Documentation and reporting improvements

[modules/adao.git] / src / daComposant / daAlgorithms / ExtendedBlue.py

diff --git a/src/daComposant/daAlgorithms/ExtendedBlue.py b/src/daComposant/daAlgorithms/ExtendedBlue.py
index 73451e01ea5a0d582b57f91ad3184f23aa1da2ef..5b1deee66f60ae0b436244e4c70ff1fc8ae2aa05 100644 (file)
--- a/src/daComposant/daAlgorithms/ExtendedBlue.py
+++ b/src/daComposant/daAlgorithms/ExtendedBlue.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2020 EDF R&D
+# Copyright (C) 2008-2021 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
@@ -21,7 +21,7 @@
 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
-from daCore import BasicObjects
+from daCore import BasicObjects, NumericObjects
 import numpy
 
 # ==============================================================================
@@ -58,6 +58,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 "MahalanobisConsistency",
                 "OMA",
                 "OMB",
+                "SampledStateForQuantiles",
                 "SigmaBck2",
                 "SigmaObs2",
                 "SimulatedObservationAtBackground",
@@ -91,9 +92,13 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             name     = "SimulationForQuantiles",
             default  = "Linear",
             typecast = str,
-            message  = "Type de simulation pour l'estimation des quantiles",
+            message  = "Type de simulation en estimation des quantiles",
             listval  = ["Linear", "NonLinear"]
             )
+        self.defineRequiredParameter( # Pas de type
+            name     = "StateBoundsForQuantiles",
+            message  = "Liste des paires de bornes pour les états utilisés en estimation des quantiles",
+            )
         self.requireInputArguments(
             mandatory= ("Xb", "Y", "HO", "R", "B"),
             )
@@ -215,30 +220,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if self._toStore("MahalanobisConsistency"):
             self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
         if self._toStore("SimulationQuantiles"):
-            nech = self._parameters["NumberOfSamplesForQuantiles"]
             HtM  = HO["Tangent"].asMatrix(ValueForMethodForm = Xa)
             HtM  = HtM.reshape(Y.size,Xa.size) # ADAO & check shape
-            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( H( Xr ) )).T
-                if YfQ is None:
-                    YfQ = Yr
-                else:
-                    YfQ = numpy.hstack((YfQ,Yr))
-            YfQ.sort(axis=-1)
-            YQ = None
-            for quantile in self._parameters["Quantiles"]:
-                if not (0. <= float(quantile) <= 1.): continue
-                indice = int(nech * float(quantile) - 1./nech)
-                if YQ is None: YQ = YfQ[:,indice]
-                else:          YQ = numpy.hstack((YQ,YfQ[:,indice]))
-            self.StoredVariables["SimulationQuantiles"].store( YQ )
+            NumericObjects.QuantilesEstimations(self, A, Xa, HXa, H, HtM)
         if self._toStore("SimulatedObservationAtBackground"):
             self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
         if self._toStore("SimulatedObservationAtCurrentState"):