black format

ogcore/SS.py

@@ -481,9 +481,9 @@ def inner_loop(outer_loop_vars, p, client):
         debt_service,
         p,
     )
-    print('Agg tax = ', total_tax_revenue)
-    print('Agg pension outlays = ', agg_pension_outlays)
-    print('Agg UBI outlays = ', UBI_outlays)
+    print("Agg tax = ", total_tax_revenue)
+    print("Agg pension outlays = ", agg_pension_outlays)
+    print("Agg UBI outlays = ", UBI_outlays)
     new_TR = fiscal.get_TR(
         Y,
         TR,
@@ -821,7 +821,9 @@ def SS_solver(
         np.squeeze(p.e[-1, :, :]),
         p,
     )
-    yss_before_tax_mat = household.get_y(r_p_ss, wss, bssmat_s, nssmat, p, "SS")
+    yss_before_tax_mat = household.get_y(
+        r_p_ss, wss, bssmat_s, nssmat, p, "SS"
+    )
     Css = aggr.get_C(cssmat, p, "SS")
     c_i_ss_mat = household.get_ci(
         cssmat, p_i_ss, p_tilde_ss, p.tau_c[-1, :], p.alpha_c

ogcore/TPI.py

@@ -818,7 +818,7 @@ def run_TPI(p, client=None):
             bmat_s[: p.T, :, :],
             n_mat[: p.T, :, :],
             p,
-            "TPI"
+            "TPI",
         )
 
         L[: p.T] = aggr.get_L(n_mat[: p.T], p, "TPI")

ogcore/aggregates.py

@@ -33,7 +33,11 @@ def get_L(n, p, method):
 
     """
     if method == "SS":
-        L_presum = np.squeeze(p.e[-1, :, :]) * np.transpose(p.omega_SS * p.lambdas) * n
+        L_presum = (
+            np.squeeze(p.e[-1, :, :])
+            * np.transpose(p.omega_SS * p.lambdas)
+            * n
+        )
         L = L_presum.sum()
     elif method == "TPI":
         L_presum = (n * (p.e * np.squeeze(p.lambdas))) * np.tile(
@@ -341,7 +345,7 @@ def revenue(
 
     """
     inc_pay_tax_liab = tax.income_tax_liab(
-        r, w, b, n, factor, 0, None, method, e, etr_params,p
+        r, w, b, n, factor, 0, None, method, e, etr_params, p
     )
     pension_benefits = tax.pension_amount(
         w, n, theta, 0, None, False, method, e, p

ogcore/household.py

@@ -387,7 +387,13 @@ def FOC_savings(
             tax_noncompliance = p.capital_income_tax_noncompliance_rate[
                 t : t + length, j
             ]
-            e_long = np.concatenate((p.e, np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1))), axis = 0)
+            e_long = np.concatenate(
+                (
+                    p.e,
+                    np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
+                ),
+                axis=0,
+            )
             e = np.diag(e_long[t : t + p.S, :, j], max(p.S - length, 0))
     else:
         chi_b = p.chi_b
@@ -403,7 +409,13 @@ def FOC_savings(
             tax_noncompliance = p.capital_income_tax_noncompliance_rate[
                 t : t + length, :
             ]
-            e_long = np.concatenate((p.e, np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1))), axis = 0)
+            e_long = np.concatenate(
+                (
+                    p.e,
+                    np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
+                ),
+                axis=0,
+            )
             e = np.diag(e_long[t : t + p.S, :, :], max(p.S - length, 0))
     e = np.squeeze(e)
     if method == "SS":
@@ -567,7 +579,13 @@ def FOC_labor(
             tax_noncompliance = p.labor_income_tax_noncompliance_rate[
                 t : t + length, j
             ]
-            e_long = np.concatenate((p.e, np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1))), axis = 0)
+            e_long = np.concatenate(
+                (
+                    p.e,
+                    np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
+                ),
+                axis=0,
+            )
             e = np.diag(e_long[t : t + p.S, :, j], max(p.S - length, 0))
     else:
         if method == "SS":
@@ -580,7 +598,13 @@ def FOC_labor(
             tax_noncompliance = p.labor_income_tax_noncompliance_rate[
                 t : t + length, :
             ]
-            e_long = np.concatenate((p.e, np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1))), axis = 0)
+            e_long = np.concatenate(
+                (
+                    p.e,
+                    np.tile(p.e[-1, :, :].reshape(1, p.S, p.J), (p.S, 1, 1)),
+                ),
+                axis=0,
+            )
             e = np.diag(e_long[t : t + p.S, :, j], max(p.S - length, 0))
     if method == "SS":
         tau_payroll = p.tau_payroll[-1]

ogcore/parameter_plots.py

@@ -914,7 +914,12 @@ def plot_income_data(
             # Plot of 3D, J>1 in levels
             fig10, ax10 = plt.subplots(subplot_kw={"projection": "3d"})
             ax10.plot_surface(
-                age_mesh, abil_mesh, emat[t, :, :], rstride=8, cstride=1, cmap=cmap1
+                age_mesh,
+                abil_mesh,
+                emat[t, :, :],
+                rstride=8,
+                cstride=1,
+                cmap=cmap1,
             )
             ax10.set_xlabel(r"age-$s$")
             ax10.set_ylabel(r"ability type -$j$")

tests/test_SS.py

@@ -585,8 +585,8 @@ def test_inner_loop(baseline, r_p, param_updates, filename, dask_client):
         print("Max diff = ", np.absolute(test_tuple[i] - v).max())
         print("Checking item = ", i)
         if np.absolute(test_tuple[i] - v).max() > 1.0:
-            print('test_value = ', test_tuple[i])
-            print('expected_value = ', v)
+            print("test_value = ", test_tuple[i])
+            print("expected_value = ", v)
 
     for i, v in enumerate(expected_tuple):
         print("Max diff = ", np.absolute(test_tuple[i] - v).max())

tests/test_aggregates.py

@@ -461,7 +461,9 @@ def test_get_C(c, p, method, expected):
 factor_u = 140000.0
 ubi_u = p_u.ubi_nom_array / factor_u
 # update parameters instance with new values for test
-p_u.e = 0.263 + (2.024 - 0.263) * random_state.rand(p.S * p.J).reshape(p.S, p.J)
+p_u.e = 0.263 + (2.024 - 0.263) * random_state.rand(p.S * p.J).reshape(
+    p.S, p.J
+)
 p_u.e = np.tile(p_u.e.reshape(1, p_u.S, p_u.J), (p_u.T, 1, 1))
 p_u.omega = 0.039 * random_state.rand(p_u.T * p_u.S * 1).reshape(p_u.T, p_u.S)
 p_u.omega = p_u.omega / p_u.omega.sum(axis=1).reshape(p_u.T, 1)

tests/test_firm.py

@@ -732,7 +732,7 @@ def test_get_K(L, r, p, method, expected):
     "epsilon": [1.0],
     "Z": [[2.0]],
     "T": 3,
-    "e": p2.e[0, :, :]
+    "e": p2.e[0, :, :],
 }
 # update parameters instance with new values for test
 p2.update_specifications(new_param_values2)
@@ -778,7 +778,7 @@ def test_get_MPx(Y, x, share, p, method, expected):
     "adjustment_factor_for_cit_receipts": [1.0],
     "c_corp_share_of_assets": 1.0,
     "T": 3,
-    "e": p1.e[0, :, :]
+    "e": p1.e[0, :, :],
 }
 # update parameters instance with new values for test
 p1.update_specifications(new_param_values1)
@@ -793,7 +793,7 @@ def test_get_MPx(Y, x, share, p, method, expected):
     "adjustment_factor_for_cit_receipts": [1.0],
     "c_corp_share_of_assets": 1.0,
     "T": 3,
-    "e": p2.e[0, :, :]
+    "e": p2.e[0, :, :],
 }
 # update parameters instance with new values for test
 p2.update_specifications(new_param_values2)
@@ -810,7 +810,7 @@ def test_get_MPx(Y, x, share, p, method, expected):
     "c_corp_share_of_assets": 1.0,
     "T": 3,
     "M": 2,
-    "e": p3.e[0, :, :]
+    "e": p3.e[0, :, :],
 }
 # update parameters instance with new values for test
 p3.update_specifications(new_param_values3)
@@ -920,7 +920,7 @@ def test_get_pm(w, Y, L, p, method, expected):
     "c_corp_share_of_assets": 1.0,
     "initial_Kg_ratio": 0.01,
     "T": 3,
-    "e": p5.e[0, :, :]
+    "e": p5.e[0, :, :],
 }
 # update parameters instance with new values for test
 p5.update_specifications(new_param_values5)
@@ -938,7 +938,7 @@ def test_get_pm(w, Y, L, p, method, expected):
     "c_corp_share_of_assets": 1.0,
     "initial_Kg_ratio": 0.01,
     "T": 3,
-    "e": p6.e[0, :, :]
+    "e": p6.e[0, :, :],
 }
 # update parameters instance with new values for test
 p6.update_specifications(new_param_values6)

tests/test_household.py

@@ -652,7 +652,9 @@ def test_FOC_savings(model_vars, in_params, expected):
     ) = model_vars
     params = copy.deepcopy(in_params)
     # reshape e matrix to be 3D
-    params.e = np.tile(params.e.reshape(1, params.S, params.J), (params.T, 1, 1))
+    params.e = np.tile(
+        params.e.reshape(1, params.S, params.J), (params.T, 1, 1)
+    )
     if method == "TPI":
         p_tilde = np.ones_like(w)
     else:
@@ -935,7 +937,9 @@ def test_FOC_labor(model_vars, params, expected):
         method,
     ) = model_vars
     # reshape e matrix for 3D
-    params.e = np.tile(params.e.reshape(1, params.S, params.J), (params.T, 1, 1))
+    params.e = np.tile(
+        params.e.reshape(1, params.S, params.J), (params.T, 1, 1)
+    )
     if method == "TPI":
         p_tilde = np.ones_like(w)
     else:

tests/test_tax.py

@@ -64,7 +64,9 @@ def test_replacement_rate_vals(n, w, factor, j, p_in, expected):
     # make e 3D
     p = copy.deepcopy(p_in)
     # p.e = np.tile(np.reshape(p.e, (1, p.S, p.J)), (p.T, 1, 1))
-    p.e = np.tile(np.reshape(p.e, (1, p.e.shape[0], p.e.shape[1])), (p.T, 1, 1))
+    p.e = np.tile(
+        np.reshape(p.e, (1, p.e.shape[0], p.e.shape[1])), (p.T, 1, 1)
+    )
     theta = tax.replacement_rate_vals(n, w, factor, j, p)
     assert np.allclose(theta, expected)