--- /dev/null
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2008-2023 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
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
+#
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+#
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+#
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+
+import numpy, copy, logging
+from daCore import BasicObjects, PlatformInfo
+mpr = PlatformInfo.PlatformInfo().MachinePrecision()
+mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
+
+# ==============================================================================
+class ElementaryAlgorithm(BasicObjects.Algorithm):
+ def __init__(self):
+ BasicObjects.Algorithm.__init__(self, "OBSERVATIONSIMULATIONCOMPARISONTEST")
+ self.defineRequiredParameter(
+ name = "ShowElementarySummary",
+ default = True,
+ typecast = bool,
+ message = "Calcule et affiche un résumé à chaque évaluation élémentaire",
+ )
+ self.defineRequiredParameter(
+ name = "NumberOfPrintedDigits",
+ default = 5,
+ typecast = int,
+ message = "Nombre de chiffres affichés pour les impressions de réels",
+ minval = 0,
+ )
+ self.defineRequiredParameter(
+ name = "NumberOfRepetition",
+ default = 1,
+ typecast = int,
+ message = "Nombre de fois où l'exécution de la fonction est répétée",
+ minval = 1,
+ )
+ self.defineRequiredParameter(
+ name = "ResultTitle",
+ default = "",
+ typecast = str,
+ message = "Titre du tableau et de la figure",
+ )
+ self.defineRequiredParameter(
+ name = "SetDebug",
+ default = False,
+ typecast = bool,
+ message = "Activation du mode debug lors de l'exécution",
+ )
+ self.defineRequiredParameter(
+ name = "StoreSupplementaryCalculations",
+ default = [],
+ typecast = tuple,
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CurrentState",
+ "Innovation",
+ "InnovationAtCurrentState",
+ "SimulatedObservationAtCurrentState",
+ ]
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B"),
+ )
+ self.setAttributes(tags=(
+ "Checking",
+ ))
+
+ 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, U, HO, EM, CM, R, B, Q)
+ #
+ Hm = HO["Direct"].appliedTo
+ #
+ X0 = copy.copy( Xb )
+ Y0 = copy.copy( Y )
+ #
+ # ----------
+ if len(self._parameters["StoreSupplementaryCalculations"]) > 0:
+ BI = B.getI()
+ RI = R.getI()
+ def CostFunction(x,HmX):
+ _X = numpy.ravel( x )
+ _HX = numpy.ravel( HmX )
+ _X0 = numpy.ravel( X0 )
+ _Y0 = numpy.ravel( Y0 )
+ Jb = float( 0.5 * (_X - _X0).T * (BI * (_X - _X0)) )
+ Jo = float( 0.5 * (_Y0 - _HX).T * (RI * (_Y0 - _HX)) )
+ J = Jb + Jo
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
+ return J, Jb, Jo
+ # ----------
+ __s = self._parameters["ShowElementarySummary"]
+ __p = self._parameters["NumberOfPrintedDigits"]
+ __r = self._parameters["NumberOfRepetition"]
+ #
+ __marge = 5*u" "
+ __flech = 3*"="+"> "
+ msgs = ("\n") # 1
+ if len(self._parameters["ResultTitle"]) > 0:
+ __rt = str(self._parameters["ResultTitle"])
+ msgs += (__marge + "====" + "="*len(__rt) + "====\n")
+ msgs += (__marge + " " + __rt + "\n")
+ msgs += (__marge + "====" + "="*len(__rt) + "====\n")
+ else:
+ msgs += (__marge + "%s\n"%self._name)
+ msgs += (__marge + "%s\n"%("="*len(self._name),))
+ #
+ msgs += ("\n")
+ msgs += (__marge + "This test allows to analyze the (repetition of the) launch of some\n")
+ msgs += (__marge + "given simulation operator F, applied to one single vector argument x,\n")
+ msgs += (__marge + "and its (repeated) comparison to observations or measures y.\n")
+ msgs += (__marge + "The output shows simple statistics related to its successful execution,\n")
+ msgs += (__marge + "or related to the similarities of repetition of its execution.\n")
+ msgs += ("\n")
+ msgs += (__flech + "Information before launching:\n")
+ msgs += (__marge + "-----------------------------\n")
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of input vector X, internally converted:\n")
+ msgs += (__marge + " Type...............: %s\n")%type( X0 )
+ msgs += (__marge + " Length of vector...: %i\n")%max(numpy.ravel( X0 ).shape)
+ msgs += (__marge + " Minimum value......: %."+str(__p)+"e\n")%numpy.min( X0 )
+ msgs += (__marge + " Maximum value......: %."+str(__p)+"e\n")%numpy.max( X0 )
+ msgs += (__marge + " Mean of vector.....: %."+str(__p)+"e\n")%numpy.mean( X0, dtype=mfp )
+ msgs += (__marge + " Standard error.....: %."+str(__p)+"e\n")%numpy.std( X0, dtype=mfp )
+ msgs += (__marge + " L2 norm of vector..: %."+str(__p)+"e\n")%numpy.linalg.norm( X0 )
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of input vector of observations Yobs, internally converted:\n")
+ msgs += (__marge + " Type...............: %s\n")%type( Y0 )
+ msgs += (__marge + " Length of vector...: %i\n")%max(numpy.ravel( Y0 ).shape)
+ msgs += (__marge + " Minimum value......: %."+str(__p)+"e\n")%numpy.min( Y0 )
+ msgs += (__marge + " Maximum value......: %."+str(__p)+"e\n")%numpy.max( Y0 )
+ msgs += (__marge + " Mean of vector.....: %."+str(__p)+"e\n")%numpy.mean( Y0, dtype=mfp )
+ msgs += (__marge + " Standard error.....: %."+str(__p)+"e\n")%numpy.std( Y0, dtype=mfp )
+ msgs += (__marge + " L2 norm of vector..: %."+str(__p)+"e\n")%numpy.linalg.norm( Y0 )
+ msgs += ("\n")
+ msgs += (__marge + "%s\n\n"%("-"*75,))
+ #
+ if self._parameters["SetDebug"]:
+ CUR_LEVEL = logging.getLogger().getEffectiveLevel()
+ logging.getLogger().setLevel(logging.DEBUG)
+ if __r > 1:
+ msgs += (__flech + "Beginning of repeated evaluation, activating debug\n")
+ else:
+ msgs += (__flech + "Beginning of evaluation, activating debug\n")
+ else:
+ if __r > 1:
+ msgs += (__flech + "Beginning of repeated evaluation, without activating debug\n")
+ else:
+ msgs += (__flech + "Beginning of evaluation, without activating debug\n")
+ print(msgs) # 1
+ #
+ # ----------
+ HO["Direct"].disableAvoidingRedundancy()
+ # ----------
+ Ys = []
+ Ds = []
+ Js = []
+ _Y0 = numpy.ravel( Y0 )
+ for i in range(__r):
+ if self._toStore("CurrentState"):
+ self.StoredVariables["CurrentState"].store( X0 )
+ if __s:
+ msgs = (__marge + "%s\n"%("-"*75,)) # 2-1
+ if __r > 1:
+ msgs += ("\n")
+ msgs += (__flech + "Repetition step number %i on a total of %i\n"%(i+1,__r))
+ msgs += ("\n")
+ msgs += (__flech + "Launching operator sequential evaluation\n")
+ print(msgs) # 2-1
+ #
+ Yn = Hm( X0 )
+ #
+ if _Y0.size != Yn.size:
+ raise ValueError("The size %i of observations Y and %i of observed calculation F(X) are different, they have to be identical."%(Y0.size,Yn.size))
+ #
+ Dn = _Y0 - numpy.ravel( Yn )
+ #
+ if __s:
+ msgs = ("\n") # 2-2
+ msgs += (__flech + "End of operator sequential evaluation\n")
+ msgs += ("\n")
+ msgs += (__flech + "Information after evaluation:\n")
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of simulated output vector Y=F(X), to compare to others:\n")
+ msgs += (__marge + " Type...............: %s\n")%type( Yn )
+ msgs += (__marge + " Length of vector...: %i\n")%max(numpy.ravel( Yn ).shape)
+ msgs += (__marge + " Minimum value......: %."+str(__p)+"e\n")%numpy.min( Yn )
+ msgs += (__marge + " Maximum value......: %."+str(__p)+"e\n")%numpy.max( Yn )
+ msgs += (__marge + " Mean of vector.....: %."+str(__p)+"e\n")%numpy.mean( Yn, dtype=mfp )
+ msgs += (__marge + " Standard error.....: %."+str(__p)+"e\n")%numpy.std( Yn, dtype=mfp )
+ msgs += (__marge + " L2 norm of vector..: %."+str(__p)+"e\n")%numpy.linalg.norm( Yn )
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of increment between observations Yobs and simulated output vector Y=F(X):\n")
+ msgs += (__marge + " Type...............: %s\n")%type( Dn )
+ msgs += (__marge + " Length of vector...: %i\n")%max(numpy.ravel( Dn ).shape)
+ msgs += (__marge + " Minimum value......: %."+str(__p)+"e\n")%numpy.min( Dn )
+ msgs += (__marge + " Maximum value......: %."+str(__p)+"e\n")%numpy.max( Dn )
+ msgs += (__marge + " Mean of vector.....: %."+str(__p)+"e\n")%numpy.mean( Dn, dtype=mfp )
+ msgs += (__marge + " Standard error.....: %."+str(__p)+"e\n")%numpy.std( Dn, dtype=mfp )
+ msgs += (__marge + " L2 norm of vector..: %."+str(__p)+"e\n")%numpy.linalg.norm( Dn )
+ if len(self._parameters["StoreSupplementaryCalculations"]) > 0:
+ J, Jb, Jo = CostFunction( X0, Yn )
+ if self._toStore("CostFunctionJ"):
+ Js.append( J )
+ msgs += ("\n")
+ msgs += (__marge + " Cost function J....: %."+str(__p)+"e\n")%J
+ msgs += (__marge + " Cost function Jb=0.: %."+str(__p)+"e\n")%Jb
+ msgs += (__marge + " Cost function Jo...: %."+str(__p)+"e\n")%Jo
+ print(msgs) # 2-2
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(Yn) )
+ if self._toStore("Innovation"):
+ self.StoredVariables["Innovation"].store( Dn )
+ if self._toStore("InnovationAtCurrentState"):
+ self.StoredVariables["InnovationAtCurrentState"].store( Dn )
+ #
+ Ys.append( copy.copy( numpy.ravel(
+ Yn
+ ) ) )
+ Ds.append( copy.copy( numpy.ravel(
+ Dn
+ ) ) )
+ # ----------
+ HO["Direct"].enableAvoidingRedundancy()
+ # ----------
+ #
+ msgs = (__marge + "%s\n\n"%("-"*75,)) # 3
+ if self._parameters["SetDebug"]:
+ if __r > 1:
+ msgs += (__flech + "End of repeated evaluation, deactivating debug if necessary\n")
+ else:
+ msgs += (__flech + "End of evaluation, deactivating debug if necessary\n")
+ logging.getLogger().setLevel(CUR_LEVEL)
+ else:
+ if __r > 1:
+ msgs += (__flech + "End of repeated evaluation, without deactivating debug\n")
+ else:
+ msgs += (__flech + "End of evaluation, without deactivating debug\n")
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ #
+ if __r > 1:
+ msgs += ("\n")
+ msgs += (__flech + "Launching statistical summary calculation for %i states\n"%__r)
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ msgs += ("\n")
+ msgs += (__flech + "Statistical analysis of the outputs obtained through sequential repeated evaluations\n")
+ msgs += ("\n")
+ msgs += (__marge + "(Remark: numbers that are (about) under %.0e represent 0 to machine precision)\n"%mpr)
+ msgs += ("\n")
+ Yy = numpy.array( Ys )
+ msgs += (__marge + "Number of evaluations...........................: %i\n")%len( Ys )
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of the whole set of outputs Y:\n")
+ msgs += (__marge + " Size of each of the outputs...................: %i\n")%Ys[0].size
+ msgs += (__marge + " Minimum value of the whole set of outputs.....: %."+str(__p)+"e\n")%numpy.min( Yy )
+ msgs += (__marge + " Maximum value of the whole set of outputs.....: %."+str(__p)+"e\n")%numpy.max( Yy )
+ msgs += (__marge + " Mean of vector of the whole set of outputs....: %."+str(__p)+"e\n")%numpy.mean( Yy, dtype=mfp )
+ msgs += (__marge + " Standard error of the whole set of outputs....: %."+str(__p)+"e\n")%numpy.std( Yy, dtype=mfp )
+ msgs += ("\n")
+ Ym = numpy.mean( numpy.array( Ys ), axis=0, dtype=mfp )
+ msgs += (__marge + "Characteristics of the vector Ym, mean of the outputs Y:\n")
+ msgs += (__marge + " Size of the mean of the outputs...............: %i\n")%Ym.size
+ msgs += (__marge + " Minimum value of the mean of the outputs......: %."+str(__p)+"e\n")%numpy.min( Ym )
+ msgs += (__marge + " Maximum value of the mean of the outputs......: %."+str(__p)+"e\n")%numpy.max( Ym )
+ msgs += (__marge + " Mean of the mean of the outputs...............: %."+str(__p)+"e\n")%numpy.mean( Ym, dtype=mfp )
+ msgs += (__marge + " Standard error of the mean of the outputs.....: %."+str(__p)+"e\n")%numpy.std( Ym, dtype=mfp )
+ msgs += ("\n")
+ Ye = numpy.mean( numpy.array( Ys ) - Ym, axis=0, dtype=mfp )
+ msgs += (__marge + "Characteristics of the mean of the differences between the outputs Y and their mean Ym:\n")
+ msgs += (__marge + " Size of the mean of the differences...........: %i\n")%Ye.size
+ msgs += (__marge + " Minimum value of the mean of the differences..: %."+str(__p)+"e\n")%numpy.min( Ye )
+ msgs += (__marge + " Maximum value of the mean of the differences..: %."+str(__p)+"e\n")%numpy.max( Ye )
+ msgs += (__marge + " Mean of the mean of the differences...........: %."+str(__p)+"e\n")%numpy.mean( Ye, dtype=mfp )
+ msgs += (__marge + " Standard error of the mean of the differences.: %."+str(__p)+"e\n")%numpy.std( Ye, dtype=mfp )
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ msgs += ("\n")
+ msgs += (__flech + "Statistical analysis of the increments obtained through sequential repeated evaluations\n")
+ msgs += ("\n")
+ msgs += (__marge + "(Remark: numbers that are (about) under %.0e represent 0 to machine precision)\n"%mpr)
+ msgs += ("\n")
+ Dy = numpy.array( Ds )
+ msgs += (__marge + "Number of evaluations...........................: %i\n")%len( Ds )
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of the whole set of increments D:\n")
+ msgs += (__marge + " Size of each of the outputs...................: %i\n")%Ds[0].size
+ msgs += (__marge + " Minimum value of the whole set of increments..: %."+str(__p)+"e\n")%numpy.min( Dy )
+ msgs += (__marge + " Maximum value of the whole set of increments..: %."+str(__p)+"e\n")%numpy.max( Dy )
+ msgs += (__marge + " Mean of vector of the whole set of increments.: %."+str(__p)+"e\n")%numpy.mean( Dy, dtype=mfp )
+ msgs += (__marge + " Standard error of the whole set of increments.: %."+str(__p)+"e\n")%numpy.std( Dy, dtype=mfp )
+ msgs += ("\n")
+ Dm = numpy.mean( numpy.array( Ds ), axis=0, dtype=mfp )
+ msgs += (__marge + "Characteristics of the vector Dm, mean of the increments D:\n")
+ msgs += (__marge + " Size of the mean of the increments............: %i\n")%Dm.size
+ msgs += (__marge + " Minimum value of the mean of the increments...: %."+str(__p)+"e\n")%numpy.min( Dm )
+ msgs += (__marge + " Maximum value of the mean of the increments...: %."+str(__p)+"e\n")%numpy.max( Dm )
+ msgs += (__marge + " Mean of the mean of the increments............: %."+str(__p)+"e\n")%numpy.mean( Dm, dtype=mfp )
+ msgs += (__marge + " Standard error of the mean of the increments..: %."+str(__p)+"e\n")%numpy.std( Dm, dtype=mfp )
+ msgs += ("\n")
+ De = numpy.mean( numpy.array( Ds ) - Dm, axis=0, dtype=mfp )
+ msgs += (__marge + "Characteristics of the mean of the differences between the increments D and their mean Dm:\n")
+ msgs += (__marge + " Size of the mean of the differences...........: %i\n")%De.size
+ msgs += (__marge + " Minimum value of the mean of the differences..: %."+str(__p)+"e\n")%numpy.min( De )
+ msgs += (__marge + " Maximum value of the mean of the differences..: %."+str(__p)+"e\n")%numpy.max( De )
+ msgs += (__marge + " Mean of the mean of the differences...........: %."+str(__p)+"e\n")%numpy.mean( De, dtype=mfp )
+ msgs += (__marge + " Standard error of the mean of the differences.: %."+str(__p)+"e\n")%numpy.std( De, dtype=mfp )
+ #
+ if self._toStore("CostFunctionJ"):
+ msgs += ("\n")
+ Jj = numpy.array( Js )
+ msgs += (__marge + "%s\n\n"%("-"*75,))
+ msgs += (__marge + "Number of evaluations...........................: %i\n")%len( Js )
+ msgs += ("\n")
+ msgs += (__marge + "Characteristics of the whole set of cost function J values:\n")
+ msgs += (__marge + " Minimum value of the whole set of J...........: %."+str(__p)+"e\n")%numpy.min( Jj )
+ msgs += (__marge + " Maximum value of the whole set of J...........: %."+str(__p)+"e\n")%numpy.max( Jj )
+ msgs += (__marge + " Mean of vector of the whole set of J..........: %."+str(__p)+"e\n")%numpy.mean( Jj, dtype=mfp )
+ msgs += (__marge + " Standard error of the whole set of J..........: %."+str(__p)+"e\n")%numpy.std( Jj, dtype=mfp )
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ #
+ msgs += ("\n")
+ msgs += (__marge + "End of the \"%s\" verification\n\n"%self._name)
+ msgs += (__marge + "%s\n"%("-"*75,))
+ print(msgs) # 3
+ #
+ self._post_run(HO)
+ return 0
+
+# ==============================================================================
+if __name__ == "__main__":
+ print('\n AUTODIAGNOSTIC\n')
