-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2013 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
+import numpy, copy
+mpr = PlatformInfo.PlatformInfo().MachinePrecision()
+mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
+if sys.version_info.major > 2:
+ unicode = str
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
BasicObjects.Algorithm.__init__(self, "FUNCTIONTEST")
+ 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 = ["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._pre_run(Parameters)
#
- # Paramètres de pilotage
- # ----------------------
- self.setParameters(Parameters)
- #
- # Opérateur
- # ---------
Hm = HO["Direct"].appliedTo
#
- # Calcul du point nominal
- # -----------------------
- Xn = numpy.asmatrix(numpy.ravel( Xb )).T
+ Xn = copy.copy( Xb )
#
- # Test
- # ----
+ # ----------
+ __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)
+ #
+ 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)
#
- msg = "===> Information before launching:\n"
- msg += " -----------------------------\n"
- msg += " Characteristics of input parameter X, internally converted:\n"
- msg += " Type...............: %s\n"%type( Xn )
- msg += " Lenght of vector...: %i\n"%max(numpy.matrix( Xn ).shape)
- msg += " Minimum value......: %.5e\n"%numpy.min( Xn )
- msg += " Maximum value......: %.5e\n"%numpy.max( Xn )
- msg += " Mean of vector.....: %.5e\n"%numpy.mean( Xn )
- msg += " Standard error.....: %.5e\n"%numpy.std( Xn )
- msg += " L2 norm of vector..: %.5e\n"%numpy.linalg.norm( Xn )
- print(msg)
+ if self._parameters["SetDebug"]:
+ CUR_LEVEL = logging.getLogger().getEffectiveLevel()
+ logging.getLogger().setLevel(logging.DEBUG)
+ print("===> Beginning of evaluation, activating debug\n")
+ else:
+ print("===> Beginning of evaluation, without activating debug\n")
#
- CUR_LEVEL = logging.getLogger().getEffectiveLevel()
- logging.getLogger().setLevel(logging.DEBUG)
- print( "===> Launching direct operator evaluation, activating debug\n")
- Y = Hm( Xn )
- print("\n===> End of direct operator evaluation, deactivating debug\n")
- logging.getLogger().setLevel(CUR_LEVEL)
+ # ----------
+ 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")
+ #
+ Yn = Hm( Xn )
+ #
+ print("\n===> End of direct operator evaluation\n")
+ #
+ 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(
+ Yn
+ ) ) )
+ # ----------
+ HO["Direct"].enableAvoidingRedundancy()
+ # ----------
#
- msg = "===> Information after launching:\n"
- msg += " ----------------------------\n"
- msg += " Characteristics of output parameter Y, to compare to observation:\n"
- msg += " Type...............: %s\n"%type( Y )
- msg += " Lenght of vector...: %i\n"%max(numpy.matrix( Y ).shape)
- msg += " Minimum value......: %.5e\n"%numpy.min( Y )
- msg += " Maximum value......: %.5e\n"%numpy.max( Y )
- msg += " Mean of vector.....: %.5e\n"%numpy.mean( Y )
- msg += " Standard error.....: %.5e\n"%numpy.std( Y )
- msg += " L2 norm of vector..: %.5e\n"%numpy.linalg.norm( Y )
- print(msg)
+ print(" %s\n"%("-"*75,))
+ if self._parameters["SetDebug"]:
+ print("===> End evaluation, deactivating debug if necessary\n")
+ logging.getLogger().setLevel(CUR_LEVEL)
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
+ if self._parameters["NumberOfRepetition"] > 1:
+ 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 )
+ 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')
