-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2014 EDF R&D
+# Copyright (C) 2008-2017 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 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.
+# 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
+# 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
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
#
-# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
-import logging
+import sys, logging
from daCore import BasicObjects, PlatformInfo
-m = PlatformInfo.SystemUsage()
import numpy, copy
+mpr = PlatformInfo.PlatformInfo().MachinePrecision()
+mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
+if sys.version_info.major > 2:
+ unicode = str
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
name = "NumberOfPrintedDigits",
default = 5,
typecast = int,
- message = "Nombre de chiffres affichés pour les impressions de réels",
+ 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",
+ message = "Nombre de fois où l'exécution de la fonction est répétée",
minval = 1,
)
self.defineRequiredParameter(
)
self.defineRequiredParameter(
name = "SetDebug",
- default = True,
+ default = False,
typecast = bool,
- message = "Activation du mode debug lors de l'exécution",
+ 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 = ["CurrentState", "SimulatedObservationAtCurrentState"]
)
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- #
- self.setParameters(Parameters)
+ self._pre_run(Parameters)
#
Hm = HO["Direct"].appliedTo
#
- Xn = numpy.asmatrix(numpy.ravel( Xb )).T
+ Xn = copy.copy( Xb )
#
# ----------
+ __marge = 5*u" "
_p = self._parameters["NumberOfPrintedDigits"]
if len(self._parameters["ResultTitle"]) > 0:
- msg = " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
- msg += " " + self._parameters["ResultTitle"] + "\n"
- msg += " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
- print("%s"%msg)
+ __rt = unicode(self._parameters["ResultTitle"])
+ msgs = u"\n"
+ msgs += __marge + "====" + "="*len(__rt) + "====\n"
+ msgs += __marge + " " + __rt + "\n"
+ msgs += __marge + "====" + "="*len(__rt) + "====\n"
+ print("%s"%msgs)
#
- msg = "===> Information before launching:\n"
- msg += " -----------------------------\n"
- msg += " Characteristics of input vector X, internally converted:\n"
- msg += " Type...............: %s\n"%type( Xn )
- msg += " Lenght of vector...: %i\n"%max(numpy.matrix( Xn ).shape)
- msg += (" Minimum value......: %."+str(_p)+"e\n")%numpy.min( Xn )
- msg += (" Maximum value......: %."+str(_p)+"e\n")%numpy.max( Xn )
- msg += (" Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Xn )
- msg += (" Standard error.....: %."+str(_p)+"e\n")%numpy.std( Xn )
- msg += (" L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Xn )
- print(msg)
+ msgs = ("===> Information before launching:\n")
+ msgs += (" -----------------------------\n")
+ msgs += (" Characteristics of input vector X, internally converted:\n")
+ msgs += (" Type...............: %s\n")%type( Xn )
+ msgs += (" Lenght of vector...: %i\n")%max(numpy.matrix( Xn ).shape)
+ msgs += (" Minimum value......: %."+str(_p)+"e\n")%numpy.min( Xn )
+ msgs += (" Maximum value......: %."+str(_p)+"e\n")%numpy.max( Xn )
+ msgs += (" Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Xn, dtype=mfp )
+ msgs += (" Standard error.....: %."+str(_p)+"e\n")%numpy.std( Xn, dtype=mfp )
+ msgs += (" L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Xn )
+ print(msgs)
#
if self._parameters["SetDebug"]:
CUR_LEVEL = logging.getLogger().getEffectiveLevel()
print("===> Beginning of evaluation, without activating debug\n")
#
# ----------
+ HO["Direct"].disableAvoidingRedundancy()
+ # ----------
Ys = []
for i in range(self._parameters["NumberOfRepetition"]):
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn) )
print(" %s\n"%("-"*75,))
if self._parameters["NumberOfRepetition"] > 1:
print("===> Repetition step number %i on a total of %i\n"%(i+1,self._parameters["NumberOfRepetition"]))
print("===> Launching direct operator evaluation\n")
#
- Y = Hm( Xn )
+ Yn = Hm( Xn )
#
print("\n===> End of direct operator evaluation\n")
#
- msg = ("===> Information after evaluation:\n")
- msg += ("\n Characteristics of output vector Y, to compare to other calculations:\n")
- msg += (" Type...............: %s\n")%type( Y )
- msg += (" Lenght of vector...: %i\n")%max(numpy.matrix( Y ).shape)
- msg += (" Minimum value......: %."+str(_p)+"e\n")%numpy.min( Y )
- msg += (" Maximum value......: %."+str(_p)+"e\n")%numpy.max( Y )
- msg += (" Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Y )
- msg += (" Standard error.....: %."+str(_p)+"e\n")%numpy.std( Y )
- msg += (" L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Y )
- print(msg)
+ msgs = ("===> Information after evaluation:\n")
+ msgs += ("\n Characteristics of simulated output vector Y=H(X), to compare to others:\n")
+ msgs += (" Type...............: %s\n")%type( Yn )
+ msgs += (" Lenght of vector...: %i\n")%max(numpy.matrix( Yn ).shape)
+ msgs += (" Minimum value......: %."+str(_p)+"e\n")%numpy.min( Yn )
+ msgs += (" Maximum value......: %."+str(_p)+"e\n")%numpy.max( Yn )
+ msgs += (" Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Yn, dtype=mfp )
+ msgs += (" Standard error.....: %."+str(_p)+"e\n")%numpy.std( Yn, dtype=mfp )
+ msgs += (" L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Yn )
+ print(msgs)
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(Yn) )
#
- Ys.append( copy.copy( numpy.ravel(Y) ) )
+ Ys.append( copy.copy( numpy.ravel(
+ Yn
+ ) ) )
+ # ----------
+ HO["Direct"].enableAvoidingRedundancy()
+ # ----------
#
print(" %s\n"%("-"*75,))
if self._parameters["SetDebug"]:
logging.getLogger().setLevel(CUR_LEVEL)
#
if self._parameters["NumberOfRepetition"] > 1:
- msg = (" %s\n"%("-"*75,))
- msg += ("\n===> Statistical analysis of the outputs obtained throught repeated evaluations\n")
+ msgs = (" %s\n"%("-"*75,))
+ msgs += ("\n===> Statistical analysis of the outputs obtained throught repeated evaluations\n")
+ msgs += ("\n (Remark: numbers that are (about) under %.0e represent 0 to machine precision)\n"%mpr)
Yy = numpy.array( Ys )
- msg += ("\n Characteristics of the whole set of outputs Y:\n")
- msg += (" Number of evaluations.........................: %i\n")%len( Ys )
- msg += (" Minimum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.min( Yy )
- msg += (" Maximum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.max( Yy )
- msg += (" Mean of vector of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.mean( Yy )
- msg += (" Standard error of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.std( Yy )
- Ym = numpy.mean( numpy.array( Ys ), axis=0 )
- msg += ("\n Characteristics of the vector Ym, mean of the outputs Y:\n")
- msg += (" Size of the mean of the outputs...............: %i\n")%Ym.size
- msg += (" Minimum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.min( Ym )
- msg += (" Maximum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.max( Ym )
- msg += (" Mean of the mean of the outputs...............: %."+str(_p)+"e\n")%numpy.mean( Ym )
- msg += (" Standard error of the mean of the outputs.....: %."+str(_p)+"e\n")%numpy.std( Ym )
- Ye = numpy.mean( numpy.array( Ys ) - Ym, axis=0 )
- msg += "\n Characteristics of the mean of the differences between the outputs Y and their mean Ym:\n"
- msg += (" Size of the mean of the differences...........: %i\n")%Ym.size
- msg += (" Minimum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.min( Ye )
- msg += (" Maximum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.max( Ye )
- msg += (" Mean of the mean of the differences...........: %."+str(_p)+"e\n")%numpy.mean( Ye )
- msg += (" Standard error of the mean of the differences.: %."+str(_p)+"e\n")%numpy.std( Ye )
- msg += ("\n %s\n"%("-"*75,))
- print(msg)
- #
- logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint : %i/%i/%i"%(self._name, HO["Direct"].nbcalls(0),HO["Tangent"].nbcalls(0),HO["Adjoint"].nbcalls(0)))
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
+ msgs += ("\n Characteristics of the whole set of outputs Y:\n")
+ msgs += (" Number of evaluations.........................: %i\n")%len( Ys )
+ msgs += (" Minimum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.min( Yy )
+ msgs += (" Maximum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.max( Yy )
+ msgs += (" Mean of vector of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.mean( Yy, dtype=mfp )
+ msgs += (" Standard error of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.std( Yy, dtype=mfp )
+ Ym = numpy.mean( numpy.array( Ys ), axis=0, dtype=mfp )
+ msgs += ("\n Characteristics of the vector Ym, mean of the outputs Y:\n")
+ msgs += (" Size of the mean of the outputs...............: %i\n")%Ym.size
+ msgs += (" Minimum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.min( Ym )
+ msgs += (" Maximum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.max( Ym )
+ msgs += (" Mean of the mean of the outputs...............: %."+str(_p)+"e\n")%numpy.mean( Ym, dtype=mfp )
+ msgs += (" Standard error of the mean of the outputs.....: %."+str(_p)+"e\n")%numpy.std( Ym, dtype=mfp )
+ Ye = numpy.mean( numpy.array( Ys ) - Ym, axis=0, dtype=mfp )
+ msgs += "\n Characteristics of the mean of the differences between the outputs Y and their mean Ym:\n"
+ msgs += (" Size of the mean of the differences...........: %i\n")%Ym.size
+ msgs += (" Minimum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.min( Ye )
+ msgs += (" Maximum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.max( Ye )
+ msgs += (" Mean of the mean of the differences...........: %."+str(_p)+"e\n")%numpy.mean( Ye, dtype=mfp )
+ msgs += (" Standard error of the mean of the differences.: %."+str(_p)+"e\n")%numpy.std( Ye, dtype=mfp )
+ msgs += ("\n %s\n"%("-"*75,))
+ print(msgs)
#
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC \n')
