From: Jean-Philippe ARGAUD Date: Wed, 23 Dec 2020 17:55:37 +0000 (+0100) Subject: Improvement and extension of EnKF algorithm (loc) X-Git-Tag: V9_7_0b1~58 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=8459b709139b009496b328e977754b30992815a4;p=modules%2Fadao.git Improvement and extension of EnKF algorithm (loc) --- diff --git a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py index c24bc94..4a3e6ad 100644 --- a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py +++ b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py @@ -49,6 +49,51 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): 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", + "AdditiveOnBackgroundCovariance", + "AdditiveOnAnalysisCovariance", + "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 = [ + "CovarianceLocalization", + "DomainLocalization", + "SchurLocalization", + "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, diff --git a/src/daComposant/daCore/NumericObjects.py b/src/daComposant/daCore/NumericObjects.py index 8acdd77..4ff8cb5 100644 --- a/src/daComposant/daCore/NumericObjects.py +++ b/src/daComposant/daCore/NumericObjects.py @@ -507,6 +507,71 @@ def mmqr( # return variables, Ecart, [n,p,iteration,increment,0] +# ============================================================================== +def CovarianceInflation( + InputCovOrEns, + InflationType = None, + InflationFactor = None, + BackgroundCov = None, + ): + """ + Inflation applicable soit sur Pb ou Pa, soit sur les ensembles EXb ou EXa + + Synthèse : Hunt 2007, section 2.3.5 + """ + if InflationFactor is None: + return InputCovOrEns + else: + InflationFactor = float(InflationFactor) + # + if InflationType in ["MultiplicativeOnAnalysisCovariance", "MultiplicativeOnBackgroundCovariance"]: + if InflationFactor < 1.: + raise ValueError("Inflation factor for multiplicative inflation has to be greater or equal than 1.") + if InflationFactor < 1.+mpr: + return InputCovOrEns + OutputCovOrEns = InflationFactor**2 * InputCovOrEns + # + elif InflationType in ["MultiplicativeOnAnalysisAnomalies", "MultiplicativeOnBackgroundAnomalies"]: + if InflationFactor < 1.: + raise ValueError("Inflation factor for multiplicative inflation has to be greater or equal than 1.") + if InflationFactor < 1.+mpr: + return InputCovOrEns + InputCovOrEnsMean = InputCovOrEns.mean(axis=1, dtype=mfp).astype('float') + OutputCovOrEns = InputCovOrEnsMean[:,numpy.newaxis] \ + + InflationFactor * (InputCovOrEns - InputCovOrEnsMean[:,numpy.newaxis]) + # + elif InflationType in ["AdditiveOnBackgroundCovariance", "AdditiveOnAnalysisCovariance"]: + if InflationFactor < 0.: + raise ValueError("Inflation factor for additive inflation has to be greater or equal than 0.") + if InflationFactor < mpr: + return InputCovOrEns + __n, __m = numpy.asarray(InputCovOrEns).shape + if __n != __m: + raise ValueError("Additive inflation can only be applied to squared (covariance) matrix.") + OutputCovOrEns = (1. - InflationFactor) * InputCovOrEns + InflationFactor * numpy.eye(__n) + # + elif InflationType == "HybridOnBackgroundCovariance": + if InflationFactor < 0.: + raise ValueError("Inflation factor for hybrid inflation has to be greater or equal than 0.") + if InflationFactor < mpr: + return InputCovOrEns + __n, __m = numpy.asarray(InputCovOrEns).shape + if __n != __m: + raise ValueError("Additive inflation can only be applied to squared (covariance) matrix.") + if BackgroundCov is None: + raise ValueError("Background covariance matrix B has to be given for hybrid inflation.") + if InputCovOrEns.shape != BackgroundCov.shape: + raise ValueError("Ensemble covariance matrix has to be of same size than background covariance matrix B.") + OutputCovOrEns = (1. - InflationFactor) * InputCovOrEns + InflationFactor * BackgroundCov + # + elif InflationType == "Relaxation": + raise NotImplementedError("InflationType Relaxation") + # + else: + raise ValueError("Error in inflation type, '%s' is not a valid keyword."%InflationType) + # + return OutputCovOrEns + # ============================================================================== def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): """ @@ -590,6 +655,12 @@ def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): else: Un = None # + if selfA._parameters["InflationType"] == "MultiplicativeOnBackgroundAnomalies": + Xn = CovarianceInflation( Xn, + selfA._parameters["InflationType"], + selfA._parameters["InflationFactor"], + ) + # if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast for i in range(__m): qi = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__n), Qn, (1,1,1))).T @@ -623,6 +694,12 @@ def senkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): ri = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__p), Rn, (1,1,1))).T Xn[:,i] = Xn_predicted[:,i] + K * (Ynpu + ri - HX_predicted[:,i]) # + if selfA._parameters["InflationType"] == "MultiplicativeOnAnalysisAnomalies": + Xn = CovarianceInflation( Xn, + selfA._parameters["InflationType"], + selfA._parameters["InflationFactor"], + ) + # Xa = Xn.mean(axis=1, dtype=mfp).astype('float') # if selfA._parameters["StoreInternalVariables"] \ @@ -797,6 +874,12 @@ def etkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): else: Un = None # + if selfA._parameters["InflationType"] == "MultiplicativeOnBackgroundAnomalies": + Xn = CovarianceInflation( Xn, + selfA._parameters["InflationType"], + selfA._parameters["InflationFactor"], + ) + # if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast for i in range(__m): qi = numpy.asmatrix(numpy.random.multivariate_normal(numpy.zeros(__n), Qn, (1,1,1))).T @@ -828,6 +911,12 @@ def etkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q): # Xn = Xfm.reshape((__n,-1)) + EaX @ ( vw.reshape((__m,-1)) + numpy.sqrt(__m-1) * Tdemi @ mU ) # + if selfA._parameters["InflationType"] == "MultiplicativeOnAnalysisAnomalies": + Xn = CovarianceInflation( Xn, + selfA._parameters["InflationType"], + selfA._parameters["InflationFactor"], + ) + # Xa = Xn.mean(axis=1, dtype=mfp).astype('float') # if selfA._parameters["StoreInternalVariables"] \