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)))
