From 1e456756117127a05b6e9a68aee7fb6e3689eb7d Mon Sep 17 00:00:00 2001
From: Chris Leaman <ckleaman@gmail.com>
Date: Mon, 4 Mar 2019 15:46:58 +1100
Subject: [PATCH] Optimize calculation of profile volume difference

---
 src/analysis/observed_storm_impacts.py | 65 ++++++++++++++++++--------
 1 file changed, 46 insertions(+), 19 deletions(-)

diff --git a/src/analysis/observed_storm_impacts.py b/src/analysis/observed_storm_impacts.py
index b0653d5..78b9d8b 100644
--- a/src/analysis/observed_storm_impacts.py
+++ b/src/analysis/observed_storm_impacts.py
@@ -4,6 +4,7 @@ import pandas as pd
 from scipy.integrate import simps
 
 from logs import setup_logging
+from utils import crossings
 
 logger = setup_logging()
 
@@ -41,14 +42,9 @@ def volume_change(df_profiles, df_profile_features, zone):
             "Calculating change in beach volume at {} in {} zone".format(site_id, zone)
         )
 
-        # TODO Change this query to an index
-        query = "site_id=='{}'&profile_type=='prestorm'".format(site_id)
-        prestorm_dune_toe_x = df_profile_features.query(query).dune_toe_x.tolist()
-        prestorm_dune_crest_x = df_profile_features.query(query).dune_crest_x.tolist()
-
-        # We may not have a dune toe or crest defined, or there may be multiple defined.
-        prestorm_dune_crest_x = return_first_or_nan(prestorm_dune_crest_x)
-        prestorm_dune_toe_x = return_first_or_nan(prestorm_dune_toe_x)
+        prestorm_row = df_profile_features.loc[(site_id, "prestorm")]
+        prestorm_dune_toe_x = prestorm_row.dune_toe_x
+        prestorm_dune_crest_x = prestorm_row.dune_crest_x
 
         # If no dune toe has been defined, Dlow = Dhigh. Refer to Sallenger (2000).
         if np.isnan(prestorm_dune_toe_x):
@@ -56,8 +52,7 @@ def volume_change(df_profiles, df_profile_features, zone):
 
         # If no prestorm and poststorm profiles, skip site and continue
         profile_lengths = [
-            len(df_site.query("profile_type == '{}'".format(x)))
-            for x in ["prestorm", "poststorm"]
+            len(df_site.xs(x, level="profile_type")) for x in ["prestorm", "poststorm"]
         ]
         if any([length == 0 for length in profile_lengths]):
             continue
@@ -113,20 +108,52 @@ def volume_change(df_profiles, df_profile_features, zone):
             x_max=x_max,
         )
 
-        # Volume change needs to be calculated including a tolerance for LIDAR accuracy. If difference between
-        # profiles is less than 20 cm, consider them as zero difference.
-        prestorm_z = (
-            df_zone.query("profile_type=='prestorm'").reset_index("profile_type").z
+        # Identify the x location where our pre and post storm profiles start to differ. This is so changes no due to
+        # the storm are not included when calculating volume.
+        df_prestorm = (
+            df_site.xs("prestorm", level="profile_type").z.rename("z_pre").to_frame()
         )
-        poststorm_z = (
-            df_zone.query("profile_type=='poststorm'").reset_index("profile_type").z
+        df_poststorm = (
+            df_site.xs("poststorm", level="profile_type").z.rename("z_post").to_frame()
         )
-        diff_z = prestorm_z - poststorm_z
-        diff_z[abs(diff_z) < 0.2] = 0
+        df_diff = df_prestorm.merge(df_poststorm, on=["site_id", "x"])
+        df_diff["z_diff"] = df_diff.z_pre - df_diff.z_post
+
+        # Find all locations where the difference in pre and post storm is zero. Take the most seaward location as the
+        # x location where our profiles are the same.
+        x_crossings = crossings(df_diff.index.get_level_values("x"), df_diff.z_diff, 0)
+
+        if len(x_crossings) != 0:
+            x_change_point = x_crossings[-1]
+        else:
+            x_change_point = np.nan
+
+        # # For debugging
+        # import matplotlib.pyplot as plt
+        # f,(ax1,ax2) = plt.subplots(2,1,sharex=True)
+        # ax1.plot(df_prestorm.index.get_level_values('x'), df_prestorm.z_pre,label='prestorm')
+        # ax1.plot(df_poststorm.index.get_level_values('x'), df_poststorm.z_post,label='poststorm')
+        # ax1.axvline(x_crossings[-1], color='red', linestyle='--', linewidth=0.5, label='Change point')
+        # ax1.legend()
+        # ax1.set_title(site_id)
+        # ax1.set_ylabel('elevation (m AHD)')
+        # ax2.plot(df_diff.index.get_level_values('x'), df_diff.z_diff)
+        # ax2.set_xlabel('x coordinate (m)')
+        # ax2.set_ylabel('elevation diff (m)')
+        # ax2.axvline(x_crossings[-1],color='red',linestyle='--',linewidth=0.5)
+        # plt.show()
+
         diff_vol = beach_volume(
-            x=diff_z.index.get_level_values("x"), z=diff_z, x_min=x_min, x_max=x_max
+            x=df_diff.index.get_level_values("x"),
+            z=df_diff.z_diff,
+            x_min=np.nanmax([x_min, x_change_point]),
+            x_max=np.nanmax([x_max, x_change_point]),
         )
 
+        # Here, if cannot calculate the difference volume, assume no volume change
+        if np.isnan(diff_vol):
+            diff_vol = 0
+
         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)] = diff_vol