1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2024 EDF R&D
5 # This library is free software; you can redistribute it and/or
6 # modify it under the terms of the GNU Lesser General Public
7 # License as published by the Free Software Foundation; either
8 # version 2.1 of the License.
10 # This library is distributed in the hope that it will be useful,
11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
13 # Lesser General Public License for more details.
15 # You should have received a copy of the GNU Lesser General Public
16 # License along with this library; if not, write to the Free Software
17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
23 import numpy, copy, logging
24 from daCore import BasicObjects, PlatformInfo
25 mpr = PlatformInfo.PlatformInfo().MachinePrecision()
26 mfp = PlatformInfo.PlatformInfo().MaximumPrecision()
28 # ==============================================================================
29 class ElementaryAlgorithm(BasicObjects.Algorithm):
31 BasicObjects.Algorithm.__init__(self, "PARALLELFUNCTIONTEST")
32 self.defineRequiredParameter(
33 name = "ShowElementarySummary",
36 message = "Calcule et affiche un résumé à chaque évaluation élémentaire",
38 self.defineRequiredParameter(
39 name = "NumberOfPrintedDigits",
42 message = "Nombre de chiffres affichés pour les impressions de réels",
45 self.defineRequiredParameter(
46 name = "NumberOfRepetition",
49 message = "Nombre de fois où l'exécution de la fonction est répétée",
52 self.defineRequiredParameter(
56 message = "Titre du tableau et de la figure",
58 self.defineRequiredParameter(
62 message = "Activation du mode debug lors de l'exécution",
64 self.defineRequiredParameter(
65 name = "StoreSupplementaryCalculations",
68 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
71 "SimulatedObservationAtCurrentState",
74 self.requireInputArguments(
75 mandatory= ("Xb", "HO"),
87 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
88 self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
90 Hm = HO["Direct"].appliedTo
95 __s = self._parameters["ShowElementarySummary"]
96 __p = self._parameters["NumberOfPrintedDigits"]
97 __r = self._parameters["NumberOfRepetition"]
100 __flech = 3 * "=" + "> "
102 if len(self._parameters["ResultTitle"]) > 0:
103 __rt = str(self._parameters["ResultTitle"])
104 msgs += (__marge + "====" + "=" * len(__rt) + "====\n")
105 msgs += (__marge + " " + __rt + "\n")
106 msgs += (__marge + "====" + "=" * len(__rt) + "====\n")
108 msgs += (__marge + "%s\n"%self._name)
109 msgs += (__marge + "%s\n"%("=" * len(self._name),))
112 msgs += (__marge + "This test allows to analyze the (repetition of the) launch of some\n")
113 msgs += (__marge + "given simulation operator F, applied to one single vector argument x,\n")
114 msgs += (__marge + "in a parallel way.\n")
115 msgs += (__marge + "The output shows simple statistics related to its successful execution,\n")
116 msgs += (__marge + "or related to the similarities of repetition of its execution.\n")
118 msgs += (__flech + "Information before launching:\n")
119 msgs += (__marge + "-----------------------------\n")
121 msgs += (__marge + "Characteristics of input vector X, internally converted:\n")
122 msgs += (__marge + " Type...............: %s\n")%type( X0 )
123 msgs += (__marge + " Length of vector...: %i\n")%max(numpy.ravel( X0 ).shape)
124 msgs += (__marge + " Minimum value......: %." + str(__p) + "e\n")%numpy.min( X0 )
125 msgs += (__marge + " Maximum value......: %." + str(__p) + "e\n")%numpy.max( X0 )
126 msgs += (__marge + " Mean of vector.....: %." + str(__p) + "e\n")%numpy.mean( X0, dtype=mfp )
127 msgs += (__marge + " Standard error.....: %." + str(__p) + "e\n")%numpy.std( X0, dtype=mfp )
128 msgs += (__marge + " L2 norm of vector..: %." + str(__p) + "e\n")%numpy.linalg.norm( X0 )
130 msgs += (__marge + "%s\n\n"%("-" * 75,))
132 if self._parameters["SetDebug"]:
133 CUR_LEVEL = logging.getLogger().getEffectiveLevel()
134 logging.getLogger().setLevel(logging.DEBUG)
136 msgs += (__flech + "Beginning of repeated evaluation, activating debug\n")
138 msgs += (__flech + "Beginning of evaluation, activating debug\n")
141 msgs += (__flech + "Beginning of repeated evaluation, without activating debug\n")
143 msgs += (__flech + "Beginning of evaluation, without activating debug\n")
145 msgs += (__marge + "%s\n"%("-" * 75,))
149 HO["Direct"].disableAvoidingRedundancy()
153 msgs = (__marge + "Appending the input vector to the agument set to be evaluated in parallel\n") # 2-1
155 if self._toStore("CurrentState"):
156 self.StoredVariables["CurrentState"].store( X0 )
161 msgs += (__marge + " Appending step number %i on a total of %i\n"%(i + 1, __r))
164 msgs += (__marge + "%s\n\n"%("-" * 75,))
165 msgs += (__flech + "Launching operator parallel evaluation for %i states\n"%__r)
168 Ys = Hm( Xs, argsAsSerie = True )
171 msgs += (__flech + "End of operator parallel evaluation for %i states\n"%__r)
173 msgs += (__marge + "%s\n"%("-" * 75,))
177 HO["Direct"].enableAvoidingRedundancy()
181 if self._parameters["SetDebug"]:
183 msgs += (__flech + "End of repeated evaluation, deactivating debug if necessary\n")
185 msgs += (__flech + "End of evaluation, deactivating debug if necessary\n")
186 logging.getLogger().setLevel(CUR_LEVEL)
189 msgs += (__flech + "End of repeated evaluation, without deactivating debug\n")
191 msgs += (__flech + "End of evaluation, without deactivating debug\n")
193 if __s or self._toStore("SimulatedObservationAtCurrentState"):
194 for i in range(self._parameters["NumberOfRepetition"]):
197 msgs += (__marge + "%s\n\n"%("-" * 75,))
198 if self._parameters["NumberOfRepetition"] > 1:
199 msgs += (__flech + "Repetition step number %i on a total of %i\n"%(i + 1, self._parameters["NumberOfRepetition"])) # noqa: E501
204 msgs += (__flech + "Information after evaluation:\n")
206 msgs += (__marge + "Characteristics of simulated output vector Y=F(X), to compare to others:\n")
207 msgs += (__marge + " Type...............: %s\n")%type( Yn )
208 msgs += (__marge + " Length of vector...: %i\n")%max(numpy.ravel( Yn ).shape)
209 msgs += (__marge + " Minimum value......: %." + str(__p) + "e\n")%numpy.min( Yn )
210 msgs += (__marge + " Maximum value......: %." + str(__p) + "e\n")%numpy.max( Yn )
211 msgs += (__marge + " Mean of vector.....: %." + str(__p) + "e\n")%numpy.mean( Yn, dtype=mfp ) # noqa: E501
212 msgs += (__marge + " Standard error.....: %." + str(__p) + "e\n")%numpy.std( Yn, dtype=mfp ) # noqa: E501
213 msgs += (__marge + " L2 norm of vector..: %." + str(__p) + "e\n")%numpy.linalg.norm( Yn )
215 if self._toStore("SimulatedObservationAtCurrentState"):
216 self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(Yn) )
219 msgs += (__marge + "%s\n"%("-" * 75,))
223 msgs += (__flech + "Launching statistical summary calculation for %i states\n"%__r)
225 msgs += (__marge + "%s\n"%("-" * 75,))
227 msgs += (__flech + "Statistical analysis of the outputs obtained through parallel repeated evaluations\n") # noqa: E501
229 msgs += (__marge + "(Remark: numbers that are (about) under %.0e represent 0 to machine precision)\n"%mpr) # noqa: E501
231 Yy = numpy.array( Ys )
232 msgs += (__marge + "Number of evaluations...........................: %i\n")%len( Ys )
234 msgs += (__marge + "Characteristics of the whole set of outputs Y:\n")
235 msgs += (__marge + " Size of each of the outputs...................: %i\n")%Ys[0].size
236 msgs += (__marge + " Minimum value of the whole set of outputs.....: %." + str(__p) + "e\n")%numpy.min( Yy ) # noqa: E501
237 msgs += (__marge + " Maximum value of the whole set of outputs.....: %." + str(__p) + "e\n")%numpy.max( Yy ) # noqa: E501
238 msgs += (__marge + " Mean of vector of the whole set of outputs....: %." + str(__p) + "e\n")%numpy.mean( Yy, dtype=mfp ) # noqa: E501
239 msgs += (__marge + " Standard error of the whole set of outputs....: %." + str(__p) + "e\n")%numpy.std( Yy, dtype=mfp ) # noqa: E501
241 Ym = numpy.mean( numpy.array( Ys ), axis=0, dtype=mfp )
242 msgs += (__marge + "Characteristics of the vector Ym, mean of the outputs Y:\n")
243 msgs += (__marge + " Size of the mean of the outputs...............: %i\n")%Ym.size
244 msgs += (__marge + " Minimum value of the mean of the outputs......: %." + str(__p) + "e\n")%numpy.min( Ym ) # noqa: E501
245 msgs += (__marge + " Maximum value of the mean of the outputs......: %." + str(__p) + "e\n")%numpy.max( Ym ) # noqa: E501
246 msgs += (__marge + " Mean of the mean of the outputs...............: %." + str(__p) + "e\n")%numpy.mean( Ym, dtype=mfp ) # noqa: E501
247 msgs += (__marge + " Standard error of the mean of the outputs.....: %." + str(__p) + "e\n")%numpy.std( Ym, dtype=mfp ) # noqa: E501
249 Ye = numpy.mean( numpy.array( Ys ) - Ym, axis=0, dtype=mfp )
250 msgs += (__marge + "Characteristics of the mean of the differences between the outputs Y and their mean Ym:\n") # noqa: E501
251 msgs += (__marge + " Size of the mean of the differences...........: %i\n")%Ye.size
252 msgs += (__marge + " Minimum value of the mean of the differences..: %." + str(__p) + "e\n")%numpy.min( Ye ) # noqa: E501
253 msgs += (__marge + " Maximum value of the mean of the differences..: %." + str(__p) + "e\n")%numpy.max( Ye ) # noqa: E501
254 msgs += (__marge + " Mean of the mean of the differences...........: %." + str(__p) + "e\n")%numpy.mean( Ye, dtype=mfp ) # noqa: E501
255 msgs += (__marge + " Standard error of the mean of the differences.: %." + str(__p) + "e\n")%numpy.std( Ye, dtype=mfp ) # noqa: E501
257 msgs += (__marge + "%s\n"%("-" * 75,))
260 msgs += (__marge + "End of the \"%s\" verification\n\n"%self._name)
261 msgs += (__marge + "%s\n"%("-" * 75,))
264 self._post_run(HO, EM)
267 # ==============================================================================
268 if __name__ == "__main__":
269 print("\n AUTODIAGNOSTIC\n")