def updateVisualizationOfNormalizedWeights():
     if self._relative_iteration_weights_box.isValid():
         weights = MultipleDataAssimilation.parseWeights(relative_iteration_weights_model.getValue())
         normalized_weights = MultipleDataAssimilation.normalizeWeights(weights)
         normalized_weights_model.setValue(", ".join("%.2f" % x for x in normalized_weights))
     else:
         normalized_weights_model.setValue("The weights are invalid!")
Example #2
0
class SimulationModeModel(ErtConnector, ChoiceModelMixin):
    __modes = [
        EnsembleExperiment(),
        EnsembleSmoother(),
        IteratedEnsembleSmoother(),
        MultipleDataAssimilation()
    ]

    def __init__(self):
        self.__value = SimulationModeModel.__modes[0]
        super(SimulationModeModel, self).__init__()

    def getChoices(self):
        return SimulationModeModel.__modes

    def getCurrentChoice(self):
        return self.__value

    def setCurrentChoice(self, value):
        self.__value = value
        self.observable().notify(self.CURRENT_CHOICE_CHANGED_EVENT)
    def test_weights(self):
        
        weights = mda.parseWeights("2, 2, 2, 2")
        print(weights)
        self.assertAlmostEqualList([2, 2, 2, 2], weights)

        weights = mda.parseWeights("1, 2, 3, ")
        self.assertAlmostEqualList([1, 2, 3], weights)

        weights = mda.parseWeights("1, 0, 1")
        self.assertAlmostEqualList([1, 1], weights)

        weights = mda.parseWeights("1.414213562373095, 1.414213562373095")
        self.assertAlmostEqualList([1.414213562373095, 1.414213562373095], weights)
  
        with self.assertRaises(ValueError):
            mda.parseWeights("2, error, 2, 2")
    def test_normalized_weights(self):
        
        weights = mda.normalizeWeights([1])
        self.assertAlmostEqualList([1.0], weights)
         
        weights = mda.normalizeWeights([1, 1])
        self.assertAlmostEqualList([1.414214, 1.414214], weights)

        weights = mda.normalizeWeights([1, 0, 1])
        self.assertAlmostEqualList([1.414214, 1.414214], weights)
         
        weights = mda.normalizeWeights([1, 1, 1])
        self.assertAlmostEqualList([1.732051, 1.732051, 1.732051], weights)
         
        weights = mda.normalizeWeights([8, 4, 2, 1])
        self.assertAlmostEqualList([9.219544457292887, 4.6097722286464435, 2.3048861143232218, 1.1524430571616109], weights)
         
        weights = mda.normalizeWeights([9.219544457292887, 4.6097722286464435, 2.3048861143232218, 1.1524430571616109])
        self.assertAlmostEqualList([9.219544457292887, 4.6097722286464435, 2.3048861143232218, 1.1524430571616109], weights)