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Improvement of algorithms arguments validation and tests
[modules/adao.git]
/
src
/
daComposant
/
daAlgorithms
/
UnscentedKalmanFilter.py
diff --git
a/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py
b/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py
index 43c873069d4a1c1c165af2932bee03a23a11f993..496f5d32399ba54e2ab11932c8c4ba51ffe53b66 100644
(file)
--- a/
src/daComposant/daAlgorithms/UnscentedKalmanFilter.py
+++ b/
src/daComposant/daAlgorithms/UnscentedKalmanFilter.py
@@
-1,6
+1,6
@@
# -*- coding: utf-8 -*-
#
# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-20
19
EDF R&D
+# Copyright (C) 2008-20
20
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
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
@@
-104,9
+104,16
@@
class ElementaryAlgorithm(BasicObjects.Algorithm):
mandatory= ("Xb", "Y", "HO", "R", "B" ),
optional = ("U", "EM", "CM", "Q"),
)
mandatory= ("Xb", "Y", "HO", "R", "B" ),
optional = ("U", "EM", "CM", "Q"),
)
+ self.setAttributes(tags=(
+ "DataAssimilation",
+ "NonLinear",
+ "Filter",
+ "Ensemble",
+ "Dynamic",
+ ))
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=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):
- self._pre_run(Parameters, Xb, Y, R, B, Q)
+ self._pre_run(Parameters, Xb, Y,
U, HO, EM, CM,
R, B, Q)
#
if self._parameters["EstimationOf"] == "Parameters":
self._parameters["StoreInternalVariables"] = True
#
if self._parameters["EstimationOf"] == "Parameters":
self._parameters["StoreInternalVariables"] = True
@@
-164,15
+171,19
@@
class ElementaryAlgorithm(BasicObjects.Algorithm):
#
# Initialisation
# --------------
#
# Initialisation
# --------------
+ __n = Xb.size
Xn = Xb
Xn = Xb
- if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(
Xn.size
)
+ if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(
__n
)
else: Pn = B
#
else: Pn = B
#
- self.StoredVariables["Analysis"].store( Xn.A1 )
- if self._toStore("APosterioriCovariance"):
- self.StoredVariables["APosterioriCovariance"].store( Pn )
- covarianceXa = Pn
- Xa = XaMin = Xb
+ if len(self.StoredVariables["Analysis"])==0 or not self._parameters["nextStep"]:
+ self.StoredVariables["Analysis"].store( numpy.ravel(Xb) )
+ if self._toStore("APosterioriCovariance"):
+ self.StoredVariables["APosterioriCovariance"].store( Pn )
+ covarianceXa = Pn
+ #
+ Xa = Xb
+ XaMin = Xb
previousJMinimum = numpy.finfo(float).max
#
for step in range(duration-1):
previousJMinimum = numpy.finfo(float).max
#
for step in range(duration-1):