#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2012 EDF R&D
+# Copyright (C) 2008-2014 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
import logging
from daCore import BasicObjects, PlatformInfo
m = PlatformInfo.SystemUsage()
-
-import numpy
-import scipy.optimize
-
-if logging.getLogger().level < logging.WARNING:
- iprint = 1
- message = scipy.optimize.tnc.MSG_ALL
- disp = 1
-else:
- iprint = -1
- message = scipy.optimize.tnc.MSG_NONE
- disp = 0
+import numpy, scipy.optimize
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
default = [],
typecast = tuple,
message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
- listval = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaObs2", "MahalanobisConsistency"]
+ listval = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles"]
+ )
+ self.defineRequiredParameter(
+ name = "Quantiles",
+ default = [],
+ typecast = tuple,
+ message = "Liste des valeurs de quantiles",
+ )
+ 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):
+ def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
+ if logging.getLogger().level < logging.WARNING:
+ self.__iprint, self.__disp = 1, 1
+ self.__message = scipy.optimize.tnc.MSG_ALL
+ else:
+ self.__iprint, self.__disp = -1, 0
+ self.__message = scipy.optimize.tnc.MSG_NONE
+ #
logging.debug("%s Lancement"%self._name)
logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
#
Bounds = None
#
# Correction pour pallier a un bug de TNC sur le retour du Minimum
- if self._parameters.has_key("Minimizer") is "TNC":
+ if self._parameters.has_key("Minimizer") == "TNC":
self.setParameterValue("StoreInternalVariables",True)
#
# Opérateur d'observation
# -----------------------
- Hm = H["Direct"].appliedTo
- Ha = H["Adjoint"].appliedInXTo
+ Hm = HO["Direct"].appliedTo
+ Ha = HO["Adjoint"].appliedInXTo
#
# 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"]
- 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()
#
# Définition de la fonction-coût
# ------------------------------
#
# Minimisation de la fonctionnelle
# --------------------------------
- n0 = self.StoredVariables["CostFunctionJ"].stepnumber()
+ nbPreviousSteps = self.StoredVariables["CostFunctionJ"].stepnumber()
#
if self._parameters["Minimizer"] == "LBFGSB":
Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
maxfun = self._parameters["MaximumNumberOfSteps"]-1,
factr = self._parameters["CostDecrementTolerance"]*1.e14,
pgtol = self._parameters["ProjectedGradientTolerance"],
- iprint = iprint,
+ iprint = self.__iprint,
)
nfeval = Informations['funcalls']
rc = Informations['warnflag']
maxfun = self._parameters["MaximumNumberOfSteps"],
pgtol = self._parameters["ProjectedGradientTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
- messages = message,
+ messages = self.__message,
)
elif self._parameters["Minimizer"] == "CG":
Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
gtol = self._parameters["GradientNormTolerance"],
- disp = disp,
+ disp = self.__disp,
full_output = True,
)
elif self._parameters["Minimizer"] == "NCG":
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
avextol = self._parameters["CostDecrementTolerance"],
- disp = disp,
+ disp = self.__disp,
full_output = True,
)
elif self._parameters["Minimizer"] == "BFGS":
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
gtol = self._parameters["GradientNormTolerance"],
- disp = disp,
+ disp = self.__disp,
full_output = True,
)
else:
raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
#
- StepMin = numpy.argmin( self.StoredVariables["CostFunctionJ"].valueserie()[n0:] )
- MinJ = self.StoredVariables["CostFunctionJ"].valueserie(step = StepMin)
+ 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"]:
- Minimum = self.StoredVariables["CurrentState"].valueserie(step = StepMin)
+ Minimum = self.StoredVariables["CurrentState"][IndexMin]
#
# Obtention de l'analyse
# ----------------------
#
self.StoredVariables["Analysis"].store( Xa.A1 )
#
+ if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ HXa = Hm(Xa)
+ #
# Calcul de la covariance d'analyse
# ---------------------------------
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
- HtM = H["Tangent"].asMatrix(ValueForMethodForm = Xa)
- HtM = HtM.reshape(len(Y),len(Xa.A1)) # ADAO & check shape
- HaM = H["Adjoint"].asMatrix(ValueForMethodForm = Xa)
- HaM = HaM.reshape(len(Xa.A1),len(Y)) # ADAO & check shape
+ if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ HtM = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
+ HtM = HtM.reshape(Y.size,Xa.size) # ADAO & check shape
+ HaM = HO["Adjoint"].asMatrix(ValueForMethodForm = Xa)
+ HaM = HaM.reshape(Xa.size,Y.size) # ADAO & check shape
HessienneI = []
- nb = len(Xa.A1)
+ nb = Xa.size
for i in range(nb):
_ee = numpy.matrix(numpy.zeros(nb)).T
_ee[i] = 1.
_HtEE = numpy.dot(HtM,_ee)
_HtEE = numpy.asmatrix(numpy.ravel( _HtEE )).T
- HessienneI.append( numpy.ravel( numpy.dot(BI,_ee) + numpy.dot(HaM,numpy.dot(RI,_HtEE)) ) )
+ HessienneI.append( numpy.ravel( BI*_ee + HaM * (RI * _HtEE) ) )
HessienneI = numpy.matrix( HessienneI )
A = HessienneI.I
if min(A.shape) != max(A.shape):
- raise ValueError("The 3DVAR 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."%str(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 (numpy.diag(A) < 0).any():
+ raise ValueError("The %s a posteriori covariance matrix A has at least one negative value on its diagonal. There is an error in the observation operator, please check it."%(self._name,))
if logging.getLogger().level < logging.WARNING: # La verification n'a lieu qu'en debug
try:
L = numpy.linalg.cholesky( A )
except:
- raise ValueError("The 3DVAR a posteriori covariance matrix A is not symmetric positive-definite. Check your B and R a priori covariances.")
+ 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
if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(Hm(Xa)) )
+ self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["OMB"].store( numpy.ravel(d) )
if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["SigmaObs2"].store( float( (d.T * (Y-Hm(Xa))) / R.trace() ) )
+ TraceR = R.trace(Y.size)
+ self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(Y)).T-numpy.asmatrix(numpy.ravel(HXa)).T)) ) / TraceR )
if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["MahalanobisConsistency"].store( float( 2.*MinJ/len(d) ) )
+ self.StoredVariables["MahalanobisConsistency"].store( float( 2.*MinJ/d.size ) )
+ if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ Qtls = self._parameters["Quantiles"]
+ nech = self._parameters["NumberOfSamplesForQuantiles"]
+ HXa = numpy.matrix(numpy.ravel( HXa )).T
+ 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( 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 Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint : %i/%i/%i"%(self._name, HO["Direct"].nbcalls()[0],HO["Tangent"].nbcalls()[0],HO["Adjoint"].nbcalls()[0]))
logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
logging.debug("%s Terminé"%self._name)
#
