+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2013 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 logging
-from daCore import BasicObjects, PlatformInfo
-m = PlatformInfo.SystemUsage()
-
-import numpy, copy
-
-# ==============================================================================
-class ElementaryAlgorithm(BasicObjects.Algorithm):
- def __init__(self):
- BasicObjects.Algorithm.__init__(self, "FUNCTIONREPETITIONTEST")
- self.defineRequiredParameter(
- name = "NumberOfPrintedDigits",
- default = 8,
- typecast = int,
- message = "Nombre de chiffres affichés pour les impressions de réels",
- minval = 0,
- )
- self.defineRequiredParameter(
- name = "NumberOfRepetition",
- default = 2,
- 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 = True,
- typecast = bool,
- message = "Activation du mode debug lors de l'exécution",
- )
-
- 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)
- #
- Hm = HO["Direct"].appliedTo
- #
- Xn = numpy.asmatrix(numpy.ravel( Xb )).T
- #
- # ----------
- _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)
- #
- 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)
- #
- 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")
- #
- # ----------
- Ys = []
- for i in range(self._parameters["NumberOfRepetition"]):
- print(" %s\n"%("-"*75,))
- 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 )
- #
- print("\n===> End of direct operator evaluation\n")
- #
- msg = "===> Information after launching:\n"
- msg += " ----------------------------\n"
- msg += " 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)
- #
- Ys.append( copy.copy( numpy.ravel(Y) ) )
- #
- print(" %s\n"%("-"*75,))
- if self._parameters["SetDebug"]:
- print("===> End evaluation, deactivating debug if necessary\n")
- logging.getLogger().setLevel(CUR_LEVEL)
- #
- msg = (" %s\n"%("-"*75,))
- msg += ("\n===> Statistical analysis of the outputs obtained throught repeated evaluations\n")
- 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 Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
- #
- return 0
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
from daCore import BasicObjects, PlatformInfo
m = PlatformInfo.SystemUsage()
-import numpy
+import numpy, copy
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
- BasicObjects.Algorithm.__init__(self, "FUNCTIONTEST")
+ BasicObjects.Algorithm.__init__(self, "REPEATEDFUNCTIONTEST")
+ 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 = "",
Xn = numpy.asmatrix(numpy.ravel( Xb )).T
#
# ----------
+ _p = self._parameters["NumberOfPrintedDigits"]
if len(self._parameters["ResultTitle"]) > 0:
msg = " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n"
msg += " " + self._parameters["ResultTitle"] + "\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......: %.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 )
+ 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)
#
if self._parameters["SetDebug"]:
print("===> Beginning of evaluation, activating debug\n")
else:
print("===> Beginning of evaluation, without activating debug\n")
- print(" %s\n"%("-"*75,))
#
- print("===> Launching direct operator evaluation\n")
- Y = Hm( Xn )
- print("\n===> End of direct operator evaluation\n")
- #
- msg = "===> Information after launching:\n"
- msg += " ----------------------------\n"
- msg += " Characteristics of output vector 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)
+ # ----------
+ Ys = []
+ for i in range(self._parameters["NumberOfRepetition"]):
+ 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 )
+ #
+ 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)
+ #
+ Ys.append( copy.copy( numpy.ravel(Y) ) )
#
print(" %s\n"%("-"*75,))
if self._parameters["SetDebug"]:
print("===> End evaluation, deactivating debug if necessary\n")
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")
+ 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 Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
logging.debug("%s Terminé"%self._name)
#
