# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2021 EDF R&D
+# 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
#
# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
-import sys, logging
+import numpy, copy, logging
from daCore import BasicObjects, PlatformInfo
-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, "PARALLELFUNCTIONTEST")
+ self.defineRequiredParameter(
+ name = "ShowElementarySummary",
+ default = True,
+ typecast = bool,
+ message = "Calcule et affiche un résumé à chaque évaluation élémentaire",
+ )
self.defineRequiredParameter(
name = "NumberOfPrintedDigits",
default = 5,
#
Hm = HO["Direct"].appliedTo
#
- Xn = copy.copy( Xb )
+ X0 = copy.copy( Xb )
#
# ----------
- __marge = 5*u" "
- _p = self._parameters["NumberOfPrintedDigits"]
+ __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 = 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)
- #
- print(" %s\n"%("-"*75,))
+ __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 + "in a parallel way.\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 + "%s\n\n"%("-"*75,))
+ #
if self._parameters["SetDebug"]:
CUR_LEVEL = logging.getLogger().getEffectiveLevel()
logging.getLogger().setLevel(logging.DEBUG)
- print("===> Beginning of evaluation, activating debug\n")
+ if __r > 1:
+ msgs += (__flech + "Beginning of repeated evaluation, activating debug\n")
+ else:
+ msgs += (__flech + "Beginning of evaluation, activating debug\n")
else:
- print("===> Beginning of evaluation, without activating debug\n")
- #
- Xs = []
- Ys = []
- for i in range(self._parameters["NumberOfRepetition"]):
- if self._toStore("CurrentState"):
- self.StoredVariables["CurrentState"].store( numpy.ravel(Xn) )
- Xs.append( Xn )
+ if __r > 1:
+ msgs += (__flech + "Beginning of repeated evaluation, without activating debug\n")
+ else:
+ msgs += (__flech + "Beginning of evaluation, without activating debug\n")
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ print(msgs) # 1
#
# ----------
HO["Direct"].disableAvoidingRedundancy()
# ----------
+ Ys = []
+ Xs = []
+ msgs = (__marge + "Appending the input vector to the agument set to be evaluated in parallel\n") # 2-1
+ for i in range(__r):
+ if self._toStore("CurrentState"):
+ self.StoredVariables["CurrentState"].store( X0 )
+ Xs.append( X0 )
+ if __s:
+ # msgs += ("\n")
+ if __r > 1:
+ msgs += (__marge + " Appending step number %i on a total of %i\n"%(i+1,__r))
+ #
+ msgs += ("\n")
+ msgs += (__marge + "%s\n\n"%("-"*75,))
+ msgs += (__flech + "Launching operator parallel evaluation for %i states\n"%__r)
+ print(msgs) # 2-1
+ #
Ys = Hm( Xs, argsAsSerie = True )
+ #
+ msgs = ("\n") # 2-2
+ msgs += (__flech + "End of operator parallel evaluation for %i states\n"%__r)
+ msgs += ("\n")
+ msgs += (__marge + "%s\n"%("-"*75,))
+ print(msgs) # 2-2
+ #
# ----------
HO["Direct"].enableAvoidingRedundancy()
# ----------
#
- print()
+ msgs = ("") # 3
if self._parameters["SetDebug"]:
- print("===> End of evaluation, deactivating debug\n")
+ 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:
- print("===> End of evaluation, without deactivating debug\n")
- #
- 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"]))
- #
- Yn = Ys[i]
- 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 self._toStore("SimulatedObservationAtCurrentState"):
- self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(Yn) )
- #
- if self._parameters["NumberOfRepetition"] > 1:
- msgs = (" %s\n"%("-"*75,))
- msgs += ("\n===> Statistical analysis of the outputs obtained through parallel repeated evaluations\n")
- msgs += ("\n (Remark: numbers that are (about) under %.0e represent 0 to machine precision)\n"%mpr)
+ if __r > 1:
+ msgs += (__flech + "End of repeated evaluation, without deactivating debug\n")
+ else:
+ msgs += (__flech + "End of evaluation, without deactivating debug\n")
+ #
+ if __s or self._toStore("SimulatedObservationAtCurrentState"):
+ for i in range(self._parameters["NumberOfRepetition"]):
+ if __s:
+ msgs += ("\n")
+ msgs += (__marge + "%s\n\n"%("-"*75,))
+ if self._parameters["NumberOfRepetition"] > 1:
+ msgs += (__flech + "Repetition step number %i on a total of %i\n"%(i+1,self._parameters["NumberOfRepetition"]))
+ #
+ Yn = Ys[i]
+ if __s:
+ 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 )
+ #
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(Yn) )
+ #
+ 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 parallel 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 += ("\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 )
+ 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 += ("\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 )
+ 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 += "\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)
+ 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 += (__marge + "End of the \"%s\" verification\n\n"%self._name)
+ msgs += (__marge + "%s\n"%("-"*75,))
+ print(msgs) # 3
#
self._post_run(HO)
return 0
