def test_to_dict_constant(self):
        model = TruncatedGaussian(min=1, max=5)
        model.fit(self.constant)

        params = model.to_dict()

        assert params == {
            'type': 'copulas.univariate.truncated_gaussian.TruncatedGaussian',
            'loc': 5,
            'scale': 0,
            'a': 5,
            'b': 5,
        }
    def test_to_dict_from_dict(self):
        model = TruncatedGaussian()
        model.fit(self.data)

        sampled_data = model.sample(50)

        params = model.to_dict()
        model2 = TruncatedGaussian.from_dict(params)

        pdf = model.probability_density(sampled_data)
        pdf2 = model2.probability_density(sampled_data)
        assert np.all(np.isclose(pdf, pdf2, atol=0.01))

        cdf = model.cumulative_distribution(sampled_data)
        cdf2 = model2.cumulative_distribution(sampled_data)
        assert np.all(np.isclose(cdf, cdf2, atol=0.01))
    def test_to_dict_from_dict_constant(self):
        model = TruncatedGaussian()
        model.fit(self.constant)

        sampled_data = model.sample(50)
        pdf = model.probability_density(sampled_data)
        cdf = model.cumulative_distribution(sampled_data)

        params = model.to_dict()
        model2 = TruncatedGaussian.from_dict(params)

        np.testing.assert_equal(np.full(50, 5), sampled_data)
        np.testing.assert_equal(np.full(50, 5), model2.sample(50))
        np.testing.assert_equal(np.full(50, 1), pdf)
        np.testing.assert_equal(np.full(50, 1), model2.probability_density(sampled_data))
        np.testing.assert_equal(np.full(50, 1), cdf)
        np.testing.assert_equal(np.full(50, 1), model2.cumulative_distribution(sampled_data))