cc28a483cf95697bc4ab82030771da69c3cda5d0,features/eolearn/tests/test_doubly_logistic_approximation.py,TestDoublyLogisticApproximation,setUpClass,#Any#,11

Before Change

    @staticmethod
    def setUpClass(cls):
        data = np.array([0, 0.05, 0.2, 0.5, 0.81, 1, 0.95, 0.51, 0.03, 0.001, 0.001])
        for i in range(3):
            data = np.expand_dims(data, axis=1)

        cls.eopatch.add_feature(FeatureType.DATA, "TEST", data)

After Change

                 datetime(2017, 12, 17), datetime(2017, 12, 19), datetime(2017, 12, 22), datetime(2017, 12, 24),
                 datetime(2017, 12, 29)]

        values = [0.059783712, 0.06304545, np.NaN, 0.07635699, np.NaN, 0.07554149, np.NaN, np.NaN, 0.095581025,
                  0.09109493, np.NaN, 0.114999376, np.NaN, np.NaN, np.NaN, 0.12209105, 0.089099824, 0.12314385,
                  np.NaN, np.NaN, np.NaN, 0.13705324, np.NaN, np.NaN, np.NaN, 0.26388252, 0.19771394, 0.27082473,
                  np.NaN, 0.3250511, 0.24847558, 0.35576594, 0.3713841, 0.30338106, 0.4179747, 0.326186, np.NaN,
                  np.NaN, np.NaN, 0.69031024, 0.58324015, 0.68232125, 0.59130245, 0.655362, 0.7490687, np.NaN,
                  0.45670646, 0.39466715, 0.43877497, 0.40336365, 0.2609013, np.NaN, 0.13757367, 0.12950855,
                  0.13163383, 0.13427204, np.NaN, 0.117703386, 0.12337628, 0.013523403, 0.076479584, 0.058155004,
                  np.NaN, 0.04863815, 0.060789138, 0.063743524, np.NaN, 0.044066854, 0.11930587]

        cls.eopatch = EOPatch(timestamp=list(dates))
        cls.eopatch.timestamp = list(dates)
        cls.eopatch.data["TEST"] = np.array(values).reshape(-1, 1, 1, 1)
        cls.eopatch.mask["VALID_DATA"] = ~np.isnan(cls.eopatch.data["TEST"])
        cls.eopatch = DoublyLogisticApproximationTask("TEST", "TEST_OUT", mask_data=True).execute(cls.eopatch)

    def test_parameters(self):
        c1, c2, a1, a2, a3, a4, a5 = self.eopatch.data_timeless["TEST_OUT"].squeeze()

Instances