-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2017 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
+from daCore import BasicObjects, NumericObjects
import numpy
# ==============================================================================
name = "StoreInternalVariables",
default = False,
typecast = bool,
- message = "Stockage des variables internes ou intermédiaires du calcul",
+ message = "Stockage des variables internes ou intermédiaires du calcul",
)
self.defineRequiredParameter(
name = "StoreSupplementaryCalculations",
default = [],
typecast = tuple,
- message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
- listval = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "APosterioriCorrelations",
+ "APosterioriCovariance",
+ "APosterioriStandardDeviations",
+ "APosterioriVariances",
+ "BMA",
+ "CostFunctionJ",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJb",
+ "CostFunctionJbAtCurrentOptimum",
+ "CostFunctionJo",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentOptimum",
+ "CurrentState",
+ "Innovation",
+ "MahalanobisConsistency",
+ "OMA",
+ "OMB",
+ "SampledStateForQuantiles",
+ "SigmaBck2",
+ "SigmaObs2",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ "SimulationQuantiles",
+ ]
)
self.defineRequiredParameter(
name = "Quantiles",
self.defineRequiredParameter(
name = "SetSeed",
typecast = numpy.random.seed,
- message = "Graine fixée pour le générateur aléatoire",
+ message = "Graine fixée pour le générateur aléatoire",
)
self.defineRequiredParameter(
name = "NumberOfSamplesForQuantiles",
default = 100,
typecast = int,
- message = "Nombre d'échantillons simulés pour le calcul des quantiles",
+ message = "Nombre d'échantillons simulés pour le calcul des quantiles",
minval = 1,
)
self.defineRequiredParameter(
name = "SimulationForQuantiles",
default = "Linear",
typecast = str,
- message = "Type de simulation pour l'estimation des quantiles",
+ message = "Type de simulation en estimation des quantiles",
listval = ["Linear", "NonLinear"]
)
+ self.defineRequiredParameter( # Pas de type
+ name = "StateBoundsForQuantiles",
+ message = "Liste des paires de bornes pour les états utilisés en estimation des quantiles",
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B"),
+ )
+ self.setAttributes(tags=(
+ "DataAssimilation",
+ "NonLinear",
+ "Filter",
+ ))
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)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
Hm = HO["Tangent"].asMatrix(Xb)
Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
H = HO["Direct"].appliedTo
#
- # Utilisation éventuelle d'un vecteur H(Xb) précalculé
+ # Utilisation éventuelle d'un vecteur H(Xb) précalculé
# ----------------------------------------------------
- if HO["AppliedToX"] is not None and "HXb" in HO["AppliedToX"]:
- HXb = H( Xb, HO["AppliedToX"]["HXb"])
+ if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
+ HXb = H( Xb, HO["AppliedInX"]["HXb"])
else:
HXb = H( Xb )
HXb = numpy.asmatrix(numpy.ravel( HXb )).T
+ if Y.size != HXb.size:
+ raise ValueError("The size %i of observations Y and %i of observed calculation H(X) are different, they have to be identical."%(Y.size,HXb.size))
+ if max(Y.shape) != max(HXb.shape):
+ raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape))
#
- # Précalcul des inversions de B et R
+ # Précalcul des inversions de B et R
# ----------------------------------
BI = B.getI()
RI = R.getI()
#
# Calcul de l'innovation
# ----------------------
- if Y.size != HXb.size:
- raise ValueError("The size %i of observations Y and %i of observed calculation H(X) are different, they have to be identical."%(Y.size,HXb.size))
- if max(Y.shape) != max(HXb.shape):
- raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape))
d = Y - HXb
#
# Calcul de la matrice de gain et de l'analyse
# --------------------------------------------
if Y.size <= Xb.size:
- _A = R + Hm * B * Ha
+ _A = R + numpy.dot(Hm, B * Ha)
_u = numpy.linalg.solve( _A , d )
Xa = Xb + B * Ha * _u
else:
- _A = BI + Ha * RI * Hm
- _u = numpy.linalg.solve( _A , Ha * RI * d )
+ _A = BI + numpy.dot(Ha, RI * Hm)
+ _u = numpy.linalg.solve( _A , numpy.dot(Ha, RI * d) )
Xa = Xb + _u
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- # Calcul de la fonction coût
+ # Calcul de la fonction coût
# --------------------------
if self._parameters["StoreInternalVariables"] or \
- "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
- "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
- "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("OMA") or \
+ self._toStore("SigmaObs2") or \
+ self._toStore("MahalanobisConsistency") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum") or \
+ self._toStore("SimulatedObservationAtCurrentState") or \
+ self._toStore("SimulatedObservationAtOptimum") or \
+ self._toStore("SimulationQuantiles"):
HXa = numpy.matrix(numpy.ravel( H( Xa ) )).T
oma = Y - HXa
if self._parameters["StoreInternalVariables"] or \
- "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
- "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("MahalanobisConsistency"):
Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
Jo = float( 0.5 * oma.T * RI * oma )
J = Jb + Jo
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
#
# Calcul de la covariance d'analyse
# ---------------------------------
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
- if (Y.size <= Xb.size): K = B * Ha * (R + Hm * B * Ha).I
- elif (Y.size > Xb.size): K = (BI + Ha * RI * Hm).I * Ha * RI
+ if self._toStore("APosterioriCovariance") or \
+ self._toStore("SimulationQuantiles"):
+ if (Y.size <= Xb.size): K = B * Ha * (R + numpy.dot(Hm, B * Ha)).I
+ elif (Y.size > Xb.size): K = (BI + numpy.dot(Ha, RI * Hm)).I * Ha * RI
A = B - K * Hm * B
if min(A.shape) != max(A.shape):
raise ValueError("The %s a posteriori covariance matrix A is of shape %s, despites it has to be a squared matrix. There is an error in the observation operator, please check it."%(self._name,str(A.shape)))
raise ValueError("The %s a posteriori covariance matrix A is not symmetric positive-definite. Please check your a priori covariances and your observation operator."%(self._name,))
self.StoredVariables["APosterioriCovariance"].store( A )
#
- # Calculs et/ou stockages supplémentaires
+ # Calculs et/ou stockages supplémentaires
# ---------------------------------------
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("CurrentOptimum"):
+ self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
+ if self._toStore("Innovation"):
self.StoredVariables["Innovation"].store( numpy.ravel(d) )
- if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("BMA"):
self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
- if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMA"):
self.StoredVariables["OMA"].store( numpy.ravel(oma) )
- if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMB"):
self.StoredVariables["OMB"].store( numpy.ravel(d) )
- if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SigmaObs2"):
TraceR = R.trace(Y.size)
self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
- if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SigmaBck2"):
self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
- if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("MahalanobisConsistency"):
self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
- if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
- Qtls = map(float, self._parameters["Quantiles"])
- nech = self._parameters["NumberOfSamplesForQuantiles"]
+ if self._toStore("SimulationQuantiles"):
HtM = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
HtM = HtM.reshape(Y.size,Xa.size) # ADAO & check shape
- YfQ = None
- for i in range(nech):
- if self._parameters["SimulationForQuantiles"] == "Linear":
- dXr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A) - Xa.A1).T
- dYr = numpy.matrix(numpy.ravel( HtM * dXr )).T
- Yr = HXa + dYr
- elif self._parameters["SimulationForQuantiles"] == "NonLinear":
- Xr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A)).T
- Yr = numpy.matrix(numpy.ravel( H( Xr ) )).T
- if YfQ is None:
- YfQ = Yr
- else:
- YfQ = numpy.hstack((YfQ,Yr))
- YfQ.sort(axis=-1)
- YQ = None
- for quantile in Qtls:
- if not (0. <= quantile <= 1.): continue
- indice = int(nech * quantile - 1./nech)
- if YQ is None: YQ = YfQ[:,indice]
- else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
- self.StoredVariables["SimulationQuantiles"].store( YQ )
- if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ NumericObjects.QuantilesEstimations(self, A, Xa, HXa, H, HtM)
+ if self._toStore("SimulatedObservationAtBackground"):
self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentState"):
self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtOptimum"):
self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
self._post_run(HO)
# ==============================================================================
if __name__ == "__main__":
- print('\n AUTODIAGNOSTIC \n')
+ print('\n AUTODIAGNOSTIC\n')
