# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
-from daCore import BasicObjects
+from daCore import BasicObjects, NumericObjects
import numpy, scipy.optimize, scipy.version
# ==============================================================================
message = "Prise en compte des contraintes",
listval = ["EstimateProjection"],
)
+ self.defineRequiredParameter(
+ name = "Variant",
+ default = "4DVAR",
+ typecast = str,
+ message = "Variant ou formulation de la méthode",
+ listval = [
+ "4DVAR",
+ ],
+ listadv = [
+ "4DVAR-Std",
+ ],
+ )
self.defineRequiredParameter(
name = "EstimationOf",
default = "State",
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
- # Opérateurs
- # ----------
- Hm = HO["Direct"].appliedControledFormTo
- #
- Mm = EM["Direct"].appliedControledFormTo
+ #--------------------------
+ # Default 4DVAR
+ if self._parameters["Variant"] in ["4DVAR", "4DVAR-Std"]:
+ NumericObjects.std4dvar(self, Xb, Y, U, HO, EM, CM, R, B, Q)
#
- if CM is not None and "Tangent" in CM and U is not None:
- Cm = CM["Tangent"].asMatrix(Xb)
+ #--------------------------
else:
- Cm = None
- #
- def Un(_step):
- if U is not None:
- if hasattr(U,"store") and 1<=_step<len(U) :
- _Un = numpy.asmatrix(numpy.ravel( U[_step] )).T
- elif hasattr(U,"store") and len(U)==1:
- _Un = numpy.asmatrix(numpy.ravel( U[0] )).T
- else:
- _Un = numpy.asmatrix(numpy.ravel( U )).T
- else:
- _Un = None
- return _Un
- def CmUn(_xn,_un):
- if Cm is not None and _un is not None: # Attention : si Cm est aussi dans M, doublon !
- _Cm = Cm.reshape(_xn.size,_un.size) # ADAO & check shape
- _CmUn = _Cm * _un
- else:
- _CmUn = 0.
- return _CmUn
- #
- # Remarque : les observations sont exploitées à partir du pas de temps
- # numéro 1, et sont utilisées dans Yo comme rangées selon ces indices.
- # Donc le pas 0 n'est pas utilisé puisque la première étape commence
- # avec l'observation du pas 1.
- #
- # Nombre de pas identique au nombre de pas d'observations
- # -------------------------------------------------------
- if hasattr(Y,"stepnumber"):
- duration = Y.stepnumber()
- else:
- duration = 2
- #
- # Précalcul des inversions de B et R
- # ----------------------------------
- BI = B.getI()
- RI = R.getI()
- #
- # Définition de la fonction-coût
- # ------------------------------
- self.DirectCalculation = [None,] # Le pas 0 n'est pas observé
- self.DirectInnovation = [None,] # Le pas 0 n'est pas observé
- def CostFunction(x):
- _X = numpy.asmatrix(numpy.ravel( x )).T
- if self._parameters["StoreInternalVariables"] or \
- self._toStore("CurrentState") or \
- self._toStore("CurrentOptimum"):
- self.StoredVariables["CurrentState"].store( _X )
- Jb = float( 0.5 * (_X - Xb).T * BI * (_X - Xb) )
- self.DirectCalculation = [None,]
- self.DirectInnovation = [None,]
- Jo = 0.
- _Xn = _X
- for step in range(0,duration-1):
- if hasattr(Y,"store"):
- _Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
- else:
- _Ynpu = numpy.asmatrix(numpy.ravel( Y )).T
- _Un = Un(step)
- #
- # Etape d'évolution
- if self._parameters["EstimationOf"] == "State":
- _Xn = Mm( (_Xn, _Un) ) + CmUn(_Xn, _Un)
- elif self._parameters["EstimationOf"] == "Parameters":
- pass
- #
- if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
- _Xn = numpy.max(numpy.hstack((_Xn,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
- _Xn = numpy.min(numpy.hstack((_Xn,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
- #
- # Etape de différence aux observations
- if self._parameters["EstimationOf"] == "State":
- _YmHMX = _Ynpu - numpy.asmatrix(numpy.ravel( Hm( (_Xn, None) ) )).T
- elif self._parameters["EstimationOf"] == "Parameters":
- _YmHMX = _Ynpu - numpy.asmatrix(numpy.ravel( Hm( (_Xn, _Un) ) )).T - CmUn(_Xn, _Un)
- #
- # Stockage de l'état
- self.DirectCalculation.append( _Xn )
- self.DirectInnovation.append( _YmHMX )
- #
- # Ajout dans la fonctionnelle d'observation
- Jo = Jo + 0.5 * float( _YmHMX.T * RI * _YmHMX )
- J = Jb + Jo
- #
- self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) )
- self.StoredVariables["CostFunctionJb"].store( Jb )
- self.StoredVariables["CostFunctionJo"].store( Jo )
- self.StoredVariables["CostFunctionJ" ].store( J )
- if self._toStore("IndexOfOptimum") or \
- self._toStore("CurrentOptimum") or \
- self._toStore("CostFunctionJAtCurrentOptimum") or \
- self._toStore("CostFunctionJbAtCurrentOptimum") or \
- self._toStore("CostFunctionJoAtCurrentOptimum"):
- IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
- if self._toStore("IndexOfOptimum"):
- self.StoredVariables["IndexOfOptimum"].store( IndexMin )
- if self._toStore("CurrentOptimum"):
- self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
- if self._toStore("CostFunctionJAtCurrentOptimum"):
- self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
- if self._toStore("CostFunctionJbAtCurrentOptimum"):
- self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
- if self._toStore("CostFunctionJoAtCurrentOptimum"):
- self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
- return J
- #
- def GradientOfCostFunction(x):
- _X = numpy.asmatrix(numpy.ravel( x )).T
- GradJb = BI * (_X - Xb)
- GradJo = 0.
- for step in range(duration-1,0,-1):
- # Etape de récupération du dernier stockage de l'évolution
- _Xn = self.DirectCalculation.pop()
- # Etape de récupération du dernier stockage de l'innovation
- _YmHMX = self.DirectInnovation.pop()
- # Calcul des adjoints
- Ha = HO["Adjoint"].asMatrix(ValueForMethodForm = _Xn)
- Ha = Ha.reshape(_Xn.size,_YmHMX.size) # ADAO & check shape
- Ma = EM["Adjoint"].asMatrix(ValueForMethodForm = _Xn)
- Ma = Ma.reshape(_Xn.size,_Xn.size) # ADAO & check shape
- # Calcul du gradient par etat adjoint
- GradJo = GradJo + Ha * RI * _YmHMX # Equivaut pour Ha lineaire à : Ha( (_Xn, RI * _YmHMX) )
- GradJo = Ma * GradJo # Equivaut pour Ma lineaire à : Ma( (_Xn, GradJo) )
- GradJ = numpy.ravel( GradJb ) - numpy.ravel( GradJo )
- return GradJ
- #
- # Point de démarrage de l'optimisation
- # ------------------------------------
- Xini = self._parameters["InitializationPoint"]
- #
- # Minimisation de la fonctionnelle
- # --------------------------------
- nbPreviousSteps = self.StoredVariables["CostFunctionJ"].stepnumber()
- #
- if self._parameters["Minimizer"] == "LBFGSB":
- # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
- if "0.19" <= scipy.version.version <= "1.1.0":
- import lbfgsbhlt as optimiseur
- else:
- import scipy.optimize as optimiseur
- Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b(
- func = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- bounds = self._parameters["Bounds"],
- maxfun = self._parameters["MaximumNumberOfSteps"]-1,
- factr = self._parameters["CostDecrementTolerance"]*1.e14,
- pgtol = self._parameters["ProjectedGradientTolerance"],
- iprint = self._parameters["optiprint"],
- )
- nfeval = Informations['funcalls']
- rc = Informations['warnflag']
- elif self._parameters["Minimizer"] == "TNC":
- Minimum, nfeval, rc = scipy.optimize.fmin_tnc(
- func = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- bounds = self._parameters["Bounds"],
- maxfun = self._parameters["MaximumNumberOfSteps"],
- pgtol = self._parameters["ProjectedGradientTolerance"],
- ftol = self._parameters["CostDecrementTolerance"],
- messages = self._parameters["optmessages"],
- )
- elif self._parameters["Minimizer"] == "CG":
- Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
- f = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- maxiter = self._parameters["MaximumNumberOfSteps"],
- gtol = self._parameters["GradientNormTolerance"],
- disp = self._parameters["optdisp"],
- full_output = True,
- )
- elif self._parameters["Minimizer"] == "NCG":
- Minimum, fopt, nfeval, grad_calls, hcalls, rc = scipy.optimize.fmin_ncg(
- f = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- maxiter = self._parameters["MaximumNumberOfSteps"],
- avextol = self._parameters["CostDecrementTolerance"],
- disp = self._parameters["optdisp"],
- full_output = True,
- )
- elif self._parameters["Minimizer"] == "BFGS":
- Minimum, fopt, gopt, Hopt, nfeval, grad_calls, rc = scipy.optimize.fmin_bfgs(
- f = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- maxiter = self._parameters["MaximumNumberOfSteps"],
- gtol = self._parameters["GradientNormTolerance"],
- disp = self._parameters["optdisp"],
- full_output = True,
- )
- else:
- raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
- #
- IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
- MinJ = self.StoredVariables["CostFunctionJ"][IndexMin]
- #
- # Correction pour pallier a un bug de TNC sur le retour du Minimum
- # ----------------------------------------------------------------
- if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
- Minimum = self.StoredVariables["CurrentState"][IndexMin]
- #
- # Obtention de l'analyse
- # ----------------------
- Xa = numpy.asmatrix(numpy.ravel( Minimum )).T
- #
- self.StoredVariables["Analysis"].store( Xa.A1 )
- #
- # Calculs et/ou stockages supplémentaires
- # ---------------------------------------
- if self._toStore("BMA"):
- self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ raise ValueError("Error in Variant name: %s"%self._parameters["Variant"])
#
self._post_run(HO)
return 0
# ==============================================================================
def multi3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, oneCycle):
"""
- Chapeau : 3DVAR multi-pas et multi-méthodes
+ 3DVAR multi-pas et multi-méthodes
"""
#
# Initialisation
# ==============================================================================
def std3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
"""
- 3DVAR (Bouttier 1999, Courtier 1993)
-
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+ 3DVAR
"""
#
+ # Initialisations
+ # ---------------
+ #
# Opérateurs
- # ----------
Hm = HO["Direct"].appliedTo
Ha = HO["Adjoint"].appliedInXTo
#
# Utilisation éventuelle d'un vecteur H(Xb) précalculé
- # ----------------------------------------------------
if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
HXb = Hm( Xb, HO["AppliedInX"]["HXb"] )
else:
selfA.StoredVariables["JacobianMatrixAtBackground"].store( HtMb )
#
# Précalcul des inversions de B et R
- # ----------------------------------
BI = B.getI()
RI = R.getI()
#
# Point de démarrage de l'optimisation
- # ------------------------------------
Xini = selfA._parameters["InitializationPoint"]
#
# Définition de la fonction-coût
# ==============================================================================
def van3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
"""
- 3DVAR variational analysis with no inversion of B (Huang 2000)
-
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+ 3DVAR variational analysis with no inversion of B
"""
#
# Initialisations
# ---------------
+ #
+ # Opérateurs
Hm = HO["Direct"].appliedTo
Ha = HO["Adjoint"].appliedInXTo
#
# ==============================================================================
def incr3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
"""
- 3DVAR incrémental (Courtier 1994, 1997)
-
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+ 3DVAR incrémental
"""
#
# Initialisations
# ==============================================================================
def psas3dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
"""
- 3DVAR PSAS (Huang 2000)
-
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+ 3DVAR PSAS
"""
#
# Initialisations
nbPreviousSteps = selfA.StoredVariables["CostFunctionJ"].stepnumber()
#
if selfA._parameters["Minimizer"] == "LBFGSB":
- # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
if "0.19" <= scipy.version.version <= "1.1.0":
import lbfgsbhlt as optimiseur
else:
return 0
# ==============================================================================
-def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
+def std4dvar(selfA, Xb, Y, U, HO, EM, CM, R, B, Q):
"""
- Stochastic EnKF (Envensen 1994, Burgers 1998)
+ 4DVAR
+ """
+ #
+ # Initialisations
+ # ---------------
+ #
+ # Opérateurs
+ Hm = HO["Direct"].appliedControledFormTo
+ Mm = EM["Direct"].appliedControledFormTo
+ #
+ if CM is not None and "Tangent" in CM and U is not None:
+ Cm = CM["Tangent"].asMatrix(Xb)
+ else:
+ Cm = None
+ #
+ def Un(_step):
+ if U is not None:
+ if hasattr(U,"store") and 1<=_step<len(U) :
+ _Un = numpy.asmatrix(numpy.ravel( U[_step] )).T
+ elif hasattr(U,"store") and len(U)==1:
+ _Un = numpy.asmatrix(numpy.ravel( U[0] )).T
+ else:
+ _Un = numpy.asmatrix(numpy.ravel( U )).T
+ else:
+ _Un = None
+ return _Un
+ def CmUn(_xn,_un):
+ if Cm is not None and _un is not None: # Attention : si Cm est aussi dans M, doublon !
+ _Cm = Cm.reshape(_xn.size,_un.size) # ADAO & check shape
+ _CmUn = _Cm * _un
+ else:
+ _CmUn = 0.
+ return _CmUn
+ #
+ # Remarque : les observations sont exploitées à partir du pas de temps
+ # numéro 1, et sont utilisées dans Yo comme rangées selon ces indices.
+ # Donc le pas 0 n'est pas utilisé puisque la première étape commence
+ # avec l'observation du pas 1.
+ #
+ # Nombre de pas identique au nombre de pas d'observations
+ if hasattr(Y,"stepnumber"):
+ duration = Y.stepnumber()
+ else:
+ duration = 2
+ #
+ # Précalcul des inversions de B et R
+ BI = B.getI()
+ RI = R.getI()
+ #
+ # Point de démarrage de l'optimisation
+ Xini = selfA._parameters["InitializationPoint"]
+ #
+ # Définition de la fonction-coût
+ # ------------------------------
+ selfA.DirectCalculation = [None,] # Le pas 0 n'est pas observé
+ selfA.DirectInnovation = [None,] # Le pas 0 n'est pas observé
+ def CostFunction(x):
+ _X = numpy.asmatrix(numpy.ravel( x )).T
+ if selfA._parameters["StoreInternalVariables"] or \
+ selfA._toStore("CurrentState") or \
+ selfA._toStore("CurrentOptimum"):
+ selfA.StoredVariables["CurrentState"].store( _X )
+ Jb = float( 0.5 * (_X - Xb).T * BI * (_X - Xb) )
+ selfA.DirectCalculation = [None,]
+ selfA.DirectInnovation = [None,]
+ Jo = 0.
+ _Xn = _X
+ for step in range(0,duration-1):
+ if hasattr(Y,"store"):
+ _Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
+ else:
+ _Ynpu = numpy.asmatrix(numpy.ravel( Y )).T
+ _Un = Un(step)
+ #
+ # Etape d'évolution
+ if selfA._parameters["EstimationOf"] == "State":
+ _Xn = Mm( (_Xn, _Un) ) + CmUn(_Xn, _Un)
+ elif selfA._parameters["EstimationOf"] == "Parameters":
+ pass
+ #
+ if selfA._parameters["Bounds"] is not None and selfA._parameters["ConstrainedBy"] == "EstimateProjection":
+ _Xn = numpy.max(numpy.hstack((_Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,0])),axis=1)
+ _Xn = numpy.min(numpy.hstack((_Xn,numpy.asmatrix(selfA._parameters["Bounds"])[:,1])),axis=1)
+ #
+ # Etape de différence aux observations
+ if selfA._parameters["EstimationOf"] == "State":
+ _YmHMX = _Ynpu - numpy.asmatrix(numpy.ravel( Hm( (_Xn, None) ) )).T
+ elif selfA._parameters["EstimationOf"] == "Parameters":
+ _YmHMX = _Ynpu - numpy.asmatrix(numpy.ravel( Hm( (_Xn, _Un) ) )).T - CmUn(_Xn, _Un)
+ #
+ # Stockage de l'état
+ selfA.DirectCalculation.append( _Xn )
+ selfA.DirectInnovation.append( _YmHMX )
+ #
+ # Ajout dans la fonctionnelle d'observation
+ Jo = Jo + 0.5 * float( _YmHMX.T * RI * _YmHMX )
+ J = Jb + Jo
+ #
+ selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["CostFunctionJ"]) )
+ selfA.StoredVariables["CostFunctionJb"].store( Jb )
+ selfA.StoredVariables["CostFunctionJo"].store( Jo )
+ selfA.StoredVariables["CostFunctionJ" ].store( J )
+ if selfA._toStore("IndexOfOptimum") or \
+ selfA._toStore("CurrentOptimum") or \
+ selfA._toStore("CostFunctionJAtCurrentOptimum") or \
+ selfA._toStore("CostFunctionJbAtCurrentOptimum") or \
+ selfA._toStore("CostFunctionJoAtCurrentOptimum"):
+ IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
+ if selfA._toStore("IndexOfOptimum"):
+ selfA.StoredVariables["IndexOfOptimum"].store( IndexMin )
+ if selfA._toStore("CurrentOptimum"):
+ selfA.StoredVariables["CurrentOptimum"].store( selfA.StoredVariables["CurrentState"][IndexMin] )
+ if selfA._toStore("CostFunctionJAtCurrentOptimum"):
+ selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] )
+ if selfA._toStore("CostFunctionJbAtCurrentOptimum"):
+ selfA.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJb"][IndexMin] )
+ if selfA._toStore("CostFunctionJoAtCurrentOptimum"):
+ selfA.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( selfA.StoredVariables["CostFunctionJo"][IndexMin] )
+ return J
+ #
+ def GradientOfCostFunction(x):
+ _X = numpy.asmatrix(numpy.ravel( x )).T
+ GradJb = BI * (_X - Xb)
+ GradJo = 0.
+ for step in range(duration-1,0,-1):
+ # Etape de récupération du dernier stockage de l'évolution
+ _Xn = selfA.DirectCalculation.pop()
+ # Etape de récupération du dernier stockage de l'innovation
+ _YmHMX = selfA.DirectInnovation.pop()
+ # Calcul des adjoints
+ Ha = HO["Adjoint"].asMatrix(ValueForMethodForm = _Xn)
+ Ha = Ha.reshape(_Xn.size,_YmHMX.size) # ADAO & check shape
+ Ma = EM["Adjoint"].asMatrix(ValueForMethodForm = _Xn)
+ Ma = Ma.reshape(_Xn.size,_Xn.size) # ADAO & check shape
+ # Calcul du gradient par etat adjoint
+ GradJo = GradJo + Ha * RI * _YmHMX # Equivaut pour Ha lineaire à : Ha( (_Xn, RI * _YmHMX) )
+ GradJo = Ma * GradJo # Equivaut pour Ma lineaire à : Ma( (_Xn, GradJo) )
+ GradJ = numpy.ravel( GradJb ) - numpy.ravel( GradJo )
+ return GradJ
+ #
+ # Minimisation de la fonctionnelle
+ # --------------------------------
+ nbPreviousSteps = selfA.StoredVariables["CostFunctionJ"].stepnumber()
+ #
+ if selfA._parameters["Minimizer"] == "LBFGSB":
+ if "0.19" <= scipy.version.version <= "1.1.0":
+ import lbfgsbhlt as optimiseur
+ else:
+ import scipy.optimize as optimiseur
+ Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b(
+ func = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ bounds = selfA._parameters["Bounds"],
+ maxfun = selfA._parameters["MaximumNumberOfSteps"]-1,
+ factr = selfA._parameters["CostDecrementTolerance"]*1.e14,
+ pgtol = selfA._parameters["ProjectedGradientTolerance"],
+ iprint = selfA._parameters["optiprint"],
+ )
+ nfeval = Informations['funcalls']
+ rc = Informations['warnflag']
+ elif selfA._parameters["Minimizer"] == "TNC":
+ Minimum, nfeval, rc = scipy.optimize.fmin_tnc(
+ func = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ bounds = selfA._parameters["Bounds"],
+ maxfun = selfA._parameters["MaximumNumberOfSteps"],
+ pgtol = selfA._parameters["ProjectedGradientTolerance"],
+ ftol = selfA._parameters["CostDecrementTolerance"],
+ messages = selfA._parameters["optmessages"],
+ )
+ elif selfA._parameters["Minimizer"] == "CG":
+ Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
+ f = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ maxiter = selfA._parameters["MaximumNumberOfSteps"],
+ gtol = selfA._parameters["GradientNormTolerance"],
+ disp = selfA._parameters["optdisp"],
+ full_output = True,
+ )
+ elif selfA._parameters["Minimizer"] == "NCG":
+ Minimum, fopt, nfeval, grad_calls, hcalls, rc = scipy.optimize.fmin_ncg(
+ f = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ maxiter = selfA._parameters["MaximumNumberOfSteps"],
+ avextol = selfA._parameters["CostDecrementTolerance"],
+ disp = selfA._parameters["optdisp"],
+ full_output = True,
+ )
+ elif selfA._parameters["Minimizer"] == "BFGS":
+ Minimum, fopt, gopt, Hopt, nfeval, grad_calls, rc = scipy.optimize.fmin_bfgs(
+ f = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ maxiter = selfA._parameters["MaximumNumberOfSteps"],
+ gtol = selfA._parameters["GradientNormTolerance"],
+ disp = selfA._parameters["optdisp"],
+ full_output = True,
+ )
+ else:
+ raise ValueError("Error in Minimizer name: %s"%selfA._parameters["Minimizer"])
+ #
+ IndexMin = numpy.argmin( selfA.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
+ MinJ = selfA.StoredVariables["CostFunctionJ"][IndexMin]
+ #
+ # Correction pour pallier a un bug de TNC sur le retour du Minimum
+ # ----------------------------------------------------------------
+ if selfA._parameters["StoreInternalVariables"] or selfA._toStore("CurrentState"):
+ Minimum = selfA.StoredVariables["CurrentState"][IndexMin]
+ #
+ # Obtention de l'analyse
+ # ----------------------
+ Xa = numpy.asmatrix(numpy.ravel( Minimum )).T
+ #
+ selfA.StoredVariables["Analysis"].store( Xa )
+ #
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ if selfA._toStore("BMA"):
+ selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ #
+ return 0
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+# ==============================================================================
+def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
+ """
+ Stochastic EnKF
"""
if selfA._parameters["EstimationOf"] == "Parameters":
selfA._parameters["StoreInternalVariables"] = True
# ==============================================================================
def etkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"):
"""
- Ensemble-Transform EnKF (ETKF or Deterministic EnKF: Bishop 2001, Hunt 2007)
-
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+ Ensemble-Transform EnKF
"""
if selfA._parameters["EstimationOf"] == "Parameters":
selfA._parameters["StoreInternalVariables"] = True
def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="MLEF13",
BnotT=False, _epsilon=1.e-3, _e=1.e-7, _jmax=15000):
"""
- Maximum Likelihood Ensemble Filter (EnKF/MLEF Zupanski 2005, Bocquet 2013)
-
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+ Maximum Likelihood Ensemble Filter
"""
if selfA._parameters["EstimationOf"] == "Parameters":
selfA._parameters["StoreInternalVariables"] = True
def ienkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="IEnKF12",
BnotT=False, _epsilon=1.e-3, _e=1.e-7, _jmax=15000):
"""
- Iterative EnKF (Sakov 2012, Sakov 2018)
-
- selfA est identique au "self" d'algorithme appelant et contient les
- valeurs.
+ Iterative EnKF
"""
if selfA._parameters["EstimationOf"] == "Parameters":
selfA._parameters["StoreInternalVariables"] = True
