Fix formatting

src/analysis/forecast_twl.py

@@ -14,6 +14,7 @@ logger = setup_logging()
 
 MULTIPROCESS_THREADS = int(os.environ.get("MULTIPROCESS_THREADS", 4))
 
+
 def forecast_twl(
     df_tides,
     df_profiles,
@@ -53,12 +54,12 @@ def forecast_twl(
 
         # When calculating mean slope, we go from the dune toe to mhw. However, in some profiles, the dune toe is not
         # defined. In these cases, we should go to the dune crest
-        df_temp['top_elevation'] = df_temp['dune_toe_z']
-        df_temp.loc[df_temp.dune_toe_z.isnull(), 'top_elevation'] = df_temp.loc[df_temp.dune_toe_z.isnull(),
-                                                                                'dune_crest_z']
-        df_temp['top_x'] = df_temp['dune_toe_x']
-        df_temp.loc[df_temp.dune_toe_x.isnull(), 'top_x'] = df_temp.loc[df_temp.dune_toe_x.isnull(),
-                                                                                'dune_crest_x']
+        df_temp["top_elevation"] = df_temp["dune_toe_z"]
+        df_temp.loc[df_temp.dune_toe_z.isnull(), "top_elevation"] = df_temp.loc[
+            df_temp.dune_toe_z.isnull(), "dune_crest_z"
+        ]
+        df_temp["top_x"] = df_temp["dune_toe_x"]
+        df_temp.loc[df_temp.dune_toe_x.isnull(), "top_x"] = df_temp.loc[df_temp.dune_toe_x.isnull(), "dune_crest_x"]
 
         with Pool(processes=n_processes) as pool:
             results = pool.starmap(
@@ -66,7 +67,6 @@ def forecast_twl(
                 [(site_id, df_temp, df_profiles, "top_elevation", "top_x", "mhw") for site_id in site_ids],
             )
 
-
         df_twl["beta"] = pd.concat(results)
 
     # Estimate runup

src/analysis/observed_storm_impacts.py

@@ -154,22 +154,20 @@ def overwrite_impacts(df_observed_impacts, df_raw_features):
     :param df_raw_profile_features:
     :return:
     """
-    df_observed_impacts.update(df_raw_features.rename(columns={
-        'observed_storm_regime':'storm_regime'}))
+    df_observed_impacts.update(df_raw_features.rename(columns={"observed_storm_regime": "storm_regime"}))
     return df_observed_impacts
 
 
-
 @click.command()
 @click.option("--profiles-csv", required=True, help="")
 @click.option("--profile-features-crest-toes-csv", required=True, help="")
-@click.option("--raw-profile-features-csv", required=True,help="")
+@click.option("--raw-profile-features-csv", required=True, help="")
 @click.option("--output-file", required=True, help="")
-def create_observed_impacts(profiles_csv, profile_features_crest_toes_csv, raw_profile_features_csv,output_file):
+def create_observed_impacts(profiles_csv, profile_features_crest_toes_csv, raw_profile_features_csv, output_file):
 
-    profiles_csv = './data/interim/profiles.csv'
-    profile_features_crest_toes_csv= './data/interim/profile_features_crest_toes.csv'
-    raw_profile_features_csv = './data/raw/profile_features_chris_leaman/profile_features_chris_leaman.csv'
+    profiles_csv = "./data/interim/profiles.csv"
+    profile_features_crest_toes_csv = "./data/interim/profile_features_crest_toes.csv"
+    raw_profile_features_csv = "./data/raw/profile_features_chris_leaman/profile_features_chris_leaman.csv"
 
     logger.info("Creating observed wave impacts")
     logger.info("Importing data")

src/data/csv_to_geojson.py

@@ -76,8 +76,8 @@ def R_high_to_geojson(sites_csv, profiles_csv, crest_toes_csv, impacts_csv, outp
 
             # Take the intersection closest to the dune face.
             try:
-                dune_face_x = np.mean(df_crest_toes.loc[(site_id, 'prestorm'), :].x)
-                int_x = min(int_x, key=lambda x:abs(x-dune_face_x))
+                dune_face_x = np.mean(df_crest_toes.loc[(site_id, "prestorm"), :].x)
+                int_x = min(int_x, key=lambda x: abs(x - dune_face_x))
             except:
                 continue
 

src/data/parse_mat.py

@@ -25,24 +25,25 @@ def parse_crest_toes(df_raw_features, df_profiles):
     """
 
     # Puts profiles_features_csv into format expected by rest of analysis
-    df_crest_toes = df_raw_features.reset_index().melt(id_vars=['site_id'],
-                               value_vars=['prestorm_dune_crest_x', 'prestorm_dune_toe_x',
-                                      'poststorm_dune_crest_x', 'poststorm_dune_toe_x'])
-    df_crest_toes['profile_type'] = df_crest_toes.variable.str.extract(r'(prestorm|poststorm)')
-    df_crest_toes['point_type'] = df_crest_toes.variable.str.extract(r'(dune_crest_x|dune_toe_x)')
-    df_crest_toes = df_crest_toes.drop(columns=['variable'])
-    df_crest_toes = df_crest_toes.sort_values('site_id')
-    df_crest_toes = df_crest_toes.set_index(['site_id', 'profile_type', 'point_type'])
+    df_crest_toes = df_raw_features.reset_index().melt(
+        id_vars=["site_id"],
+        value_vars=["prestorm_dune_crest_x", "prestorm_dune_toe_x", "poststorm_dune_crest_x", "poststorm_dune_toe_x"],
+    )
+    df_crest_toes["profile_type"] = df_crest_toes.variable.str.extract(r"(prestorm|poststorm)")
+    df_crest_toes["point_type"] = df_crest_toes.variable.str.extract(r"(dune_crest_x|dune_toe_x)")
+    df_crest_toes = df_crest_toes.drop(columns=["variable"])
+    df_crest_toes = df_crest_toes.sort_values("site_id")
+    df_crest_toes = df_crest_toes.set_index(["site_id", "profile_type", "point_type"])
     df_crest_toes = df_crest_toes.unstack()
     df_crest_toes.columns = df_crest_toes.columns.droplevel()
 
     # Now let's calculate the corresponding z elevations for each of our x coordinates
     for site_id in df_crest_toes.index.get_level_values("site_id").unique():
-        logger.info('Calculating dune toe/crest z elevations for {}'.format(site_id))
+        logger.info("Calculating dune toe/crest z elevations for {}".format(site_id))
 
         # Get profile for this site
         idx = pd.IndexSlice
-        df_profile = df_profiles.loc[idx[site_id, :,:], :]
+        df_profile = df_profiles.loc[idx[site_id, :, :], :]
 
         for param in ["prestorm", "poststorm"]:
             for loc in ["crest", "toe"]:
@@ -55,13 +56,13 @@ def parse_crest_toes(df_raw_features, df_profiles):
                     continue
 
                 # Try get the value from the other profile if we return nan or empty dataframe
-                df_z = df_profile.loc[idx[site_id, param, x_val],:]
+                df_z = df_profile.loc[idx[site_id, param, x_val], :]
                 if df_z.empty:
                     if param == "prestorm":
-                        new_param = 'poststorm'
+                        new_param = "poststorm"
                     elif param == "poststorm":
-                        new_param = 'prestorm'
-                    z_val = df_profile.loc[idx[site_id, new_param, x_val],:].z
+                        new_param = "prestorm"
+                    z_val = df_profile.loc[idx[site_id, new_param, x_val], :].z
                 else:
                     z_val = df_z.z
 
@@ -80,6 +81,7 @@ def parse_crest_toes(df_raw_features, df_profiles):
 
     return df_crest_toes
 
+
 def parse_dune_crest_toes(df_sites, crest_mat, toe_mat):
     """
     :param df_sites:
@@ -128,7 +130,6 @@ def parse_dune_crest_toes(df_sites, crest_mat, toe_mat):
     return df_profile_features
 
 
-
 def parse_waves(waves_mat):
     """
     Parses the raw waves.mat file and returns a pandas dataframe
@@ -426,14 +427,13 @@ def create_crest_toes(profile_features_csv, profiles_csv, output_file):
     logger.info("Creating %s", output_file)
 
     df_raw_features = pd.read_csv(profile_features_csv, index_col=[0])
-    df_profiles = pd.read_csv(profiles_csv, index_col=[0,1,2])
+    df_profiles = pd.read_csv(profiles_csv, index_col=[0, 1, 2])
     df_crest_toes = parse_crest_toes(df_raw_features, df_profiles)
 
-    df_crest_toes.to_csv(output_file,float_format="%.3f")
+    df_crest_toes.to_csv(output_file, float_format="%.3f")
     logger.info("Created %s", output_file)
 
 
-
 @click.command(short_help="create profiles.csv")
 @click.option("--profiles-mat", required=True, help=".mat file containing beach profiles")
 @click.option("--profiles-output-file", required=True, help="where to save profiles.csv")
@@ -450,7 +450,7 @@ def create_sites_and_profiles_csv(profiles_mat, profiles_output_file, sites_outp
 
     df_profiles.to_csv(profiles_output_file)
     logger.info("Created %s", profiles_output_file)
-    df_sites.to_csv(sites_output_file,float_format="%.3f")
+    df_sites.to_csv(sites_output_file, float_format="%.3f")
     logger.info("Created %s", sites_output_file)