-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2016 EDF R&D
+# Copyright (C) 2008-2019 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
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", "CurrentState", "CostFunctionJ", "Innovation"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "APosterioriCorrelations",
+ "APosterioriCovariance",
+ "APosterioriStandardDeviations",
+ "APosterioriVariances",
+ "BMA",
+ "CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CurrentState",
+ "InnovationAtCurrentState",
+ ]
)
self.defineRequiredParameter( # Pas de type
name = "Bounds",
message = "Liste des valeurs de bornes",
)
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B" ),
+ optional = ("U", "EM", "CM", "Q"),
+ )
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)
+ self._pre_run(Parameters, Xb, Y, R, B, Q)
#
- 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
if self._parameters["EstimationOf"] == "Parameters":
self._parameters["StoreInternalVariables"] = True
#
Wc = numpy.array( Ww )
Wc[0] = Lambda / (L + Lambda) + (1. - Alpha**2 + Beta)
#
- # Opérateurs
+ # Opérateurs
# ----------
- if B is None:
- raise ValueError("Background error covariance matrix has to be properly defined!")
- if R is None:
- raise ValueError("Observation error covariance matrix has to be properly defined!")
- #
- H = HO["Direct"].appliedControledFormTo
+ Hm = HO["Direct"].appliedControledFormTo
#
if self._parameters["EstimationOf"] == "State":
- M = EM["Direct"].appliedControledFormTo
+ Mm = EM["Direct"].appliedControledFormTo
#
- if CM is not None and CM.has_key("Tangent") and U is not None:
+ if CM is not None and "Tangent" in CM and U is not None:
Cm = CM["Tangent"].asMatrix(Xb)
else:
Cm = None
else:
duration = 2
#
- # Précalcul des inversions de B et R
+ # Précalcul des inversions de B et R
# ----------------------------------
- if self._parameters["StoreInternalVariables"]:
+ if self._parameters["StoreInternalVariables"] \
+ or self._toStore("CostFunctionJ") \
+ or self._toStore("CostFunctionJb") \
+ or self._toStore("CostFunctionJo"):
BI = B.getI()
RI = R.getI()
#
# Initialisation
# --------------
Xn = Xb
- if hasattr(B,"asfullmatrix"):
- Pn = B.asfullmatrix(Xn.size)
- else:
- Pn = B
+ if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(Xn.size)
+ else: Pn = B
#
self.StoredVariables["Analysis"].store( Xn.A1 )
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("APosterioriCovariance"):
self.StoredVariables["APosterioriCovariance"].store( Pn )
covarianceXa = Pn
- Xa = Xn
+ Xa = XaMin = Xb
previousJMinimum = numpy.finfo(float).max
#
for step in range(duration-1):
Xnp = numpy.hstack([Xn, Xn+Gamma*Pndemi, Xn-Gamma*Pndemi])
nbSpts = 2*Xn.size+1
#
- if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+ if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
for point in range(nbSpts):
- Xnp[:,point] = numpy.max(numpy.hstack((Xnp[:,point],numpy.asmatrix(Bounds)[:,0])),axis=1)
- Xnp[:,point] = numpy.min(numpy.hstack((Xnp[:,point],numpy.asmatrix(Bounds)[:,1])),axis=1)
+ Xnp[:,point] = numpy.max(numpy.hstack((Xnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
+ Xnp[:,point] = numpy.min(numpy.hstack((Xnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
#
XEtnnp = []
for point in range(nbSpts):
if self._parameters["EstimationOf"] == "State":
- XEtnnpi = numpy.asmatrix(numpy.ravel( M( (Xnp[:,point], Un) ) )).T
+ XEtnnpi = numpy.asmatrix(numpy.ravel( Mm( (Xnp[:,point], Un) ) )).T
if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
Cm = Cm.reshape(Xn.size,Un.size) # ADAO & check shape
XEtnnpi = XEtnnpi + Cm * Un
- if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
- XEtnnpi = numpy.max(numpy.hstack((XEtnnpi,numpy.asmatrix(Bounds)[:,0])),axis=1)
- XEtnnpi = numpy.min(numpy.hstack((XEtnnpi,numpy.asmatrix(Bounds)[:,1])),axis=1)
+ if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+ XEtnnpi = numpy.max(numpy.hstack((XEtnnpi,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
+ XEtnnpi = numpy.min(numpy.hstack((XEtnnpi,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
elif self._parameters["EstimationOf"] == "Parameters":
# --- > Par principe, M = Id, Q = 0
XEtnnpi = Xnp[:,point]
#
Xncm = numpy.matrix( XEtnnp.getA()*numpy.array(Wm) ).sum(axis=1)
#
- if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
- Xncm = numpy.max(numpy.hstack((Xncm,numpy.asmatrix(Bounds)[:,0])),axis=1)
- Xncm = numpy.min(numpy.hstack((Xncm,numpy.asmatrix(Bounds)[:,1])),axis=1)
+ if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+ Xncm = numpy.max(numpy.hstack((Xncm,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
+ Xncm = numpy.min(numpy.hstack((Xncm,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
#
if self._parameters["EstimationOf"] == "State": Pnm = Q
elif self._parameters["EstimationOf"] == "Parameters": Pnm = 0.
for point in range(nbSpts):
Pnm += Wc[i] * (XEtnnp[:,point]-Xncm) * (XEtnnp[:,point]-Xncm).T
#
- if self._parameters["EstimationOf"] == "Parameters" and Bounds is not None:
+ if self._parameters["EstimationOf"] == "Parameters" and self._parameters["Bounds"] is not None:
Pnmdemi = self._parameters["Reconditioner"] * numpy.linalg.cholesky(Pnm)
else:
Pnmdemi = numpy.linalg.cholesky(Pnm)
#
Xnnp = numpy.hstack([Xncm, Xncm+Gamma*Pnmdemi, Xncm-Gamma*Pnmdemi])
#
- if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+ if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
for point in range(nbSpts):
- Xnnp[:,point] = numpy.max(numpy.hstack((Xnnp[:,point],numpy.asmatrix(Bounds)[:,0])),axis=1)
- Xnnp[:,point] = numpy.min(numpy.hstack((Xnnp[:,point],numpy.asmatrix(Bounds)[:,1])),axis=1)
+ Xnnp[:,point] = numpy.max(numpy.hstack((Xnnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
+ Xnnp[:,point] = numpy.min(numpy.hstack((Xnnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
#
Ynnp = []
for point in range(nbSpts):
if self._parameters["EstimationOf"] == "State":
- Ynnpi = numpy.asmatrix(numpy.ravel( H( (Xnnp[:,point], None) ) )).T
+ Ynnpi = numpy.asmatrix(numpy.ravel( Hm( (Xnnp[:,point], None) ) )).T
elif self._parameters["EstimationOf"] == "Parameters":
- Ynnpi = numpy.asmatrix(numpy.ravel( H( (Xnnp[:,point], Un) ) )).T
+ Ynnpi = numpy.asmatrix(numpy.ravel( Hm( (Xnnp[:,point], Un) ) )).T
Ynnp.append( Ynnpi )
Ynnp = numpy.hstack( Ynnp )
#
Xn = Xncm + Kn * d
Pn = Pnm - Kn * Pyyn * Kn.T
#
- if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
- Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(Bounds)[:,0])),axis=1)
- Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(Bounds)[:,1])),axis=1)
+ if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+ Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
+ Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
+ Xa = Xn # Pointeurs
#
- self.StoredVariables["Analysis"].store( Xn.A1 )
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ # ---> avec analysis
+ self.StoredVariables["Analysis"].store( Xa )
+ if self._toStore("APosterioriCovariance"):
self.StoredVariables["APosterioriCovariance"].store( Pn )
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["Innovation"].store( numpy.ravel( d.A1 ) )
- if self._parameters["StoreInternalVariables"]:
- Jb = 0.5 * (Xn - Xb).T * BI * (Xn - Xb)
- Jo = 0.5 * d.T * RI * d
- J = float( Jb ) + float( Jo )
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["CurrentState"].store( Xn )
+ # ---> avec current state
+ if self._toStore("InnovationAtCurrentState"):
+ self.StoredVariables["InnovationAtCurrentState"].store( d )
+ if self._parameters["StoreInternalVariables"] \
+ or self._toStore("CurrentState"):
+ self.StoredVariables["CurrentState"].store( Xn )
+ if self._parameters["StoreInternalVariables"] \
+ or self._toStore("CostFunctionJ") \
+ or self._toStore("CostFunctionJb") \
+ or self._toStore("CostFunctionJo"):
+ Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
+ Jo = float( 0.5 * d.T * RI * d )
+ J = Jb + Jo
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
- if J < previousJMinimum:
- previousJMinimum = J
- Xa = Xn
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
- covarianceXa = Pn
- else:
- Xa = Xn
- #
+ if self._parameters["EstimationOf"] == "Parameters" \
+ and J < previousJMinimum:
+ previousJMinimum = J
+ XaMin = Xa
+ if self._toStore("APosterioriCovariance"):
+ covarianceXaMin = Pn
#
- # Stockage supplementaire de l'optimum en estimation de parametres
- # ----------------------------------------------------------------
+ # Stockage final supplémentaire de l'optimum en estimation de paramètres
+ # ----------------------------------------------------------------------
if self._parameters["EstimationOf"] == "Parameters":
- self.StoredVariables["Analysis"].store( Xa.A1 )
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["APosterioriCovariance"].store( covarianceXa )
- #
- if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ self.StoredVariables["Analysis"].store( XaMin )
+ if self._toStore("APosterioriCovariance"):
+ self.StoredVariables["APosterioriCovariance"].store( covarianceXaMin )
+ if self._toStore("BMA"):
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) )
#
self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')
