--- /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
+
+# ==============================================================================
+class ElementaryAlgorithm(BasicObjects.Algorithm):
+ def __init__(self):
+ BasicObjects.Algorithm.__init__(self, "FUNCTIONREPETITIONTEST")
+ 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
+ #
+ # ----------
+ 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 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")
+ #
+ # ----------
+ 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 parameter 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......: %.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)
+ #
+ return 0
+
+# ==============================================================================
+if __name__ == "__main__":
+ print '\n AUTODIAGNOSTIC \n'
