1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2019 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
22 "Verification d'un exemple de la documentation"
24 # ==============================================================================
27 from numpy import array, matrix
28 from adao import adaoBuilder
29 case = adaoBuilder.New()
30 case.set( 'AlgorithmParameters', Algorithm='3DVAR' )
31 case.set( 'Background', Vector=[0, 1, 2] )
32 case.set( 'BackgroundError', ScalarSparseMatrix=1.0 )
33 case.set( 'Observation', Vector=array([0.5, 1.5, 2.5]) )
34 case.set( 'ObservationError', DiagonalSparseMatrix='1 1 1' )
35 case.set( 'ObservationOperator', Matrix='1 0 0;0 2 0;0 0 3' )
36 case.set( 'Observer', Variable="Analysis", Template="ValuePrinter" )
39 return case.get("Analysis")[-1]
43 from numpy import array, matrix
44 from adao import adaoBuilder
45 case = adaoBuilder.New()
46 case.set( 'AlgorithmParameters', Algorithm='3DVAR' )
47 case.set( 'Background', Vector=[0, 1, 2] )
48 case.set( 'BackgroundError', ScalarSparseMatrix=1.0 )
49 case.set( 'Observation', Vector=array([0.5, 1.5, 2.5]) )
50 case.set( 'ObservationError', DiagonalSparseMatrix='1 1 1' )
53 __x = numpy.matrix(numpy.ravel(numpy.matrix(x))).T
54 __H = numpy.matrix("1 0 0;0 2 0;0 0 3")
57 case.set( 'ObservationOperator',
58 OneFunction = simulation,
59 Parameters = {"DifferentialIncrement":0.01},
61 case.set( 'Observer', Variable="Analysis", Template="ValuePrinter" )
64 return case.get("Analysis")[-1]
66 # ==============================================================================
67 def assertAlmostEqualArrays(first, second, places=7, msg=None, delta=None):
68 "Compare two vectors, like unittest.assertAlmostEqual"
73 if ( (numpy.asarray(first) - numpy.asarray(second)) > float(delta) ).any():
74 raise AssertionError("%s != %s within %s places"%(first,second,delta))
76 if ( (numpy.asarray(first) - numpy.asarray(second)) > 10**(-int(places)) ).any():
77 raise AssertionError("%s != %s within %i places"%(first,second,places))
78 return max(abs(numpy.asarray(first) - numpy.asarray(second)))
80 # ==============================================================================
81 if __name__ == "__main__":
82 print('\nAUTODIAGNOSTIC\n')
83 print("""Exemple de la doc :
85 Creation detaillee d'un cas de calcul TUI ADAO
86 ++++++++++++++++++++++++++++++++++++++++++++++
87 Les deux resultats sont testes pour etre identiques.
91 ecart = assertAlmostEqualArrays(xa1, xa2, places = 15)
93 print(" L'écart absolu maximal obtenu lors du test est de %.2e."%ecart)
94 print(" Les résultats obtenus sont corrects.")