diff --git a/iot/generate_data.py b/iot/generate_data.py
index b0d2df6..27c00d0 100644
--- a/iot/generate_data.py
+++ b/iot/generate_data.py
@@ -63,9 +63,7 @@ def gen_microdata(
                 baseline = tc.Policy.read_json_reform(s)
             else:
                 baseline = s
-            pol1.implement_reform(
-                baseline, print_warnings=False, raise_errors=False
-            )
+            pol1.implement_reform(baseline, print_warnings=False, raise_errors=False)
     else:
         pol1 = tc.Policy()
 
diff --git a/iot/inverse_optimal_tax.py b/iot/inverse_optimal_tax.py
index e5aff57..32fbe4c 100644
--- a/iot/inverse_optimal_tax.py
+++ b/iot/inverse_optimal_tax.py
@@ -130,9 +130,7 @@ def compute_mtr_dist(
                     for each income bin
         """
         bins = 1000  # number of equal-width bins
-        data.loc[:, ["z_bin"]] = pd.cut(
-            data[income_measure], bins, include_lowest=True
-        )
+        data.loc[:, ["z_bin"]] = pd.cut(data[income_measure], bins, include_lowest=True)
         binned_data = pd.DataFrame(
             data[["mtr", income_measure, "z_bin", weight_var]]
             .groupby(["z_bin"])
@@ -191,14 +189,11 @@ def compute_income_dist(
         # drop zero income observations
         data = data[data[income_measure] > 0]
         if dist_type == "log_normal":
-            mu = (
-                np.log(data[income_measure]) * data[weight_var]
-            ).sum() / data[weight_var].sum()
+            mu = (np.log(data[income_measure]) * data[weight_var]).sum() / data[
+                weight_var
+            ].sum()
             sigmasq = (
-                (
-                    ((np.log(data[income_measure]) - mu) ** 2)
-                    * data[weight_var]
-                ).values
+                (((np.log(data[income_measure]) - mu) ** 2) * data[weight_var]).values
                 / data[weight_var].sum()
             ).sum()
             # F = st.lognorm.cdf(z_line, s=(sigmasq) ** 0.5, scale=np.exp(mu))
@@ -209,10 +204,7 @@ def compute_income_dist(
             # analytical derivative of lognormal
             sigma = np.sqrt(sigmasq)
             F = (1 / 2) * (
-                1
-                + scipy.special.erf(
-                    (np.log(z_line) - mu) / (np.sqrt(2) * sigma)
-                )
+                1 + scipy.special.erf((np.log(z_line) - mu) / (np.sqrt(2) * sigma))
             )
             f = (
                 (1 / (sigma * np.sqrt(2 * np.pi)))
@@ -269,9 +261,7 @@ def sw_weights(self):
         )
         # use Lockwood and Weinzierl formula, which should be equivalent but using numerical differentiation
         bracket_term = (
-            1
-            - self.F
-            - (self.mtr / (1 - self.mtr)) * self.eti * self.z * self.f
+            1 - self.F - (self.mtr / (1 - self.mtr)) * self.eti * self.z * self.f
         )
         # d_dz_bracket = np.gradient(bracket_term, edge_order=2)
         d_dz_bracket = np.diff(bracket_term) / np.diff(self.z)