diff --git a/las_outputs.py b/las_outputs.py
index edfd6db..3260657 100644
--- a/las_outputs.py
+++ b/las_outputs.py
@@ -12,6 +12,7 @@
 ########################### IMPORTS ###########################################
 import os
 import io
+import re
 import subprocess
 import pandas as pd
 import numpy as np
@@ -147,6 +148,42 @@ def plot_profiles(profile_name, survey_date, csv_output_dir, graph_loc, ch_limit
     plt.close()
 
 
+def calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir):
+    csv_prof = profile_name + '.csv'
+    beach = re.search('.*(?=_\d)', profile_name).group()
+    profiles = pd.read_csv(os.path.join(csv_output_dir, csv_prof))
+
+    # Remove metadata, and extract profile coordinates
+    profiles = profiles.loc[:, 'Chainage':].set_index('Chainage')
+
+    # Find landward limit of profile (behind beach)
+    ch_min = ch_limits.loc[profile_name, 'Landward Limit']
+
+    # Open volume spreadsheet
+    csv_vol = os.path.join(volume_output_dir, 'volumes.csv')
+    try:
+        volumes = pd.read_csv(csv_vol, index_col=0)
+    except FileNotFoundError:
+        volumes = pd.DataFrame()
+
+    # Format dates
+    date_str = str(survey_date)
+    date = '{}-{}-{}'.format(date_str[:4], date_str[4:6], date_str[6:])
+
+    for current_date in profiles.columns:
+        # Get Nielsen erosion volumes
+        chainage = profiles.loc[:, current_date].dropna().index
+        elevation = profiles.loc[:, current_date].dropna().values
+        volume = neilson_volumes.volume_available(chainage, elevation, ch_min)
+
+        # Update spreadsheet
+        volumes.loc[profile_name, date] = volume
+
+    # Save updated volumes spreadsheet
+    volumes = volumes.sort_index()
+    volumes.to_csv(csv_vol)
+
+
 input_file = 'Parameter Files/las-manipulation-survey-2.xlsx'
 params_file=pd.read_excel(input_file, sheet_name="PARAMS")
 
@@ -166,7 +203,7 @@ for i, row in params_file.iterrows():
     profile_limit_file = row['PROFILE LIMIT FILE']
     csv_output_dir = row['CSV OUTPUT FOLDER']
     graph_loc = row['PNG OUTPUT FOLDER']
-    volume_output = row['CSV VOLUMES FOLDER']
+    volume_output_dir = row['CSV VOLUMES FOLDER']
     tmp_dir = row['TMP FOLDER']
 
     # Get base name of input las
@@ -203,18 +240,18 @@ for i, row in params_file.iterrows():
         beach,
         args=['-parse', 'sxyz', '-keep_class', '2'],
         verbose=False)
+
+    # Update survey profiles
     update_survey_output(df, csv_output_dir)
 
     # Get landward limit of surveys
     ch_limits = pd.read_excel(profile_limit_file, index_col='Profile')
 
-    # Plot profiles of current beach
+    # Plot profiles, and save sand volumes for current beach
     profile_names = df['Profile'].unique()
     for profile_name in profile_names:
         plot_profiles(profile_name, survey_date, csv_output_dir, graph_loc, ch_limits)
+        calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volume_output_dir)
 
     # Remove temprary files
     remove_temp_files(tmp_dir)
-
-print("doing the volume analysis")
-# test=profile_plots_volume(csv_output_dir, profile_limit_file, volume_output, graph_loc)