def test_twist_doughnut(file_inputs, file_outputs): ''' Test TPI.twist_doughnut function. Provide inputs to function and ensure that output returned matches what it has been before. ''' input_tuple = utils.safe_read_pickle(file_inputs) test_list = TPI.twist_doughnut(*input_tuple) expected_list = utils.safe_read_pickle(file_outputs) assert (np.allclose(np.array(test_list), np.array(expected_list)))
def test_twist_doughnut(file_inputs, file_outputs): ''' Test TPI.twist_doughnut function. Provide inputs to function and ensure that output returned matches what it has been before. ''' input_tuple = utils.safe_read_pickle(file_inputs) (guesses, r, w, bq, tr, theta, factor, j, s, t, tau_c, etr_params, mtrx_params, mtry_params, initial_b, p) = input_tuple input_tuple = (guesses, r, w, bq, tr, theta, factor, j, s, t, tau_c, etr_params, mtrx_params, mtry_params, initial_b, p) test_list = TPI.twist_doughnut(*input_tuple) expected_list = utils.safe_read_pickle(file_outputs) assert (np.allclose(np.array(test_list), np.array(expected_list)))
def test_twist_doughnut(): # Test TPI.twist_doughnut function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/twist_doughnut_inputs.pkl')) guesses, r, w, BQ, T_H, j, s, t, params = input_tuple income_tax_params, tpi_params, initial_b = params tpi_params = tpi_params + [True] income_tax_params = ('DEP',) + income_tax_params params = (income_tax_params, tpi_params, initial_b) test_list = TPI.twist_doughnut(guesses, r, w, BQ, T_H, j, s, t, params) expected_list = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/twist_doughnut_outputs.pkl')) assert(np.allclose(np.array(test_list), np.array(expected_list)))
def test_twist_doughnut(): # Test TPI.twist_doughnut function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data', 'twist_doughnut_inputs.pkl')) guesses, r, w, BQ, TR, j, s, t, params = input_tuple income_tax_params, tpi_params, initial_b = params tpi_params = tpi_params + [True] p = Specifications() (p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon, Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n, tau_b, delta_tau, tau_payroll, tau_bq, p.rho, p.omega, N_tilde, lambdas, p.imm_rates, p.e, retire, p.mean_income_data, factor, h_wealth, p_wealth, m_wealth, p.b_ellipse, p.upsilon, p.chi_b, p.chi_n, theta, p.baseline) = tpi_params p.Z = np.ones(p.T + p.S) * Z p.tau_bq = np.ones(p.T + p.S) * 0.0 p.tau_c = np.ones((p.T + p.S, p.S, p.J)) * 0.0 p.tau_payroll = np.ones(p.T + p.S) * tau_payroll p.tau_b = np.ones(p.T + p.S) * tau_b p.delta_tau = np.ones(p.T + p.S) * delta_tau p.h_wealth = np.ones(p.T + p.S) * h_wealth p.p_wealth = np.ones(p.T + p.S) * p_wealth p.m_wealth = np.ones(p.T + p.S) * m_wealth p.retire = (np.ones(p.T + p.S) * retire).astype(int) p.tax_func_type = 'DEP' p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\ income_tax_params p.lambdas = lambdas.reshape(p.J, 1) p.num_workers = 1 length = int(len(guesses) / 2) tau_c_to_use = np.diag(p.tau_c[:p.S, :, j], p.S - (s + 2)) bq = BQ[t:t + length] / p.lambdas[j] tr = TR[t:t + length] test_list = TPI.twist_doughnut(guesses, r, w, bq, tr, theta, factor, j, s, t, tau_c_to_use, etr_params, mtrx_params, mtry_params, initial_b, p) expected_list = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data', 'twist_doughnut_outputs.pkl')) assert(np.allclose(np.array(test_list), np.array(expected_list)))
def test_twist_doughnut(): # Test TPI.twist_doughnut function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/twist_doughnut_inputs.pkl')) guesses, r, w, BQ, T_H, j, s, t, params = input_tuple income_tax_params, tpi_params, initial_b = params tpi_params = tpi_params + [True] p = Specifications() (p.J, p.S, p.T, p.BW, p.beta, p.sigma, p.alpha, p.gamma, p.epsilon, Z, p.delta, p.ltilde, p.nu, p.g_y, p.g_n, tau_b, delta_tau, tau_payroll, tau_bq, p.rho, p.omega, N_tilde, lambdas, p.imm_rates, p.e, retire, p.mean_income_data, factor, h_wealth, p_wealth, m_wealth, p.b_ellipse, p.upsilon, p.chi_b, p.chi_n, theta, p.baseline) = tpi_params p.Z = np.ones(p.T + p.S) * Z p.tau_bq = np.ones(p.T + p.S) * 0.0 p.tau_c = np.ones((p.T + p.S, p.S, p.J)) * 0.0 p.tau_payroll = np.ones(p.T + p.S) * tau_payroll p.tau_b = np.ones(p.T + p.S) * tau_b p.delta_tau = np.ones(p.T + p.S) * delta_tau p.h_wealth = np.ones(p.T + p.S) * h_wealth p.p_wealth = np.ones(p.T + p.S) * p_wealth p.m_wealth = np.ones(p.T + p.S) * m_wealth p.retire = (np.ones(p.T + p.S) * retire).astype(int) p.tax_func_type = 'DEP' p.analytical_mtrs, etr_params, mtrx_params, mtry_params =\ income_tax_params p.lambdas = lambdas.reshape(p.J, 1) p.num_workers = 1 length = int(len(guesses) / 2) tau_c_to_use = np.diag(p.tau_c[:p.S, :, j], p.S - (s + 2)) bq = BQ[t:t + length] / p.lambdas[j] test_list = TPI.twist_doughnut(guesses, r, w, bq, T_H, theta, factor, j, s, t, tau_c_to_use, etr_params, mtrx_params, mtry_params, initial_b, p) expected_list = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/twist_doughnut_outputs.pkl')) assert(np.allclose(np.array(test_list), np.array(expected_list)))