# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2019 EDF R&D
+# Copyright (C) 2008-2021 EDF R&D
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
-from daCore import BasicObjects, PlatformInfo
-import numpy, math
-mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
+from daCore import BasicObjects, NumericObjects
+import numpy
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
BasicObjects.Algorithm.__init__(self, "ENSEMBLEKALMANFILTER")
+ self.defineRequiredParameter(
+ name = "Variant",
+ default = "EnKF",
+ typecast = str,
+ message = "Variant ou formulation de la méthode",
+ listval = [
+ "EnKF",
+ "ETKF",
+ "ETKF-N",
+ "MLEF",
+ "IEnKF",
+ ],
+ listadv = [
+ "StochasticEnKF",
+ "EnKF-05",
+ "EnKF-16",
+ "ETKF-KFF",
+ "ETKF-VAR",
+ "ETKF-N-11",
+ "ETKF-N-15",
+ "ETKF-N-16",
+ "MLEF-T",
+ "MLEF-B",
+ "IEnKF-T",
+ "IEnKF-B",
+ "IEKF",
+ ],
+ )
self.defineRequiredParameter(
name = "NumberOfMembers",
default = 100,
typecast = int,
message = "Nombre de membres dans l'ensemble",
- minval = -1,
+ minval = 2,
)
self.defineRequiredParameter(
name = "EstimationOf",
message = "Estimation d'etat ou de parametres",
listval = ["State", "Parameters"],
)
+ self.defineRequiredParameter(
+ name = "InflationType",
+ default = "MultiplicativeOnAnalysisCovariance",
+ typecast = str,
+ message = "Méthode d'inflation d'ensemble",
+ listval = [
+ "MultiplicativeOnAnalysisCovariance",
+ "MultiplicativeOnBackgroundCovariance",
+ "MultiplicativeOnAnalysisAnomalies",
+ "MultiplicativeOnBackgroundAnomalies",
+ "AdditiveOnAnalysisCovariance",
+ "AdditiveOnBackgroundCovariance",
+ "HybridOnBackgroundCovariance",
+ ],
+ )
+ self.defineRequiredParameter(
+ name = "InflationFactor",
+ default = 1.,
+ typecast = float,
+ message = "Facteur d'inflation",
+ minval = 0.,
+ )
+ self.defineRequiredParameter(
+ name = "LocalizationType",
+ default = "SchurLocalization",
+ typecast = str,
+ message = "Méthode d'inflation d'ensemble",
+ listval = [
+ "SchurLocalization",
+ ],
+ listadv = [
+ "CovarianceLocalization",
+ "DomainLocalization",
+ "GaspariCohnLocalization",
+ ],
+ )
+ self.defineRequiredParameter(
+ name = "LocalizationFactor",
+ default = 1.,
+ typecast = float,
+ message = "Facteur de localisation",
+ minval = 0.,
+ )
+ self.defineRequiredParameter( # Pas de type
+ name = "LocalizationMatrix",
+ message = "Matrice de localisation ou de distances",
+ )
self.defineRequiredParameter(
name = "SetSeed",
typecast = numpy.random.seed,
typecast = tuple,
message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
listval = [
+ "Analysis",
"APosterioriCorrelations",
"APosterioriCovariance",
"APosterioriStandardDeviations",
"APosterioriVariances",
"BMA",
"CostFunctionJ",
+ "CostFunctionJAtCurrentOptimum",
"CostFunctionJb",
+ "CostFunctionJbAtCurrentOptimum",
"CostFunctionJo",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentIterationNumber",
+ "CurrentOptimum",
"CurrentState",
- "Innovation",
+ "ForecastState",
+ "IndexOfOptimum",
+ "InnovationAtCurrentAnalysis",
+ "InnovationAtCurrentState",
+ "SimulatedObservationAtCurrentAnalysis",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
]
)
self.requireInputArguments(
mandatory= ("Xb", "Y", "HO", "R", "B"),
optional = ("U", "EM", "CM", "Q"),
)
+ self.setAttributes(tags=(
+ "DataAssimilation",
+ "NonLinear",
+ "Filter",
+ "Ensemble",
+ "Dynamic",
+ ))
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, R, B, Q)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
- if self._parameters["EstimationOf"] == "Parameters":
- self._parameters["StoreInternalVariables"] = True
+ #--------------------------
+ # Default EnKF = EnKF-16 = StochasticEnKF
+ if self._parameters["Variant"] == "EnKF-05":
+ NumericObjects.senkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula05")
#
- # Opérateurs
- # ----------
- H = HO["Direct"].appliedControledFormTo
+ elif self._parameters["Variant"] in ["EnKF-16", "StochasticEnKF", "EnKF"]:
+ NumericObjects.senkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula16")
#
- if self._parameters["EstimationOf"] == "State":
- M = EM["Direct"].appliedControledFormTo
+ #--------------------------
+ # Default ETKF = ETKF-KFF
+ elif self._parameters["Variant"] in ["ETKF-KFF", "ETKF"]:
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula")
#
- if CM is not None and "Tangent" in CM and U is not None:
- Cm = CM["Tangent"].asMatrix(Xb)
- else:
- Cm = None
+ elif self._parameters["Variant"] == "ETKF-VAR":
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="Variational")
#
- # Nombre de pas identique au nombre de pas d'observations
- # -------------------------------------------------------
- if hasattr(Y,"stepnumber"):
- duration = Y.stepnumber()
- __p = numpy.cumprod(Y.shape())[-1]
- else:
- duration = 2
- __p = numpy.array(Y).size
+ #--------------------------
+ # Default ETKF-N = ETKF-N-16
+ elif self._parameters["Variant"] == "ETKF-N-11":
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize11")
#
- # Précalcul des inversions de B et R
- # ----------------------------------
- if self._parameters["StoreInternalVariables"] or \
- self._toStore("CostFunctionJ") or \
- self._toStore("CostFunctionJb") or \
- self._toStore("CostFunctionJo") or \
- self._toStore("APosterioriCovariance"):
- BI = B.getI()
- RI = R.getI()
- BIdemi = B.choleskyI()
- RIdemi = R.choleskyI()
+ elif self._parameters["Variant"] == "ETKF-N-15":
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize15")
#
- # Initialisation
- # --------------
- __n = Xb.size
- __m = self._parameters["NumberOfMembers"]
- Xn = numpy.asmatrix(numpy.dot( Xb.reshape(__n,1), numpy.ones((1,__m)) ))
- if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(__n)
- else: Pn = B
- if hasattr(R,"asfullmatrix"): Rn = R.asfullmatrix(__p)
- else: Rn = R
- if hasattr(Q,"asfullmatrix"): Qn = Q.asfullmatrix(__n)
- else: Qn = Q
+ elif self._parameters["Variant"] in ["ETKF-N-16", "ETKF-N"]:
+ NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize16")
#
- self.StoredVariables["Analysis"].store( Xb.A1 )
- if self._toStore("APosterioriCovariance"):
- self.StoredVariables["APosterioriCovariance"].store( Pn )
- covarianceXa = Pn
- Xa = Xb
- previousJMinimum = numpy.finfo(float).max
+ #--------------------------
+ # Default MLEF = MLEF-T
+ elif self._parameters["Variant"] in ["MLEF-T", "MLEF"]:
+ NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False)
#
- # Predimensionnement
- Xn_predicted = numpy.asmatrix(numpy.zeros((__n,__m)))
- HX_predicted = numpy.asmatrix(numpy.zeros((__p,__m)))
+ elif self._parameters["Variant"] == "MLEF-B":
+ NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=True)
#
- for step in range(duration-1):
- if hasattr(Y,"store"):
- Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
- else:
- Ynpu = numpy.asmatrix(numpy.ravel( Y )).T
- #
- if U is not None:
- if hasattr(U,"store") and len(U)>1:
- 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
- #
- if self._parameters["EstimationOf"] == "State":
- for i in range(__m):
- qi = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__n), Qn)).T
- Xn_predicted[:,i] = numpy.asmatrix(numpy.ravel( M((Xn[:,i], Un)) )).T + qi
- HX_predicted[:,i] = numpy.asmatrix(numpy.ravel( H((Xn_predicted[:,i], Un)) )).T
- if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
- Cm = Cm.reshape(__n,Un.size) # ADAO & check shape
- Xn_predicted = Xn_predicted + Cm * Un
- elif self._parameters["EstimationOf"] == "Parameters":
- # --- > Par principe, M = Id, Q = 0
- Xn_predicted = Xn
- #
- Xfm = numpy.asmatrix(numpy.ravel(Xn_predicted.mean(axis=1, dtype=mfp))).T
- Hfm = numpy.asmatrix(numpy.ravel(HX_predicted.mean(axis=1, dtype=mfp))).T
- Af = Xn_predicted - Xfm
- Hf = HX_predicted - Hfm
- #
- PfHT, HPfHT = 0., 0.
- for i in range(__m):
- PfHT += Af[:,i] * Hf[:,i].T
- HPfHT += Hf[:,i] * Hf[:,i].T
- PfHT = (1./(__m-1)) * PfHT
- HPfHT = (1./(__m-1)) * HPfHT
- #
- K = PfHT * ( R + HPfHT ).I
- #
- Yo = numpy.asmatrix(numpy.zeros((__p,__m)))
- for i in range(__m):
- ri = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__p), Rn)).T
- Yo[:,i] = Ynpu + ri
- #
- for i in range(__m):
- Xn[:,i] = Xn_predicted[:,i] + K * (Yo[:,i] - HX_predicted[:,i])
- #
- Xa = Xn.mean(axis=1, dtype=mfp)
- self.StoredVariables["Analysis"].store( Xa )
- #
- del Yo, PfHT, HPfHT
- if self._parameters["StoreInternalVariables"] or \
- self._toStore("CostFunctionJ") or \
- self._toStore("CostFunctionJb") or \
- self._toStore("CostFunctionJo") or \
- self._toStore("APosterioriCovariance") or \
- self._toStore("Innovation"):
- d = Ynpu - numpy.asmatrix(numpy.ravel( H((Xa, Un)) )).T
- self.StoredVariables["Innovation"].store( d )
- if self._parameters["StoreInternalVariables"] \
- or self._toStore("CurrentState"):
- self.StoredVariables["CurrentState"].store( Xn )
- if self._parameters["StoreInternalVariables"] or \
- self._toStore("CostFunctionJ") or \
- self._toStore("CostFunctionJb") or \
- self._toStore("CostFunctionJo") or \
- self._toStore("APosterioriCovariance"):
- Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
- Jo = 0.5 * d.T * RI * d
- J = float( Jb ) + float( Jo )
- self.StoredVariables["CostFunctionJb"].store( Jb )
- self.StoredVariables["CostFunctionJo"].store( Jo )
- self.StoredVariables["CostFunctionJ" ].store( J )
- if self._toStore("APosterioriCovariance"):
- Ht = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
- Ht = Ht.reshape(__p,__n) # ADAO & check shape
- Pf = 0.
- for i in range(__m):
- Pf += Af[:,i] * Af[:,i].T
- Pf = (1./(__m-1)) * Pf
- Pn = (1. - K * Ht) * Pf
- self.StoredVariables["APosterioriCovariance"].store( Pn )
- if J < previousJMinimum:
- previousJMinimum = J
- Xa = Xn
- covarianceXa = Pn
+ #--------------------------
+ # Default IEnKF = IEnKF-T
+ elif self._parameters["Variant"] in ["IEnKF-T", "IEnKF"]:
+ NumericObjects.ienkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False)
#
- # Stockage supplementaire de l'optimum en estimation de parametres
- # ----------------------------------------------------------------
- if self._parameters["EstimationOf"] == "Parameters":
- self.StoredVariables["Analysis"].store( Xa.A1 )
- if self._toStore("APosterioriCovariance"):
- self.StoredVariables["APosterioriCovariance"].store( covarianceXa )
+ elif self._parameters["Variant"] in ["IEnKF-B", "IEKF"]:
+ NumericObjects.ienkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=True)
#
- if self._toStore("BMA"):
- self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ #--------------------------
+ else:
+ raise ValueError("Error in Variant name: %s"%self._parameters["Variant"])
#
self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print('\n AUTODIAGNOSTIC \n')
+ print('\n AUTODIAGNOSTIC\n')
