-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2020 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
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 = "LengthOfTabuList",
name = "NumberOfElementaryPerturbations",
default = 1,
typecast = int,
- message = "Nombre de perturbations élémentaires pour choisir une perturbation d'état",
+ message = "Nombre de perturbations élémentaires pour choisir une perturbation d'état",
minval = 1,
)
self.defineRequiredParameter(
name = "NoiseDistribution",
default = "Uniform",
typecast = str,
- message = "Distribution pour générer les perturbations d'état",
+ message = "Distribution pour générer les perturbations d'état",
listval = ["Gaussian","Uniform"],
)
self.defineRequiredParameter(
name = "QualityCriterion",
default = "AugmentedWeightedLeastSquares",
typecast = str,
- message = "Critère de qualité utilisé",
+ message = "Critère de qualité utilisé",
listval = ["AugmentedWeightedLeastSquares","AWLS","DA",
"WeightedLeastSquares","WLS",
"LeastSquares","LS","L2",
name = "NoiseHalfRange",
default = [],
typecast = numpy.matrix,
- message = "Demi-amplitude des perturbations uniformes centrées d'état pour chaque composante de l'état",
+ message = "Demi-amplitude des perturbations uniformes centrées d'état pour chaque composante de l'état",
)
self.defineRequiredParameter(
name = "StandardDeviation",
default = [],
typecast = numpy.matrix,
- message = "Ecart-type des perturbations gaussiennes d'état pour chaque composante de l'état",
+ message = "Ecart-type des perturbations gaussiennes d'état pour chaque composante de l'état",
)
self.defineRequiredParameter(
name = "NoiseAddingProbability",
default = 1.,
typecast = float,
- message = "Probabilité de perturbation d'une composante de l'état",
+ message = "Probabilité de perturbation d'une composante de l'état",
minval = 0.,
maxval = 1.,
)
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 = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "BMA",
+ "CurrentState",
+ "CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "Innovation",
+ "OMA",
+ "OMB",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
)
self.defineRequiredParameter( # Pas de type
name = "Bounds",
message = "Liste des valeurs de bornes",
)
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B"),
+ )
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
- # ----------------------
- self.setParameters(Parameters)
- #
- if self._parameters.has_key("Bounds") and (type(self._parameters["Bounds"]) is type([]) or type(self._parameters["Bounds"]) is type(())) and (len(self._parameters["Bounds"]) > 0):
- Bounds = self._parameters["Bounds"]
- logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
- else:
- Bounds = None
+ self._pre_run(Parameters, Xb, Y, R, B, Q)
#
if self._parameters["NoiseDistribution"] == "Uniform":
nrange = numpy.ravel(self._parameters["NoiseHalfRange"]) # Vecteur
if sigma.size != Xb.size:
raise ValueError("Noise generation by Gaussian distribution requires standard deviation for all variable increments. The actual standard deviation vector is:\n%s"%sigma)
#
- # Opérateur d'observation
+ # Opérateur d'observation
# -----------------------
Hm = HO["Direct"].appliedTo
#
- # Précalcul des inversions de B et R
+ # Précalcul des inversions de B et R
# ----------------------------------
BI = B.getI()
RI = R.getI()
#
- # Définition de la fonction de deplacement
+ # Définition de la fonction de deplacement
# ----------------------------------------
def Tweak( x, NoiseDistribution, NoiseAddingProbability ):
- _X = numpy.asmatrix(numpy.ravel( x )).T
+ _X = numpy.matrix(numpy.ravel( x )).T
if NoiseDistribution == "Uniform":
- for i in xrange(_X.size):
+ for i in range(_X.size):
if NoiseAddingProbability >= numpy.random.uniform():
_increment = numpy.random.uniform(low=-nrange[i], high=nrange[i])
- # On ne traite pas encore le dépassement des bornes ici
+ # On ne traite pas encore le dépassement des bornes ici
_X[i] += _increment
elif NoiseDistribution == "Gaussian":
- for i in xrange(_X.size):
+ for i in range(_X.size):
if NoiseAddingProbability >= numpy.random.uniform():
_increment = numpy.random.normal(loc=0., scale=sigma[i])
- # On ne traite pas encore le dépassement des bornes ici
+ # On ne traite pas encore le dépassement des bornes ici
_X[i] += _increment
#
return _X
for state in TL:
if numpy.all(numpy.abs( _X - numpy.ravel(state) ) <= 1e-16*numpy.abs(_X)):
_xInList = True
- if _xInList: sys.exit()
+ # if _xInList: import sys ; sys.exit()
return _xInList
#
# Minimisation de la fonctionnelle
)
for nbt in range(self._parameters["NumberOfElementaryPerturbations"]-1):
_W = Tweak( _S, self._parameters["NoiseDistribution"], self._parameters["NoiseAddingProbability"] )
- # _qualityW = CostFunction( _W, self._parameters["QualityCriterion"] )
+ #Â _qualityW = CostFunction( _W, self._parameters["QualityCriterion"] )
_qualityW = BasicObjects.CostFunction3D(
_W,
_Hm = Hm,
if _qualityS < _qualityBest:
_Best, _qualityBest = _S, _qualityS
#
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
self.StoredVariables["CurrentState"].store( _Best )
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentState"):
_HmX = Hm( numpy.asmatrix(numpy.ravel( _Best )).T )
_HmX = numpy.asmatrix(numpy.ravel( _HmX )).T
self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HmX )
#
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"] or \
- "OMB" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("Innovation") or \
+ self._toStore("OMB") or \
+ self._toStore("SimulatedObservationAtBackground"):
HXb = Hm(Xb)
d = Y - HXb
- if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMA") or \
+ self._toStore("SimulatedObservationAtOptimum"):
HXa = Hm(Xa)
#
- # Calculs et/ou stockages supplémentaires
+ # Calculs et/ou stockages supplémentaires
# ---------------------------------------
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ 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(Y) - numpy.ravel(HXa) )
- if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMB"):
self.StoredVariables["OMB"].store( numpy.ravel(d) )
- if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtBackground"):
self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ 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')
