#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2012 EDF R&D
+# Copyright (C) 2008-2015 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
-m = PlatformInfo.SystemUsage()
-
+from daCore import BasicObjects
import numpy
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
BasicObjects.Algorithm.__init__(self, "BLUE")
+ self.defineRequiredParameter(
+ name = "StoreInternalVariables",
+ default = False,
+ typecast = bool,
+ 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 = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency"]
+ listval = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles"]
+ )
+ self.defineRequiredParameter(
+ name = "Quantiles",
+ default = [],
+ typecast = tuple,
+ message = "Liste des valeurs de quantiles",
+ minval = 0.,
+ maxval = 1.,
+ )
+ self.defineRequiredParameter(
+ name = "SetSeed",
+ typecast = numpy.random.seed,
+ 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",
+ minval = 1,
+ )
+ self.defineRequiredParameter(
+ name = "SimulationForQuantiles",
+ default = "Linear",
+ typecast = str,
+ message = "Type de simulation pour l'estimation des quantiles",
+ listval = ["Linear", "NonLinear"]
)
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
+ 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()
#
# Paramètres de pilotage
# ----------------------
#
# Opérateur d'observation
# -----------------------
- Hm = H["Tangent"].asMatrix(None)
- Ha = H["Adjoint"].asMatrix(None)
+ Hm = HO["Tangent"].asMatrix(Xb)
+ Hm = Hm.reshape(Y.size,Xb.size) # ADAO & check shape
+ Ha = HO["Adjoint"].asMatrix(Xb)
+ Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
#
# Utilisation éventuelle d'un vecteur H(Xb) précalculé
# ----------------------------------------------------
- if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
- HXb = H["AppliedToX"]["HXb"]
+ if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
+ HXb = HO["AppliedToX"]["HXb"]
else:
HXb = Hm * Xb
HXb = numpy.asmatrix(numpy.ravel( HXb )).T
#
# Précalcul des inversions de B et R
# ----------------------------------
- if B is not None:
- BI = B.I
- elif self._parameters["B_scalar"] is not None:
- BI = 1.0 / self._parameters["B_scalar"]
- B = self._parameters["B_scalar"]
- else:
- raise ValueError("Background error covariance matrix has to be properly defined!")
- #
- if R is not None:
- RI = R.I
- elif self._parameters["R_scalar"] is not None:
- RI = 1.0 / self._parameters["R_scalar"]
- else:
- raise ValueError("Observation error covariance matrix has to be properly defined!")
+ BI = B.getI()
+ RI = R.getI()
#
# Calcul de l'innovation
# ----------------------
# Calcul de la matrice de gain et de l'analyse
# --------------------------------------------
if Y.size <= Xb.size:
- if self._parameters["R_scalar"] is not None:
- R = self._parameters["R_scalar"] * numpy.eye(len(Y), dtype=numpy.float)
- K = B * Ha * (Hm * B * Ha + R).I
+ if Y.size > 100: # len(R)
+ _A = R + Hm * B * Ha
+ _u = numpy.linalg.solve( _A , d )
+ Xa = Xb + B * Ha * _u
+ else:
+ K = B * Ha * (R + Hm * B * Ha).I
+ Xa = Xb + K*d
else:
- K = (Ha * RI * Hm + BI).I * Ha * RI
- Xa = Xb + K*d
+ if Y.size > 100: # len(R)
+ _A = BI + Ha * RI * Hm
+ _u = numpy.linalg.solve( _A , Ha * RI * d )
+ Xa = Xb + _u
+ else:
+ K = (BI + Ha * RI * Hm).I * Ha * RI
+ Xa = Xb + K*d
+ self.StoredVariables["Analysis"].store( Xa.A1 )
#
# Calcul de la fonction coût
# --------------------------
- oma = Y - Hm * Xa
- Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
- Jo = 0.5 * oma.T * RI * oma
- J = float( Jb ) + float( Jo )
- self.StoredVariables["Analysis"].store( Xa.A1 )
- self.StoredVariables["CostFunctionJb"].store( Jb )
- self.StoredVariables["CostFunctionJo"].store( Jo )
- self.StoredVariables["CostFunctionJ" ].store( J )
+ if self._parameters["StoreInternalVariables"] or \
+ "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
+ "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ HXa = Hm * Xa
+ oma = Y - HXa
+ if self._parameters["StoreInternalVariables"] or \
+ "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ Jb = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
+ Jo = 0.5 * oma.T * RI * oma
+ J = float( Jb ) + float( Jo )
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
#
# Calcul de la covariance d'analyse
# ---------------------------------
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
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)))
if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["OMB"].store( numpy.ravel(d) )
if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
- if R is not None:
- TraceR = R.trace()
- elif self._parameters["R_scalar"] is not None:
- TraceR = float(self._parameters["R_scalar"]*Y.size)
+ 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"]:
self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
+ if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ Qtls = self._parameters["Quantiles"]
+ nech = self._parameters["NumberOfSamplesForQuantiles"]
+ 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( Hm * 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( Hm * 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 )
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
- #
+ self._post_run(HO)
return 0
# ==============================================================================
