Fix formatting with black

src/analysis/analysis.py

@@ -1,13 +1,15 @@
 import pandas as pd
 import os
 
+
 def main():
 
-    data_folder = './data/interim'
-    df_waves = pd.read_csv(os.path.join(data_folder, 'waves.csv'), index_col=[0,1])
-    df_tides = pd.read_csv(os.path.join(data_folder, 'tides.csv'), index_col=[0,1])
-    df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0,1,2])
-    df_sites = pd.read_csv(os.path.join(data_folder, 'sites.csv'),index_col=[0])
+    data_folder = "./data/interim"
+    df_waves = pd.read_csv(os.path.join(data_folder, "waves.csv"), index_col=[0, 1])
+    df_tides = pd.read_csv(os.path.join(data_folder, "tides.csv"), index_col=[0, 1])
+    df_profiles = pd.read_csv(os.path.join(data_folder, "profiles.csv"), index_col=[0, 1, 2])
+    df_sites = pd.read_csv(os.path.join(data_folder, "sites.csv"), index_col=[0])
+
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

src/analysis/compare_impacts.py

@@ -7,7 +7,7 @@ import os
 
 import pandas as pd
 
-logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False)
+logging.config.fileConfig("./src/logging.conf", disable_existing_loggers=False)
 logger = logging.getLogger(__name__)
 
 
@@ -18,15 +18,16 @@ def compare_impacts(df_forecasted, df_observed):
     :param df_observed:
     :return:
     """
-    df_compared = df_forecasted.merge(df_observed, left_index=True, right_index=True,
-                                      suffixes=['_forecasted', '_observed'])
+    df_compared = df_forecasted.merge(
+        df_observed, left_index=True, right_index=True, suffixes=["_forecasted", "_observed"]
+    )
     return df_compared
 
 
-if __name__ == '__main__':
-    logger.info('Importing existing data')
-    data_folder = './data/interim'
-    df_forecasted = pd.read_csv(os.path.join(data_folder, 'impacts_forecasted_mean_slope_sto06.csv'), index_col=[0])
-    df_observed = pd.read_csv(os.path.join(data_folder, 'impacts_observed.csv'), index_col=[0])
+if __name__ == "__main__":
+    logger.info("Importing existing data")
+    data_folder = "./data/interim"
+    df_forecasted = pd.read_csv(os.path.join(data_folder, "impacts_forecasted_mean_slope_sto06.csv"), index_col=[0])
+    df_observed = pd.read_csv(os.path.join(data_folder, "impacts_observed.csv"), index_col=[0])
     df_compared = compare_impacts(df_forecasted, df_observed)
-    df_compared.to_csv(os.path.join(data_folder, 'impacts_observed_vs_forecasted_mean_slope_sto06.csv'))
+    df_compared.to_csv(os.path.join(data_folder, "impacts_observed_vs_forecasted_mean_slope_sto06.csv"))

src/analysis/forecast_twl.py

@@ -9,54 +9,57 @@ from scipy import stats
 
 from src.analysis.runup_models import sto06_individual, sto06
 
-logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False)
+logging.config.fileConfig("./src/logging.conf", disable_existing_loggers=False)
 logger = logging.getLogger(__name__)
 
 
-def forecast_twl(df_tides, df_profiles, df_waves, df_profile_features, runup_function, n_processes=4,
-                 slope='foreshore'):
+def forecast_twl(
+    df_tides, df_profiles, df_waves, df_profile_features, runup_function, n_processes=4, slope="foreshore"
+):
     # Use df_waves as a base
     df_twl = df_waves.copy()
 
     # Merge tides
-    logger.info('Merging tides')
+    logger.info("Merging tides")
     df_twl = df_twl.merge(df_tides, left_index=True, right_index=True)
 
     # Estimate foreshore slope. Do the analysis per site_id. This is so we only have to query the x and z
     # cross-section profiles once per site.
-    logger.info('Calculating beach slopes')
-    site_ids = df_twl.index.get_level_values('site_id').unique()
+    logger.info("Calculating beach slopes")
+    site_ids = df_twl.index.get_level_values("site_id").unique()
     # site_ids = [x for x in site_ids if 'NARRA' in x]  # todo remove this - for testing narrabeen only
 
-    if slope == 'foreshore':
+    if slope == "foreshore":
         # Process each site_id with a different process and combine results at the end
         with Pool(processes=n_processes) as pool:
-            results = pool.starmap(foreshore_slope_for_site_id,
-                                   [(site_id, df_twl, df_profiles) for site_id in site_ids])
-        df_twl['beta'] = pd.concat(results)
+            results = pool.starmap(
+                foreshore_slope_for_site_id, [(site_id, df_twl, df_profiles) for site_id in site_ids]
+            )
+        df_twl["beta"] = pd.concat(results)
 
-    elif slope == 'mean':
+    elif slope == "mean":
         # todo mean beach profile
-        df_temp = df_twl.join(df_profile_features, how='inner')
-        df_temp['mhw'] = 0.5
+        df_temp = df_twl.join(df_profile_features, how="inner")
+        df_temp["mhw"] = 0.5
         with Pool(processes=n_processes) as pool:
-            results = pool.starmap(mean_slope_for_site_id,
-                                   [(site_id, df_temp, df_profiles, 'dune_toe_z', 'mhw') for site_id in site_ids])
-        df_twl['beta'] = pd.concat(results)
+            results = pool.starmap(
+                mean_slope_for_site_id, [(site_id, df_temp, df_profiles, "dune_toe_z", "mhw") for site_id in site_ids]
+            )
+        df_twl["beta"] = pd.concat(results)
 
     # Estimate runup
-    R2, setup, S_total, S_inc, S_ig = runup_function(df_twl, Hs0_col='Hs0', Tp_col='Tp', beta_col='beta')
+    R2, setup, S_total, S_inc, S_ig = runup_function(df_twl, Hs0_col="Hs0", Tp_col="Tp", beta_col="beta")
 
-    df_twl['R2'] = R2
-    df_twl['setup'] = setup
-    df_twl['S_total'] = S_total
+    df_twl["R2"] = R2
+    df_twl["setup"] = setup
+    df_twl["S_total"] = S_total
 
     # Estimate TWL
-    df_twl['R_high'] = df_twl['tide'] + df_twl['R2']
-    df_twl['R_low'] = df_twl['tide'] + 1.1 * df_twl['setup'] - 1.1 / 2 * df_twl['S_total']
+    df_twl["R_high"] = df_twl["tide"] + df_twl["R2"]
+    df_twl["R_low"] = df_twl["tide"] + 1.1 * df_twl["setup"] - 1.1 / 2 * df_twl["S_total"]
 
     # Drop unneeded columns
-    df_twl.drop(columns=['E', 'Exs', 'P', 'Pxs', 'dir'], inplace=True, errors='ignore')
+    df_twl.drop(columns=["E", "Exs", "P", "Pxs", "dir"], inplace=True, errors="ignore")
 
     return df_twl
 
@@ -74,15 +77,21 @@ def mean_slope_for_site_id(site_id, df_twl, df_profiles, top_elevation_col, btm_
 
     # Get the prestorm beach profile
     profile = df_profiles.query("site_id =='{}' and profile_type == 'prestorm'".format(site_id))
-    profile_x = profile.index.get_level_values('x').tolist()
+    profile_x = profile.index.get_level_values("x").tolist()
     profile_z = profile.z.tolist()
 
     df_twl_site = df_twl.query("site_id == '{}'".format(site_id))
 
-    df_beta = df_twl_site.apply(lambda row: slope_from_profile(profile_x=profile_x, profile_z=profile_z,
-                                                               top_elevation=row[top_elevation_col],
-                                                               btm_elevation=row[btm_elevation_col],
-                                                               method='end_points'), axis=1)
+    df_beta = df_twl_site.apply(
+        lambda row: slope_from_profile(
+            profile_x=profile_x,
+            profile_z=profile_z,
+            top_elevation=row[top_elevation_col],
+            btm_elevation=row[btm_elevation_col],
+            method="end_points",
+        ),
+        axis=1,
+    )
     return df_beta
 
 
@@ -99,16 +108,22 @@ def foreshore_slope_for_site_id(site_id, df_twl, df_profiles):
 
     # Get the prestorm beach profile
     profile = df_profiles.query("site_id =='{}' and profile_type == 'prestorm'".format(site_id))
-    profile_x = profile.index.get_level_values('x').tolist()
+    profile_x = profile.index.get_level_values("x").tolist()
     profile_z = profile.z.tolist()
 
     df_twl_site = df_twl.query("site_id == '{}'".format(site_id))
 
-    df_beta = df_twl_site.apply(lambda row: foreshore_slope_from_profile(profile_x=profile_x, profile_z=profile_z,
-                                                                         tide=row.tide,
-                                                                         runup_function=sto06_individual,
-                                                                         Hs0=row.Hs0,
-                                                                         Tp=row.Tp), axis=1)
+    df_beta = df_twl_site.apply(
+        lambda row: foreshore_slope_from_profile(
+            profile_x=profile_x,
+            profile_z=profile_z,
+            tide=row.tide,
+            runup_function=sto06_individual,
+            Hs0=row.Hs0,
+            Tp=row.Tp,
+        ),
+        axis=1,
+    )
     return df_beta
 
 
@@ -137,9 +152,13 @@ def foreshore_slope_from_profile(profile_x, profile_z, tide, runup_function, **k
 
     while True:
         R2, setup, S_total, _, _ = runup_function(beta=beta, **kwargs)
-        beta_new = slope_from_profile(profile_x=profile_x, profile_z=profile_z, method='end_points',
-                                      top_elevation=tide + setup + S_total / 2,
-                                      btm_elevation=tide + setup - S_total / 2)
+        beta_new = slope_from_profile(
+            profile_x=profile_x,
+            profile_z=profile_z,
+            method="end_points",
+            top_elevation=tide + setup + S_total / 2,
+            btm_elevation=tide + setup - S_total / 2,
+        )
 
         # Return None if we can't find a slope, usually because the elevations we've specified are above/below our
         # profile x and z coordinates.
@@ -158,7 +177,7 @@ def foreshore_slope_from_profile(profile_x, profile_z, tide, runup_function, **k
         iteration_count += 1
 
 
-def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, method='end_points'):
+def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, method="end_points"):
     """
     Returns a slope (beta) from a bed profile, given the top and bottom elevations of where the slope should be taken.
     :param x: List of x bed profile coordinates
@@ -173,16 +192,10 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
     if any([x is None for x in [profile_x, profile_z, top_elevation, btm_elevation]]):
         return None
 
-    end_points = {
-        'top': {
-            'z': top_elevation,
-        },
-        'btm': {
-            'z': btm_elevation,
-        }}
+    end_points = {"top": {"z": top_elevation}, "btm": {"z": btm_elevation}}
 
     for end_type in end_points.keys():
-        elevation = end_points[end_type]['z']
+        elevation = end_points[end_type]["z"]
         intersection_x = crossings(profile_x, profile_z, elevation)
 
         # No intersections found
@@ -191,26 +204,26 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
 
         # One intersection
         elif len(intersection_x) == 1:
-            end_points[end_type]['x'] = intersection_x[0]
+            end_points[end_type]["x"] = intersection_x[0]
 
         # More than on intersection
         else:
-            if end_type == 'top':
+            if end_type == "top":
                 # For top elevation, take most seaward intersection
-                end_points[end_type]['x'] = intersection_x[-1]
+                end_points[end_type]["x"] = intersection_x[-1]
             else:
                 # For bottom elevation, take most landward intersection that is seaward of top elevation
-                end_points[end_type]['x'] = [x for x in intersection_x if x > end_points['top']['x']][0]
+                end_points[end_type]["x"] = [x for x in intersection_x if x > end_points["top"]["x"]][0]
 
-    if method == 'end_points':
-        x_top = end_points['top']['x']
-        x_btm = end_points['btm']['x']
-        z_top = end_points['top']['z']
-        z_btm = end_points['btm']['z']
+    if method == "end_points":
+        x_top = end_points["top"]["x"]
+        x_btm = end_points["btm"]["x"]
+        z_top = end_points["top"]["z"]
+        z_btm = end_points["btm"]["z"]
         return -(z_top - z_btm) / (x_top - x_btm)
 
-    elif method == 'least_squares':
-        profile_mask = [True if end_points['top']['x'] < pts < end_points['btm']['x'] else False for pts in x]
+    elif method == "least_squares":
+        profile_mask = [True if end_points["top"]["x"] < pts < end_points["btm"]["x"] else False for pts in x]
         slope_x = np.array(profile_x)[profile_mask].tolist()
         slope_z = np.array(profile_z)[profile_mask].tolist()
         slope, _, _, _, _ = stats.linregress(slope_x, slope_z)
@@ -245,23 +258,25 @@ def crossings(profile_x, profile_z, constant_z):
     return [profile_x[i] - (profile_x[i] - profile_x[i + 1]) / (z[i] - z[i + 1]) * (z[i]) for i in indicies]
 
 
-if __name__ == '__main__':
-    logger.info('Importing data')
-    data_folder = './data/interim'
-    df_waves = pd.read_csv(os.path.join(data_folder, 'waves.csv'), index_col=[0, 1])
-    df_tides = pd.read_csv(os.path.join(data_folder, 'tides.csv'), index_col=[0, 1])
-    df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0, 1, 2])
-    df_sites = pd.read_csv(os.path.join(data_folder, 'sites.csv'), index_col=[0])
-    df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'), index_col=[0])
+if __name__ == "__main__":
+    logger.info("Importing data")
+    data_folder = "./data/interim"
+    df_waves = pd.read_csv(os.path.join(data_folder, "waves.csv"), index_col=[0, 1])
+    df_tides = pd.read_csv(os.path.join(data_folder, "tides.csv"), index_col=[0, 1])
+    df_profiles = pd.read_csv(os.path.join(data_folder, "profiles.csv"), index_col=[0, 1, 2])
+    df_sites = pd.read_csv(os.path.join(data_folder, "sites.csv"), index_col=[0])
+    df_profile_features = pd.read_csv(os.path.join(data_folder, "profile_features.csv"), index_col=[0])
 
-    logger.info('Forecasting TWL')
+    logger.info("Forecasting TWL")
 
-    df_twl_foreshore_slope_sto06 = forecast_twl(df_tides, df_profiles, df_waves, df_profile_features,
-                                                runup_function=sto06, slope='foreshore')
-    df_twl_foreshore_slope_sto06.to_csv(os.path.join(data_folder, 'twl_foreshore_slope_sto06.csv'))
+    df_twl_foreshore_slope_sto06 = forecast_twl(
+        df_tides, df_profiles, df_waves, df_profile_features, runup_function=sto06, slope="foreshore"
+    )
+    df_twl_foreshore_slope_sto06.to_csv(os.path.join(data_folder, "twl_foreshore_slope_sto06.csv"))
 
-    df_twl_mean_slope_sto06 = forecast_twl(df_tides, df_profiles, df_waves, df_profile_features,
-                                           runup_function=sto06, slope='mean')
-    df_twl_mean_slope_sto06.to_csv(os.path.join(data_folder, 'twl_mean_slope_sto06.csv'))
+    df_twl_mean_slope_sto06 = forecast_twl(
+        df_tides, df_profiles, df_waves, df_profile_features, runup_function=sto06, slope="mean"
+    )
+    df_twl_mean_slope_sto06.to_csv(os.path.join(data_folder, "twl_mean_slope_sto06.csv"))
 
-    logger.info('Done')
+    logger.info("Done")

src/analysis/forecasted_storm_impacts.py

@@ -7,7 +7,7 @@ import os
 
 import pandas as pd
 
-logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False)
+logging.config.fileConfig("./src/logging.conf", disable_existing_loggers=False)
 logger = logging.getLogger(__name__)
 
 
@@ -19,20 +19,19 @@ def forecasted_impacts(df_profile_features, df_forecasted_twl):
     :param df_forecasted_twl:
     :return:
     """
-    logger.info('Getting forecasted storm regimes')
+    logger.info("Getting forecasted storm regimes")
 
     df_forecasted_impacts = pd.DataFrame(index=df_profile_features.index)
 
     # For each site, find the maximum R_high value and the corresponding R_low value.
-    idx = df_forecasted_twl.groupby(level=['site_id'])['R_high'].idxmax().dropna()
-    df_r_vals = df_forecasted_twl.loc[idx, ['R_high', 'R_low']].reset_index(['datetime'])
-    df_forecasted_impacts = df_forecasted_impacts.merge(df_r_vals, how='left', left_index=True, right_index=True)
+    idx = df_forecasted_twl.groupby(level=["site_id"])["R_high"].idxmax().dropna()
+    df_r_vals = df_forecasted_twl.loc[idx, ["R_high", "R_low"]].reset_index(["datetime"])
+    df_forecasted_impacts = df_forecasted_impacts.merge(df_r_vals, how="left", left_index=True, right_index=True)
 
     # Join with df_profile features to find dune toe and crest elevations
-    df_forecasted_impacts = df_forecasted_impacts.merge(df_profile_features[['dune_toe_z', 'dune_crest_z']],
-                                                        how='left',
-                                                        left_index=True,
-                                                        right_index=True)
+    df_forecasted_impacts = df_forecasted_impacts.merge(
+        df_profile_features[["dune_toe_z", "dune_crest_z"]], how="left", left_index=True, right_index=True
+    )
 
     # Compare R_high and R_low wirth dune crest and toe elevations
     df_forecasted_impacts = storm_regime(df_forecasted_impacts)
@@ -47,27 +46,33 @@ def storm_regime(df_forecasted_impacts):
     :param df_forecasted_impacts:
     :return:
     """
-    logger.info('Getting forecasted storm regimes')
+    logger.info("Getting forecasted storm regimes")
     df_forecasted_impacts.loc[
-        df_forecasted_impacts.R_high <= df_forecasted_impacts.dune_toe_z, 'storm_regime'] = 'swash'
+        df_forecasted_impacts.R_high <= df_forecasted_impacts.dune_toe_z, "storm_regime"
+    ] = "swash"
     df_forecasted_impacts.loc[
-        df_forecasted_impacts.dune_toe_z <= df_forecasted_impacts.R_high, 'storm_regime'] = 'collision'
-    df_forecasted_impacts.loc[(df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high) &
-                              (df_forecasted_impacts.R_low <= df_forecasted_impacts.dune_crest_z),
-                              'storm_regime'] = 'overwash'
-    df_forecasted_impacts.loc[(df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_low) &
-                              (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high),
-                              'storm_regime'] = 'inundation'
+        df_forecasted_impacts.dune_toe_z <= df_forecasted_impacts.R_high, "storm_regime"
+    ] = "collision"
+    df_forecasted_impacts.loc[
+        (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high)
+        & (df_forecasted_impacts.R_low <= df_forecasted_impacts.dune_crest_z),
+        "storm_regime",
+    ] = "overwash"
+    df_forecasted_impacts.loc[
+        (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_low)
+        & (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high),
+        "storm_regime",
+    ] = "inundation"
 
     return df_forecasted_impacts
 
 
-if __name__ == '__main__':
-    logger.info('Importing existing data')
-    data_folder = './data/interim'
-    df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0, 1, 2])
-    df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'), index_col=[0])
-    df_forecasted_twl = pd.read_csv(os.path.join(data_folder, 'twl_mean_slope_sto06.csv'), index_col=[0, 1])
+if __name__ == "__main__":
+    logger.info("Importing existing data")
+    data_folder = "./data/interim"
+    df_profiles = pd.read_csv(os.path.join(data_folder, "profiles.csv"), index_col=[0, 1, 2])
+    df_profile_features = pd.read_csv(os.path.join(data_folder, "profile_features.csv"), index_col=[0])
+    df_forecasted_twl = pd.read_csv(os.path.join(data_folder, "twl_mean_slope_sto06.csv"), index_col=[0, 1])
 
     df_forecasted_impacts = forecasted_impacts(df_profile_features, df_forecasted_twl)
-    df_forecasted_impacts.to_csv(os.path.join(data_folder, 'impacts_forecasted_mean_slope_sto06.csv'))
+    df_forecasted_impacts.to_csv(os.path.join(data_folder, "impacts_forecasted_mean_slope_sto06.csv"))

src/analysis/observed_storm_impacts.py

@@ -5,7 +5,7 @@ import numpy as np
 import pandas as pd
 from scipy.integrate import simps
 
-logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False)
+logging.config.fileConfig("./src/logging.conf", disable_existing_loggers=False)
 logger = logging.getLogger(__name__)
 
 
@@ -29,14 +29,14 @@ def volume_change(df_profiles, df_profile_features, zone):
     :param zone: Either 'swash' or 'dune_face'
     :return:
     """
-    logger.info('Calculating change in beach volume in {} zone'.format(zone))
+    logger.info("Calculating change in beach volume in {} zone".format(zone))
 
     df_vol_changes = pd.DataFrame(index=df_profile_features.index)
     df_profiles = df_profiles.sort_index()
-    sites = df_profiles.groupby(level=['site_id'])
+    sites = df_profiles.groupby(level=["site_id"])
 
     for site_id, df_site in sites:
-        logger.debug('Calculating change in beach volume at {} in {} zone'.format(site_id, zone))
+        logger.debug("Calculating change in beach volume at {} in {} zone".format(site_id, zone))
         prestorm_dune_toe_x = df_profile_features.loc[df_profile_features.index == site_id].dune_toe_x.tolist()
         prestorm_dune_crest_x = df_profile_features.loc[df_profile_features.index == site_id].dune_crest_x.tolist()
 
@@ -50,36 +50,44 @@ def volume_change(df_profiles, df_profile_features, zone):
 
         # Find last x coordinate where we have both prestorm and poststorm measurements. If we don't do this,
         # the prestorm and poststorm values are going to be calculated over different lengths.
-        df_zone = df_site.dropna(subset=['z'])
-        x_last_obs = min([max(df_zone.query("profile_type == '{}'".format(profile_type)).index.get_level_values('x'))
-                          for profile_type in ['prestorm', 'poststorm']])
+        df_zone = df_site.dropna(subset=["z"])
+        x_last_obs = min(
+            [
+                max(df_zone.query("profile_type == '{}'".format(profile_type)).index.get_level_values("x"))
+                for profile_type in ["prestorm", "poststorm"]
+            ]
+        )
 
         # Where we want to measure pre and post storm volume is dependant on the zone selected
-        if zone == 'swash':
+        if zone == "swash":
             x_min = prestorm_dune_toe_x
             x_max = x_last_obs
-        elif zone == 'dune_face':
+        elif zone == "dune_face":
             x_min = prestorm_dune_crest_x
             x_max = prestorm_dune_toe_x
         else:
-            logger.warning('Zone argument not properly specified. Please check')
+            logger.warning("Zone argument not properly specified. Please check")
             x_min = None
             x_max = None
 
         # Now, compute the volume of sand between the x-coordinates prestorm_dune_toe_x and x_swash_last for both prestorm
         # and post storm profiles.
-        prestorm_vol = beach_volume(x=df_zone.query("profile_type=='prestorm'").index.get_level_values('x'),
-                                    z=df_zone.query("profile_type=='prestorm'").z,
-                                    x_min=x_min,
-                                    x_max=x_max)
-        poststorm_vol = beach_volume(x=df_zone.query("profile_type=='poststorm'").index.get_level_values('x'),
-                                     z=df_zone.query("profile_type=='poststorm'").z,
-                                     x_min=x_min,
-                                     x_max=x_max)
-
-        df_vol_changes.loc[site_id, 'prestorm_{}_vol'.format(zone)] = prestorm_vol
-        df_vol_changes.loc[site_id, 'poststorm_{}_vol'.format(zone)] = poststorm_vol
-        df_vol_changes.loc[site_id, '{}_vol_change'.format(zone)] = prestorm_vol - poststorm_vol
+        prestorm_vol = beach_volume(
+            x=df_zone.query("profile_type=='prestorm'").index.get_level_values("x"),
+            z=df_zone.query("profile_type=='prestorm'").z,
+            x_min=x_min,
+            x_max=x_max,
+        )
+        poststorm_vol = beach_volume(
+            x=df_zone.query("profile_type=='poststorm'").index.get_level_values("x"),
+            z=df_zone.query("profile_type=='poststorm'").z,
+            x_min=x_min,
+            x_max=x_max,
+        )
+
+        df_vol_changes.loc[site_id, "prestorm_{}_vol".format(zone)] = prestorm_vol
+        df_vol_changes.loc[site_id, "poststorm_{}_vol".format(zone)] = poststorm_vol
+        df_vol_changes.loc[site_id, "{}_vol_change".format(zone)] = prestorm_vol - poststorm_vol
 
     return df_vol_changes
 
@@ -110,28 +118,28 @@ def storm_regime(df_observed_impacts):
     :param df_observed_impacts:
     :return:
     """
-    logger.info('Getting observed storm regimes')
-    df_observed_impacts.loc[df_observed_impacts.swash_vol_change < 3, 'storm_regime'] = 'swash'
-    df_observed_impacts.loc[df_observed_impacts.dune_face_vol_change > 3, 'storm_regime'] = 'collision'
+    logger.info("Getting observed storm regimes")
+    df_observed_impacts.loc[df_observed_impacts.swash_vol_change < 3, "storm_regime"] = "swash"
+    df_observed_impacts.loc[df_observed_impacts.dune_face_vol_change > 3, "storm_regime"] = "collision"
     return df_observed_impacts
 
 
-if __name__ == '__main__':
-    logger.info('Importing existing data')
-    data_folder = './data/interim'
-    df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0, 1, 2])
-    df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'), index_col=[0])
+if __name__ == "__main__":
+    logger.info("Importing existing data")
+    data_folder = "./data/interim"
+    df_profiles = pd.read_csv(os.path.join(data_folder, "profiles.csv"), index_col=[0, 1, 2])
+    df_profile_features = pd.read_csv(os.path.join(data_folder, "profile_features.csv"), index_col=[0])
 
-    logger.info('Creating new dataframe for observed impacts')
+    logger.info("Creating new dataframe for observed impacts")
     df_observed_impacts = pd.DataFrame(index=df_profile_features.index)
 
-    logger.info('Getting pre/post storm volumes')
-    df_swash_vol_changes = volume_change(df_profiles, df_profile_features, zone='swash')
-    df_dune_face_vol_changes = volume_change(df_profiles, df_profile_features, zone='dune_face')
+    logger.info("Getting pre/post storm volumes")
+    df_swash_vol_changes = volume_change(df_profiles, df_profile_features, zone="swash")
+    df_dune_face_vol_changes = volume_change(df_profiles, df_profile_features, zone="dune_face")
     df_observed_impacts = df_observed_impacts.join([df_swash_vol_changes, df_dune_face_vol_changes])
 
     # Classify regime based on volume changes
     df_observed_impacts = storm_regime(df_observed_impacts)
 
     # Save dataframe to csv
-    df_observed_impacts.to_csv(os.path.join(data_folder, 'impacts_observed.csv'))
+    df_observed_impacts.to_csv(os.path.join(data_folder, "impacts_observed.csv"))

src/analysis/runup_models.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 
+
 def sto06_individual(Hs0, Tp, beta):
 
     Lp = 9.8 * Tp ** 2 / 2 / np.pi
@@ -9,17 +10,18 @@ def sto06_individual(Hs0, Tp, beta):
     S_inc = 0.75 * beta * np.sqrt(Hs0 * Lp)
 
     # Dissipative conditions
-    if beta / (Hs0/Lp)**(0.5) <= 0.3:
+    if beta / (Hs0 / Lp) ** (0.5) <= 0.3:
         setup = 0.016 * (Hs0 * Lp) ** 0.5
         S_total = 0.046 * (Hs0 * Lp) ** 0.5
-        R2 = 0.043 *  (Hs0 * Lp) ** 0.5
+        R2 = 0.043 * (Hs0 * Lp) ** 0.5
     else:
         setup = 0.35 * beta * (Hs0 * Lp) ** 0.5
-        S_total = np.sqrt(S_inc ** 2 + S_ig **2)
+        S_total = np.sqrt(S_inc ** 2 + S_ig ** 2)
         R2 = 1.1 * (setup + S_total / 2)
 
     return R2, setup, S_total, S_inc, S_ig
 
+
 def sto06(df, Hs0_col, Tp_col, beta_col):
     """
     Vectorized version of Stockdon06 which can be used with dataframes
@@ -30,22 +32,23 @@ def sto06(df, Hs0_col, Tp_col, beta_col):
     :return:
     """
 
-    Lp  = 9.8 * df[Tp_col] ** 2 / 2 / np.pi
+    Lp = 9.8 * df[Tp_col] ** 2 / 2 / np.pi
 
     # General equation
-    S_ig = pd.to_numeric(0.06 * np.sqrt(df[Hs0_col] * Lp), errors='coerce')
-    S_inc = pd.to_numeric(0.75 * df[beta_col] * np.sqrt(df[Hs0_col] * Lp), errors='coerce')
-    setup = pd.to_numeric(0.35 * df[beta_col] * np.sqrt(df[Hs0_col] * Lp), errors='coerce')
+    S_ig = pd.to_numeric(0.06 * np.sqrt(df[Hs0_col] * Lp), errors="coerce")
+    S_inc = pd.to_numeric(0.75 * df[beta_col] * np.sqrt(df[Hs0_col] * Lp), errors="coerce")
+    setup = pd.to_numeric(0.35 * df[beta_col] * np.sqrt(df[Hs0_col] * Lp), errors="coerce")
     S_total = np.sqrt(S_inc ** 2 + S_ig ** 2)
     R2 = 1.1 * (setup + S_total / 2)
 
     # Dissipative conditions
-    dissipative = df[beta_col] / (df[Hs0_col] / Lp)**(0.5) <= 0.3
-    setup.loc[dissipative,:] = 0.016 * (df[Hs0_col] * Lp) ** (0.5)  # eqn 16
-    S_total.loc[dissipative,:] = 0.046 * (df[Hs0_col] * Lp) ** (0.5)  # eqn 17
-    R2.loc[dissipative,:] = 0.043 * (df[Hs0_col] * Lp) ** (0.5)  # eqn 18
+    dissipative = df[beta_col] / (df[Hs0_col] / Lp) ** (0.5) <= 0.3
+    setup.loc[dissipative, :] = 0.016 * (df[Hs0_col] * Lp) ** (0.5)  # eqn 16
+    S_total.loc[dissipative, :] = 0.046 * (df[Hs0_col] * Lp) ** (0.5)  # eqn 17
+    R2.loc[dissipative, :] = 0.043 * (df[Hs0_col] * Lp) ** (0.5)  # eqn 18
 
     return R2, setup, S_total, S_inc, S_ig
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     pass

src/data/profile_features.py

@@ -19,14 +19,14 @@ def shapes_from_shp(shp_file):
     shapes = []
     ids = []
     properties = []
-    for feat in fiona.open(shp_file, 'r'):
-        shapes.append(shape(feat['geometry']))
-        ids.append(feat['id'])
-        properties.append(feat['properties'])
+    for feat in fiona.open(shp_file, "r"):
+        shapes.append(shape(feat["geometry"]))
+        ids.append(feat["id"])
+        properties.append(feat["properties"])
     return shapes, ids, properties
 
 
-def convert_coord_systems(g1, in_coord_system='EPSG:4326', out_coord_system='EPSG:28356'):
+def convert_coord_systems(g1, in_coord_system="EPSG:4326", out_coord_system="EPSG:28356"):
     """
     Converts coordinates from one coordinates system to another. Needed because shapefiles are usually defined in
     lat/lon but should be converted to GDA to calculated distances.
@@ -38,7 +38,8 @@ def convert_coord_systems(g1, in_coord_system='EPSG:4326', out_coord_system='EPS
     project = partial(
         pyproj.transform,
         pyproj.Proj(init=in_coord_system),  # source coordinate system
-        pyproj.Proj(init=out_coord_system))  # destination coordinate system
+        pyproj.Proj(init=out_coord_system),
+    )  # destination coordinate system
 
     g2 = transform(project, g1)  # apply projection
     return g2
@@ -59,15 +60,19 @@ def distance_to_intersection(lat, lon, landward_orientation, beach, line_strings
     start_point = convert_coord_systems(start_point)
 
     distance = 1000  # m look up to 1000m for an intersection
-    landward_point = Point(start_point.coords.xy[0] + distance * np.cos(np.deg2rad(landward_orientation)),
-                           start_point.coords.xy[1] + distance * np.sin(np.deg2rad(landward_orientation)))
+    landward_point = Point(
+        start_point.coords.xy[0] + distance * np.cos(np.deg2rad(landward_orientation)),
+        start_point.coords.xy[1] + distance * np.sin(np.deg2rad(landward_orientation)),
+    )
     landward_line = LineString([start_point, landward_point])
-    seaward_point = Point(start_point.coords.xy[0] - distance * np.cos(np.deg2rad(landward_orientation)),
-                          start_point.coords.xy[1] - distance * np.sin(np.deg2rad(landward_orientation)))
+    seaward_point = Point(
+        start_point.coords.xy[0] - distance * np.cos(np.deg2rad(landward_orientation)),
+        start_point.coords.xy[1] - distance * np.sin(np.deg2rad(landward_orientation)),
+    )
     seaward_line = LineString([start_point, seaward_point])
 
     # Look at relevant line_strings which have the same beach property in order to reduce computation time
-    line_strings = [s for s, p in zip(line_strings, line_properties) if p['beach'] == beach]
+    line_strings = [s for s, p in zip(line_strings, line_properties) if p["beach"] == beach]
 
     # Check whether profile_line intersects with any lines in line_string. If intersection point is landwards,
     # consider this negative, otherwise seawards is positive.
@@ -99,7 +104,7 @@ def beach_profile_elevation(x_coord, df_profiles, profile_type, site_id):
 
     # Get profile
     df_profile = df_profiles.query('profile_type == "{}" and site_id =="{}"'.format(profile_type, site_id))
-    return np.interp(x_coord, df_profile.index.get_level_values('x'), df_profile['z'])
+    return np.interp(x_coord, df_profile.index.get_level_values("x"), df_profile["z"])
 
 
 def parse_profile_features(df_sites, df_profiles, dune_crest_shp, dune_toe_shp):
@@ -111,51 +116,38 @@ def parse_profile_features(df_sites, df_profiles, dune_crest_shp, dune_toe_shp):
     # Get site information. Base our profile features on each site
     df_profile_features = df_sites
 
-    features = {
-        'dune_crest':
-            {
-                'file': dune_crest_shp
-            },
-        'dune_toe':
-            {
-                'file': dune_toe_shp
-            },
-    }
+    features = {"dune_crest": {"file": dune_crest_shp}, "dune_toe": {"file": dune_toe_shp}}
 
     # Import our dune crest and toes
     for feat in features.keys():
-        shapes, _, properties = shapes_from_shp(features[feat]['file'])
+        shapes, _, properties = shapes_from_shp(features[feat]["file"])
         shapes = [convert_coord_systems(x) for x in shapes]
 
         # Figure out the x coordinates of our crest and toes, by looking at where our beach sections intersect our
         # shape files.
-        col_name = '{}_x'.format(feat)
-        df_profile_features[col_name] = df_profile_features['profile_x_lat_lon'] + \
-                                        df_profile_features.apply(lambda row:
-                                                                  distance_to_intersection(
-                                                                      row['lat'], row['lon'], row['orientation'],
-                                                                      row['beach'], shapes, properties),
-                                                                  axis=1)
+        col_name = "{}_x".format(feat)
+        df_profile_features[col_name] = df_profile_features["profile_x_lat_lon"] + df_profile_features.apply(
+            lambda row: distance_to_intersection(
+                row["lat"], row["lon"], row["orientation"], row["beach"], shapes, properties
+            ),
+            axis=1,
+        )
         # Get the elevations of the crest and toe
-        col_name = '{}_z'.format(feat)
-        df_profile_features[col_name] = df_profile_features.apply(lambda row:
-                                                                  beach_profile_elevation(
-                                                                      row['{}_x'.format(feat)],
-                                                                      df_profiles,
-                                                                      'prestorm',
-                                                                      row.name),
-                                                                  axis=1)
-
-    df_profile_features = df_profile_features.drop(columns=['beach', 'lat', 'lon', 'orientation'])
+        col_name = "{}_z".format(feat)
+        df_profile_features[col_name] = df_profile_features.apply(
+            lambda row: beach_profile_elevation(row["{}_x".format(feat)], df_profiles, "prestorm", row.name), axis=1
+        )
+
+    df_profile_features = df_profile_features.drop(columns=["beach", "lat", "lon", "orientation"])
     return df_profile_features
 
 
-if __name__ == '__main__':
-    data_folder = './data/interim'
-    df_sites = pd.read_csv(os.path.join(data_folder, 'sites.csv'), index_col=[0])
-    df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0, 1, 2])
+if __name__ == "__main__":
+    data_folder = "./data/interim"
+    df_sites = pd.read_csv(os.path.join(data_folder, "sites.csv"), index_col=[0])
+    df_profiles = pd.read_csv(os.path.join(data_folder, "profiles.csv"), index_col=[0, 1, 2])
 
-    dune_crest_shp = './data/raw/profile_features/dune_crests.shp'
-    dune_toe_shp = './data/raw/profile_features/dune_toes.shp'
+    dune_crest_shp = "./data/raw/profile_features/dune_crests.shp"
+    dune_toe_shp = "./data/raw/profile_features/dune_toes.shp"
     df_profile_features = parse_profile_features(df_sites, df_profiles, dune_crest_shp, dune_toe_shp)
-    df_profile_features.to_csv('./data/interim/profile_features.csv')
+    df_profile_features.to_csv("./data/interim/profile_features.csv")