text: from unittest import TestCase
  
  import numpy as np
  from scipy.stats import norm
  
  from copulas.univariate.gaussian import GaussianUnivariate
  
  
  class TestGaussianUnivariate(TestCase):
  
      def test__fit_constant(self):
          distribution = GaussianUnivariate()
  
          distribution._fit_constant(np.array([1, 1, 1, 1]))
  
          assert distribution._params == {
              'loc': 1,
              'scale': 0
          }
  
      def test__fit(self):
          distribution = GaussianUnivariate()
  
          data = norm.rvs(size=1000, loc=1, scale=1)
          distribution._fit(data)
  
          assert distribution._params == {
              'loc': np.mean(data),
              'scale': np.std(data),
          }
  
      def test__is_constant_true(self):
          distribution = GaussianUnivariate()
  
          distribution.fit(np.array([1, 1, 1, 1]))
  
          assert distribution._is_constant()
  
      def test__is_constant_false(self):
          distribution = GaussianUnivariate()
  
          distribution.fit(np.array([1, 2, 3, 4]))
  
          assert not distribution._is_constant()
  
      def test__extract_constant(self):
          distribution = GaussianUnivariate()
          distribution._params = {
              'loc': 1,
              'scale': 0
          }
  
          constant = distribution._extract_constant()
  
          assert 1 == constant
  