old_contents: import pytest
  import numpy as np
  from ogusa import household
  
  
  test_data = [(0.1, 1, 10),
               (0.2, 2.5, 55.90169944),
               (np.array([0.5, 6.2, 1.5]), 3.2,
                np.array([9.18958684, 0.002913041, 0.273217159]))]
  
  
  @pytest.mark.parametrize('c,sigma,expected', test_data,
                           ids=['Scalar 0', 'Scalar 1', 'Vector'])
  def test_marg_ut_cons(c, sigma, expected):
      # Test marginal utility of consumption calculation
      test_value = household.marg_ut_cons(c, sigma)
  
      assert np.allclose(test_value, expected)
  
  
  # def test_marg_ut_labor():
  #
  #     assert np.allclose()
  #
  #
  # def test_get_cons():
  #
  #     assert np.allclose()
  #
  #
  # def test_FOC_savings():
  #
  #     assert np.allclose()
  #
  #
  # def test_FOC_labor():
  #
  #     assert np.allclose()
  #
  #
  # def test_constraint_checker_SS():
  #
  #     assert np.allclose()
  #
  #
  # def test_constraint_checker_TPI():
  #
  #     assert np.allclose()
  
new_contents: import pytest
  import numpy as np
  from ogusa import household
  
  
  test_data = [(0.1, 1, 10),
               (0.2, 2.5, 55.90169944),
               (np.array([0.5, 6.2, 1.5]), 3.2,
                np.array([9.18958684, 0.002913041, 0.273217159]))]
  
  
  @pytest.mark.parametrize('c,sigma,expected', test_data,
                           ids=['Scalar 0', 'Scalar 1', 'Vector'])
  def test_marg_ut_cons(c, sigma, expected):
      # Test marginal utility of consumption calculation
      test_value = household.marg_ut_cons(c, sigma)
  
      assert np.allclose(test_value, expected)
  
  
  # Note that params tuple in order: b_ellipse, upsilon, ltilde, chi_n
  test_data = [(0.87, (0.527, 1.497, 1.0, 3.3), 2.825570309),
               (0.0, (0.527, 1.497, 1.0, 3.3), 0),
               (0.99999, (0.527, 1.497, 1.0, 3.3), 69.52423604),
               (0.00001, (0.527, 1.497, 1.0, 3.3), 0.005692782),
               (0.8, (0.527, 0.9, 1.0, 3.3), 1.471592068),
               (0.8, (0.527, 0.9, 2.3, 3.3), 0.795937549),
               (0.8, (2.6, 1.497, 1.0, 3.3), 11.66354267),
               (np.array([[0.8, 0.9, 0.3], [0.5, 0.2, 0.99]]),
                (0.527, 1.497, 1.0, 3.3),
                np.array([[2.364110379, 3.126796062, 1.014935377],
                          [1.4248841, 0.806333875, 6.987729463]]))]
  
  
  @pytest.mark.parametrize('n,params,expected', test_data,
                           ids=['1', '2', '3', '4', '5', '6', '7', '8'])
  def test_marg_ut_labor(n, params, expected):
      # Test marginal utility of labor calculation
      test_value = household.marg_ut_labor(n, params)
  
      assert np.allclose(test_value, expected)
  
  
  # def test_get_cons():
  #
  #     assert np.allclose()
  #
  #
  # def test_FOC_savings():
  #
  #     assert np.allclose()
  #
  #
  # def test_FOC_labor():
  #
  #     assert np.allclose()
  #
  #
  # def test_constraint_checker_SS():
  #
  #     assert np.allclose()
  #
  #
  # def test_constraint_checker_TPI():
  #
  #     assert np.allclose()
  
subject: test MU_l