \
1433             or selfA._toStore("APosterioriCovariance"):
1434             Jb  = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
1435             Jo  = float( 0.5 * _Innovation.T * RI * _Innovation )
1436             J   = Jb + Jo
1437             selfA.StoredVariables["CostFunctionJb"].store( Jb )
1438             selfA.StoredVariables["CostFunctionJo"].store( Jo )
1439             selfA.StoredVariables["CostFunctionJ" ].store( J )
1440             #
1441             if selfA._toStore("IndexOfOptimum") \
1442                 or selfA._toStore("CurrentOptimum") \
1443                 or selfA._toStore("CostFunctionJAtCurrentOptimum") \
1444                 or selfA._toStore("CostFunctionJbAtCurrentOptimum") \
1445                 or selfA._toStore("CostFunctionJoAtCurrentOptimum") \
1446                 or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1447                 IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
1448             if selfA._toStore("IndexOfOptimum"):
1449                 selfA.StoredVariables["IndexOfOptimum"].store( IndexMin )
1450             if selfA._toStore("CurrentOptimum"):
1451                 selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] )
1452             if selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1453                 selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] )
1454             if selfA._toStore("CostFunctionJbAtCurrentOptimum"):
1455                 selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] )
1456             if selfA._toStore("CostFunctionJoAtCurrentOptimum"):
1457                 selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] )
1458             if selfA._toStore("CostFunctionJAtCurrentOptimum"):
1459                 selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] )
1460         if selfA._toStore("APosterioriCovariance"):
1461             Eai = EnsembleOfAnomalies( Xn ) / numpy.sqrt(__m-1) # Anomalies
1462             Pn = Eai @ Eai.T
1463             Pn = 0.5 * (Pn + Pn.T)
1464             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
1465         if selfA._parameters["EstimationOf"] == "Parameters" \
1466             and J < previousJMinimum:
1467             previousJMinimum    = J
1468             XaMin               = Xa
1469             if selfA._toStore("APosterioriCovariance"):
1470                 covarianceXaMin = Pn
1471     #
1472     # Stockage final supplémentaire de l'optimum en estimation de paramètres
1473     # ----------------------------------------------------------------------
1474     if selfA._parameters["EstimationOf"] == "Parameters":
1475         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
1476         selfA.StoredVariables["Analysis"].store( XaMin )
1477         if selfA._toStore("APosterioriCovariance"):
1478             selfA.StoredVariables["APosterioriCovariance"].store( covarianceXaMin )
1479         if selfA._toStore("BMA"):
1480             selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) )
1481     #
1482     return 0
1483
1484 # ==============================================================================
1485 def ienkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="IEnKF12",
1486     BnotT=False, _epsilon=1.e-3, _e=1.e-7, _jmax=15000):
1487     """
1488     Iterative EnKF (Sakov 2012, Sakov 2018)
1489
1490     selfA est identique au "self" d'algorithme appelant et contient les
1491     valeurs.
1492     """
1493     if selfA._parameters["EstimationOf"] == "Parameters":
1494         selfA._parameters["StoreInternalVariables"] = True
1495     #
1496     # Opérateurs
1497     # ----------
1498     H = HO["Direct"].appliedControledFormTo
1499     #
1500     if selfA._parameters["EstimationOf"] == "State":
1501         M = EM["Direct"].appliedControledFormTo
1502     #
1503     if CM is not None and "Tangent" in CM and U is not None:
1504         Cm = CM["Tangent"].asMatrix(Xb)
1505     else:
1506         Cm = None
1507     #
1508     # Nombre de pas identique au nombre de pas d'observations
1509     # -------------------------------------------------------
1510     if hasattr(Y,"stepnumber"):
1511         duration = Y.stepnumber()
1512         __p = numpy.cumprod(Y.shape())[-1]
1513     else:
1514         duration = 2
1515         __p = numpy.array(Y).size
1516     #
1517     # Précalcul des inversions de B et R
1518     # ----------------------------------
1519     if selfA._parameters["StoreInternalVariables"] \
1520         or selfA._toStore("CostFunctionJ") \
1521         or selfA._toStore("CostFunctionJb") \
1522         or selfA._toStore("CostFunctionJo") \
1523         or selfA._toStore("CurrentOptimum") \
1524         or selfA._toStore("APosterioriCovariance"):
1525         BI = B.getI()
1526     RI = R.getI()
1527     #
1528     # Initialisation
1529     # --------------
1530     __n = Xb.size
1531     __m = selfA._parameters["NumberOfMembers"]
1532     if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(__n)
1533     else:                         Pn = B
1534     if hasattr(R,"asfullmatrix"): Rn = R.asfullmatrix(__p)
1535     else:                         Rn = R
1536     if hasattr(Q,"asfullmatrix"): Qn = Q.asfullmatrix(__n)
1537     else:                         Qn = Q
1538     Xn = EnsembleOfBackgroundPerturbations( Xb, Pn, __m )
1539     #
1540     if len(selfA.StoredVariables["Analysis"])==0 or not selfA._parameters["nextStep"]:
1541         selfA.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
1542         if selfA._toStore("APosterioriCovariance"):
1543             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
1544             covarianceXa = Pn
1545     #
1546     previousJMinimum = numpy.finfo(float).max
1547     #
1548     for step in range(duration-1):
1549         if hasattr(Y,"store"):
1550             Ynpu = numpy.ravel( Y[step+1] ).reshape((__p,-1))
1551         else:
1552             Ynpu = numpy.ravel( Y ).reshape((__p,-1))
1553         #
1554         if U is not None:
1555             if hasattr(U,"store") and len(U)>1:
1556                 Un = numpy.asmatrix(numpy.ravel( U[step] )).T
1557             elif hasattr(U,"store") and len(U)==1:
1558                 Un = numpy.asmatrix(numpy.ravel( U[0] )).T
1559             else:
1560                 Un = numpy.asmatrix(numpy.ravel( U )).T
1561         else:
1562             Un = None
1563         #
1564         if selfA._parameters["InflationType"] == "MultiplicativeOnBackgroundAnomalies":
1565             Xn = CovarianceInflation( Xn,
1566                 selfA._parameters["InflationType"],
1567                 selfA._parameters["InflationFactor"],
1568                 )
1569         #
1570         #--------------------------
1571         if VariantM == "IEnKF12":
1572             Xfm = numpy.ravel(Xn.mean(axis=1, dtype=mfp).astype('float'))
1573             EaX = EnsembleOfAnomalies( Xn ) / numpy.sqrt(__m-1)
1574             __j = 0
1575             Deltaw = 1
1576             if not BnotT:
1577                 Ta  = numpy.eye(__m)
1578             vw  = numpy.zeros(__m)
1579             while numpy.linalg.norm(Deltaw) >= _e and __j <= _jmax:
1580                 vx1 = (Xfm + EaX @ vw).reshape((__n,-1))
1581                 #
1582                 if BnotT:
1583                     E1 = vx1 + _epsilon * EaX
1584                 else:
1585                     E1 = vx1 + numpy.sqrt(__m-1) * EaX @ Ta
1586                 #
1587                 if selfA._parameters["EstimationOf"] == "State": # Forecast + Q
1588                     E2 = M( [(E1[:,i,numpy.newaxis], Un) for i in range(__m)],
1589                         argsAsSerie = True,
1590                         returnSerieAsArrayMatrix = True )
1591                 elif selfA._parameters["EstimationOf"] == "Parameters":
1592                     # --- > Par principe, M = Id
1593                     E2 = Xn
1594                 vx2 = E2.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
1595                 vy1 = H((vx2, Un)).reshape((__p,-1))
1596                 #
1597                 HE2 = H( [(E2[:,i,numpy.newaxis], Un) for i in range(__m)],
1598                     argsAsSerie = True,
1599                     returnSerieAsArrayMatrix = True )
1600                 vy2 = HE2.mean(axis=1, dtype=mfp).astype('float').reshape((__p,-1))
1601                 #
1602                 if BnotT:
1603                     EaY = (HE2 - vy2) / _epsilon
1604                 else:
1605                     EaY = ( (HE2 - vy2) @ numpy.linalg.inv(Ta) ) / numpy.sqrt(__m-1)
1606                 #
1607                 GradJ = numpy.ravel(vw[:,None] - EaY.transpose() @ (RI * ( Ynpu - vy1 )))
1608                 mH = numpy.eye(__m) + EaY.transpose() @ (RI * EaY)
1609                 Deltaw = - numpy.linalg.solve(mH,GradJ)
1610                 #
1611                 vw = vw + Deltaw
1612                 #
1613                 if not BnotT:
1614                     Ta = numpy.real(scipy.linalg.sqrtm(numpy.linalg.inv( mH )))
1615                 #
1616                 __j = __j + 1
1617             #
1618             A2 = EnsembleOfAnomalies( E2 )
1619             #
1620             if BnotT:
1621                 Ta = numpy.real(scipy.linalg.sqrtm(numpy.linalg.inv( mH )))
1622                 A2 = numpy.sqrt(__m-1) * A2 @ Ta / _epsilon
1623             #
1624             Xn = vx2 + A2
1625         #--------------------------
1626         else:
1627             raise ValueError("VariantM has to be chosen in the authorized methods list.")
1628         #
1629         if selfA._parameters["InflationType"] == "MultiplicativeOnAnalysisAnomalies":
1630             Xn = CovarianceInflation( Xn,
1631                 selfA._parameters["InflationType"],
1632                 selfA._parameters["InflationFactor"],
1633                 )
1634         #
1635         Xa = Xn.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1))
1636         #--------------------------
1637         #
1638         if selfA._parameters["StoreInternalVariables"] \
1639             or selfA._toStore("CostFunctionJ") \
1640             or selfA._toStore("CostFunctionJb") \
1641             or selfA._toStore("CostFunctionJo") \
1642             or selfA._toStore("APosterioriCovariance") \
1643             or selfA._toStore("InnovationAtCurrentAnalysis") \
1644             or selfA._toStore("SimulatedObservationAtCurrentAnalysis") \
1645             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1646             _HXa = numpy.asmatrix(numpy.ravel( H((Xa, Un)) )).T
1647             _Innovation = Ynpu - _HXa
1648         #
1649         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
1650         # ---> avec analysis
1651         selfA.StoredVariables["Analysis"].store( Xa )
1652         if selfA._toStore("SimulatedObservationAtCurrentAnalysis"):
1653             selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"].store( _HXa )
1654         if selfA._toStore("InnovationAtCurrentAnalysis"):
1655             selfA.StoredVariables["InnovationAtCurrentAnalysis"].store( _Innovation )
1656         # ---> avec current state
1657         if selfA._parameters["StoreInternalVariables"] \
1658             or selfA._toStore("CurrentState"):
1659             selfA.StoredVariables["CurrentState"].store( Xn )
1660         if selfA._toStore("ForecastState"):
1661             selfA.StoredVariables["ForecastState"].store( E2 )
1662         if selfA._toStore("BMA"):
1663             selfA.StoredVariables["BMA"].store( E2 - Xa )
1664         if selfA._toStore("InnovationAtCurrentState"):
1665             selfA.StoredVariables["InnovationAtCurrentState"].store( - HE2 + Ynpu.reshape((__p,-1)) )
1666         if selfA._toStore("SimulatedObservationAtCurrentState") \
1667             or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1668             selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( HE2 )
1669         # ---> autres
1670         if selfA._parameters["StoreInternalVariables"] \
1671             or selfA._toStore("CostFunctionJ") \
1672             or selfA._toStore("CostFunctionJb") \
1673             or selfA._toStore("CostFunctionJo") \
1674             or selfA._toStore("CurrentOptimum") \
1675             or selfA._toStore("APosterioriCovariance"):
1676             Jb  = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
1677             Jo  = float( 0.5 * _Innovation.T * RI * _Innovation )
1678             J   = Jb + Jo
1679             selfA.StoredVariables["CostFunctionJb"].store( Jb )
1680             selfA.StoredVariables["CostFunctionJo"].store( Jo )
1681             selfA.StoredVariables["CostFunctionJ" ].store( J )
1682             #
1683             if selfA._toStore("IndexOfOptimum") \
1684                 or selfA._toStore("CurrentOptimum") \
1685                 or selfA._toStore("CostFunctionJAtCurrentOptimum") \
1686                 or selfA._toStore("CostFunctionJbAtCurrentOptimum") \
1687                 or selfA._toStore("CostFunctionJoAtCurrentOptimum") \
1688                 or selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1689                 IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
1690             if selfA._toStore("IndexOfOptimum"):
1691                 selfA.StoredVariables["IndexOfOptimum"].store( IndexMin )
1692             if selfA._toStore("CurrentOptimum"):
1693                 selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["Analysis"][IndexMin] )
1694             if selfA._toStore("SimulatedObservationAtCurrentOptimum"):
1695                 selfA.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( selfA.StoredVariables["SimulatedObservationAtCurrentAnalysis"][IndexMin] )
1696             if selfA._toStore("CostFunctionJbAtCurrentOptimum"):
1697                 selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] )
1698             if selfA._toStore("CostFunctionJoAtCurrentOptimum"):
1699                 selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] )
1700             if selfA._toStore("CostFunctionJAtCurrentOptimum"):
1701                 selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] )
1702         if selfA._toStore("APosterioriCovariance"):
1703             Eai = EnsembleOfAnomalies( Xn ) / numpy.sqrt(__m-1) # Anomalies
1704             Pn = Eai @ Eai.T
1705             Pn = 0.5 * (Pn + Pn.T)
1706             selfA.StoredVariables["APosterioriCovariance"].store( Pn )
1707         if selfA._parameters["EstimationOf"] == "Parameters" \
1708             and J < previousJMinimum:
1709             previousJMinimum    = J
1710             XaMin               = Xa
1711             if selfA._toStore("APosterioriCovariance"):
1712                 covarianceXaMin = Pn
1713     #
1714     # Stockage final supplémentaire de l'optimum en estimation de paramètres
1715     # ----------------------------------------------------------------------
1716     if selfA._parameters["EstimationOf"] == "Parameters":
1717         selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["Analysis"]) )
1718         selfA.StoredVariables["Analysis"].store( XaMin )
1719         if selfA._toStore("APosterioriCovariance"):
1720             selfA.StoredVariables["APosterioriCovariance"].store( covarianceXaMin )
1721         if selfA._toStore("BMA"):
1722             selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) )
1723     #
1724     return 0
1725
1726 # ==============================================================================
1727 if __name__ == "__main__":
1728     print('\n AUTODIAGNOSTIC\n')