diff --git a/src/analysis/forecast_twl.py b/src/analysis/forecast_twl.py
index 0fab400..7bab74b 100644
--- a/src/analysis/forecast_twl.py
+++ b/src/analysis/forecast_twl.py
@@ -59,7 +59,9 @@ def forecast_twl(
         df_twl["beta"] = pd.concat(results)
 
     # Estimate runup
-    R2, setup, S_total, S_inc, S_ig = runup_function(Hs0=df_twl['Hs0'].tolist(), Tp=df_twl["Tp"].tolist(), beta=df_twl["beta"].tolist())
+    R2, setup, S_total, S_inc, S_ig = runup_function(
+        Hs0=df_twl["Hs0"].tolist(), Tp=df_twl["Tp"].tolist(), beta=df_twl["beta"].tolist()
+    )
 
     df_twl["R2"] = R2
     df_twl["setup"] = setup
diff --git a/src/analysis/runup_models.py b/src/analysis/runup_models.py
index 9176adb..c508f65 100644
--- a/src/analysis/runup_models.py
+++ b/src/analysis/runup_models.py
@@ -12,7 +12,7 @@ def sto06(Hs0, Tp, beta):
 
     df = pd.DataFrame({"Hs0": Hs0, "Tp": Tp, "beta": beta}, index=[x for x in range(0, np.size(Hs0))])
 
-    df["Lp"] = 9.8 * df['Tp'] ** 2 / 2 / np.pi
+    df["Lp"] = 9.8 * df["Tp"] ** 2 / 2 / np.pi
 
     # General equation
     df["S_ig"] = pd.to_numeric(0.06 * np.sqrt(df["Hs0"] * df["Lp"]), errors="coerce")
diff --git a/src/data/parse_mat.py b/src/data/parse_mat.py
index 4020479..25a867a 100644
--- a/src/data/parse_mat.py
+++ b/src/data/parse_mat.py
@@ -200,7 +200,7 @@ def parse_profiles_and_sites(profiles_mat):
 
     # Our z values can come from these columns, depending on the isgood flag.
     # Let's reoganise them into a list of list
-    z_names = ["Zpre", 'Zpost', 'Zrec1', 'Zrec2', 'Zrec3', 'Zrec4']
+    z_names = ["Zpre", "Zpost", "Zrec1", "Zrec2", "Zrec3", "Zrec4"]
     z_cols = [mat_data[col] for col in z_names]
     z_sites = []
     for cols in zip(*z_cols):
@@ -226,8 +226,6 @@ def parse_profiles_and_sites(profiles_mat):
         # Want to calculation the orientation
         orientation = {}
 
-
-
         for x, lat, lon, z_site, easting, northing in zip(
             mat_data["x"][i],
             mat_data["lats"][i],
@@ -237,13 +235,12 @@ def parse_profiles_and_sites(profiles_mat):
             mat_data["northings"][i],
         ):
 
-
             profile_type = None
             for j, is_good in enumerate([1] + mat_data["isgood"][i]):
 
                 # Assumes the first profile is always good and is the prestorm profike
                 if j == 0:
-                    profile_type = 'prestorm'
+                    profile_type = "prestorm"
                     z = z_site[j]
                     land_lim = np.nan
 
@@ -253,7 +250,7 @@ def parse_profiles_and_sites(profiles_mat):
 
                 # Takes the first isgood profile as the post storm profile
                 else:
-                    profile_type = 'poststorm'
+                    profile_type = "poststorm"
                     z = z_site[j]
                     land_lim = mat_data["landlims"][i][j]
 
@@ -287,10 +284,9 @@ def parse_profiles_and_sites(profiles_mat):
                 )
 
                 # Stop looking at profiles if we've got our post-storm profile
-                if profile_type == 'poststorm':
+                if profile_type == "poststorm":
                     break
 
-
         orientation = math.degrees(
             math.atan2(
                 orientation["land_northing"] - orientation["sea_northing"],
@@ -334,8 +330,7 @@ def remove_zeros(df_profiles):
         )
         df_profile = df_profiles[idx_site]
         x_last_ele = df_profile[df_profile.z == 0].index.get_level_values("x")[0]
-        df_profiles.loc[idx_site & (df_profiles.index.get_level_values("x") > x_last_ele),
-                        "z"] = np.nan
+        df_profiles.loc[idx_site & (df_profiles.index.get_level_values("x") > x_last_ele), "z"] = np.nan
     logger.info("Removed zeros from end of profiles")
 
     return df_profiles