# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2019 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
"Verification d'un exemple de la documentation"
+import sys
+import unittest
+
# ==============================================================================
#
# Construction artificielle d'un exemple de donnees utilisateur
def simulation(x):
"Fonction de simulation H pour effectuer Y=H(X)"
import numpy
- __x = numpy.matrix(numpy.ravel(numpy.matrix(x))).T
- __H = numpy.matrix("1 0 0;0 2 0;0 0 3; 1 2 3")
- return __H * __x
+ __x = numpy.ravel(x)
+ __H = numpy.array([[1,0,0],[0,2,0],[0,0,3],[1,2,3]])
+ return __H @ __x
#
def multisimulation( xserie ):
yserie = []
observations = simulation((2, 3, 4))
# ==============================================================================
-def test1():
- "Test"
- import numpy
- from adao import adaoBuilder
- #
- # Mise en forme des entrees
- # -------------------------
- Xb = (alpha, beta, gamma)
- Bounds = (
- (alphamin, alphamax),
- (betamin, betamax ),
- (gammamin, gammamax))
- #
- # TUI ADAO
- # --------
- case = adaoBuilder.New()
- case.set(
- 'AlgorithmParameters',
- Algorithm = '3DVAR',
- Parameters = {
- "Bounds":Bounds,
- "MaximumNumberOfSteps":100,
- "StoreSupplementaryCalculations":[
- "CostFunctionJ",
- "CurrentState",
- "SimulatedObservationAtOptimum",
- ],
- }
- )
- case.set( 'Background', Vector = numpy.array(Xb), Stored = True )
- case.set( 'Observation', Vector = numpy.array(observations) )
- case.set( 'BackgroundError', ScalarSparseMatrix = 1.0e10 )
- case.set( 'ObservationError', ScalarSparseMatrix = 1.0 )
- case.set(
- 'ObservationOperator',
- OneFunction = multisimulation,
- Parameters = {"DifferentialIncrement":0.0001},
- InputFunctionAsMulti = True,
- )
- case.set( 'Observer', Variable="CurrentState", Template="ValuePrinter" )
- case.execute()
- #
- # Exploitation independante
- # -------------------------
- Xbackground = case.get("Background")
- Xoptimum = case.get("Analysis")[-1]
- FX_at_optimum = case.get("SimulatedObservationAtOptimum")[-1]
- J_values = case.get("CostFunctionJ")[:]
- print("")
- print("Number of internal iterations...: %i"%len(J_values))
- print("Initial state...................: %s"%(numpy.ravel(Xbackground),))
- print("Optimal state...................: %s"%(numpy.ravel(Xoptimum),))
- print("Simulation at optimal state.....: %s"%(numpy.ravel(FX_at_optimum),))
- print("")
- #
- return case.get("Analysis")[-1]
+class Test_Adao(unittest.TestCase):
+ def test1(self):
+ "Test"
+ print("""Exemple de la doc :
+
+ Exploitation independante des resultats d'un cas de calcul
+ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ """)
+ #---------------------------------------------------------------------------
+ import numpy
+ from adao import adaoBuilder
+ #
+ # Mise en forme des entrees
+ # -------------------------
+ Xb = (alpha, beta, gamma)
+ Bounds = (
+ (alphamin, alphamax),
+ (betamin, betamax ),
+ (gammamin, gammamax))
+ #
+ # TUI ADAO
+ # --------
+ case = adaoBuilder.New()
+ case.set(
+ 'AlgorithmParameters',
+ Algorithm = '3DVAR',
+ Parameters = {
+ "Bounds":Bounds,
+ "MaximumNumberOfIterations":100,
+ "StoreSupplementaryCalculations":[
+ "CostFunctionJ",
+ "CurrentState",
+ "SimulatedObservationAtOptimum",
+ ],
+ }
+ )
+ case.set( 'Background', Vector = numpy.array(Xb), Stored = True )
+ case.set( 'Observation', Vector = numpy.array(observations) )
+ case.set( 'BackgroundError', ScalarSparseMatrix = 1.0e10 )
+ case.set( 'ObservationError', ScalarSparseMatrix = 1.0 )
+ case.set(
+ 'ObservationOperator',
+ OneFunction = multisimulation,
+ Parameters = {"DifferentialIncrement":0.0001},
+ InputFunctionAsMulti = True,
+ )
+ case.set( 'Observer', Variable="CurrentState", Template="ValuePrinter" )
+ case.execute()
+ #
+ # Exploitation independante
+ # -------------------------
+ Xbackground = case.get("Background")
+ Xoptimum = case.get("Analysis")[-1]
+ FX_at_optimum = case.get("SimulatedObservationAtOptimum")[-1]
+ J_values = case.get("CostFunctionJ")[:]
+ print("")
+ print("Number of internal iterations...: %i"%len(J_values))
+ print("Initial state...................: %s"%(numpy.ravel(Xbackground),))
+ print("Optimal state...................: %s"%(numpy.ravel(Xoptimum),))
+ print("Simulation at optimal state.....: %s"%(numpy.ravel(FX_at_optimum),))
+ print("")
+ #
+ #---------------------------------------------------------------------------
+ xa = case.get("Analysis")[-1]
+ ecart = assertAlmostEqualArrays(xa, [ 2., 3., 4.])
+ #
+ print(" L'écart absolu maximal obtenu lors du test est de %.2e."%ecart)
+ print(" Les résultats obtenus sont corrects.")
+ print("")
# ==============================================================================
def assertAlmostEqualArrays(first, second, places=7, msg=None, delta=None):
if msg is not None:
print(msg)
if delta is not None:
- if ( (numpy.asarray(first) - numpy.asarray(second)) > float(delta) ).any():
+ if ( numpy.abs(numpy.asarray(first) - numpy.asarray(second)) > float(delta) ).any():
raise AssertionError("%s != %s within %s places"%(first,second,delta))
else:
- if ( (numpy.asarray(first) - numpy.asarray(second)) > 10**(-int(places)) ).any():
+ if ( numpy.abs(numpy.asarray(first) - numpy.asarray(second)) > 10**(-int(places)) ).any():
raise AssertionError("%s != %s within %i places"%(first,second,places))
return max(abs(numpy.asarray(first) - numpy.asarray(second)))
# ==============================================================================
if __name__ == "__main__":
print('\nAUTODIAGNOSTIC\n')
- print("""Exemple de la doc :
-
- Exploitation independante des resultats d'un cas de calcul
- ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
- """)
- xa = test1()
- ecart = assertAlmostEqualArrays(xa, [ 2., 3., 4.])
- #
- print(" L'écart absolu maximal obtenu lors du test est de %.2e."%ecart)
- print(" Les résultats obtenus sont corrects.")
- print("")
+ sys.stderr = sys.stdout
+ unittest.main(verbosity=2)
