Update slope function to accept top x coordinate

Needed to ensure correctly mean slope is calculated. If only inputting dune toe z coordinates, sometimes the function can chose the wrong x coordinate if there are multiple crossings of the z coordinate.

src/analysis/forecast_twl.py

@@ -23,6 +23,7 @@ def forecast_twl(
     runup_function,
     n_processes=MULTIPROCESS_THREADS,
     slope="foreshore",
+    profile_type='prestorm'
 ):
     # Use df_waves as a base
     df_twl = df_waves.copy()
@@ -45,12 +46,14 @@ def forecast_twl(
         df_twl["beta"] = pd.concat(results)
 
     elif slope == "mean":
-        df_temp = df_twl.join(df_profile_features.query("profile_type=='prestorm'").reset_index(level='profile_type')
+        df_temp = df_twl.join(df_profile_features.query("profile_type=='{}'".format(profile_type)).reset_index(
+            level='profile_type')
                               , 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]
+                mean_slope_for_site_id, [(site_id, df_temp, df_profiles, "dune_toe_z", "dune_toe_x", "mhw") for
+                                         site_id in site_ids]
             )
         df_twl["beta"] = pd.concat(results)
 
@@ -71,7 +74,8 @@ def forecast_twl(
     return df_twl
 
 
-def mean_slope_for_site_id(site_id, df_twl, df_profiles, top_elevation_col, btm_elevation_col):
+def mean_slope_for_site_id(site_id, df_twl, df_profiles, top_elevation_col, top_x_col, btm_elevation_col,
+                           profile_type='prestorm'):
     """
     Calculates the foreshore slope values a given site_id. Returns a series (with same indicies as df_twl) of
     foreshore slopes. This function is used to parallelize getting foreshore slopes as it is computationally
@@ -83,7 +87,7 @@ 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 = df_profiles.query("site_id =='{}' and profile_type == '{}'".format(site_id, profile_type))
     profile_x = profile.index.get_level_values("x").tolist()
     profile_z = profile.z.tolist()
 
@@ -96,6 +100,7 @@ def mean_slope_for_site_id(site_id, df_twl, df_profiles, top_elevation_col, btm_
             top_elevation=row[top_elevation_col],
             btm_elevation=row[btm_elevation_col],
             method="end_points",
+            top_x= row[top_x_col]
         ),
         axis=1,
     )
@@ -191,7 +196,7 @@ 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", top_x=None, btm_x=None):
     """
     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
@@ -199,6 +204,9 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
     :param top_elevation: Top elevation of where to take the slope
     :param btm_elevation: Bottom elevation of where to take the slope
     :param method: Method used to calculate slope (end_points or least_squares)
+    :param top_x: x-coordinate of the top end point. May be needed, as there may be multiple crossings of the
+    top_elevation.
+    :param btm_x: x-coordinate of the bottom end point
     :return:
     """
 
@@ -208,7 +216,18 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
 
     end_points = {"top": {"z": top_elevation}, "btm": {"z": btm_elevation}}
 
+
+
     for end_type in end_points.keys():
+
+        # Add x coordinates if they are specified
+        if top_x and end_type == 'top':
+            end_points['top']['x'] = top_x
+            continue
+        if btm_x and end_type == 'top':
+            end_points['btm']['x'] = btm_x
+            continue
+
         elevation = end_points[end_type]["z"]
         intersection_x = crossings(profile_x, profile_z, elevation)
 
@@ -285,8 +304,10 @@ def crossings(profile_x, profile_z, constant_z):
 @click.option("--profile-features-csv", required=True, help="")
 @click.option("--runup-function", required=True, help="", type=click.Choice(["sto06"]))
 @click.option("--slope", required=True, help="", type=click.Choice(["foreshore", "mean"]))
+@click.option("--profile-type", required=True, help="", type=click.Choice(["prestorm", "poststorm"]))
 @click.option("--output-file", required=True, help="")
-def create_twl_forecast(waves_csv, tides_csv, profiles_csv, profile_features_csv, runup_function, slope, output_file):
+def create_twl_forecast(waves_csv, tides_csv, profiles_csv, profile_features_csv, runup_function, slope, 
+                        profile_type,output_file):
     logger.info("Creating forecast of total water levels")
     logger.info("Importing data")
     df_waves = pd.read_csv(waves_csv, index_col=[0, 1])
@@ -295,15 +316,16 @@ def create_twl_forecast(waves_csv, tides_csv, profiles_csv, profile_features_csv
     df_profile_features = pd.read_csv(profile_features_csv, index_col=[0,1])
 
     logger.info("Forecasting TWL")
-    df_twl_foreshore_slope_sto06 = forecast_twl(
+    df_twl = forecast_twl(
         df_tides,
         df_profiles,
         df_waves,
         df_profile_features,
         runup_function=getattr(runup_models, runup_function),
         slope=slope,
+        profile_type=profile_type
     )
 
-    df_twl_foreshore_slope_sto06.to_csv(output_file)
+    df_twl.to_csv(output_file)
     logger.info("Saved to %s", output_file)
     logger.info("Done!")