diff --git a/las_outputs.py b/las_outputs.py
index 3260657..04dfce1 100644
--- a/las_outputs.py
+++ b/las_outputs.py
@@ -13,102 +13,19 @@
 import os
 import io
 import re
+import math
+import datetime
 import subprocess
-import pandas as pd
 import numpy as np
-import neilson_volumes
+import pandas as pd
+from cycler import cycler
 import matplotlib.pyplot as plt
 from matplotlib.ticker import MultipleLocator
-import datetime
-import xlsxwriter
-import math
-from cycler import cycler
 
+import neilson_volumes
 from survey_tools import call_lastools, extract_pts, update_survey_output
 
 
-def profile_plots_volume(csv_loc, LL_xlsx, output_xlsx, graph_location):
-    #get a list of all csvs which will each be analysed
-    file_list=[]
-    for file in os.listdir(csv_loc):
-        if file.endswith(".csv"):
-            file_list.append(os.path.join(csv_loc, file))
-
-    #now read the LL file
-    LL_limit_file=pd.read_excel(LL_xlsx, 'profile_locations')
-    LL_info={}
-    for i in range(0, len(LL_limit_file)):
-        #make a dictionary that alllows you to search the LL
-        prof="%s_%s" % (LL_limit_file['Profile'][i].split(" ")[0], LL_limit_file['Profile'][i].split(" ")[-1])
-
-        LL_info[prof]=LL_limit_file['Landward Limit'][i]
-
-    all_dates=[]
-    results_volume={}
-    for file in file_list:
-        #read the profile data - this should have all dates
-        profile_data=CC_split_profile(file)
-        profile=profile_data['info']['Profile']
-
-        #plot all of the profiles
-        print(profile)
-        plot_profiles(profile_data, profile, graph_location,LL_info[profile])
-
-        results_volume[profile]={}
-
-        #nowgo through each date and do a neilson volume calculations
-        for date in profile_data.keys():
-            if date!='info':
-                if date not in all_dates:
-                    all_dates.append(date)
-
-                chainage=profile_data[date]['Chainage']
-                elevation=[0 if pd.isnull(profile_data[date]['Elevation'][i]) else profile_data[date]['Elevation'][i] for i in range(0, len(profile_data[date]['Elevation']))]
-                LL_limit=LL_info[profile]
-                #do a neilson calculation to get the ZSA volume
-                if len(elevation)>2:
-                    #if there aren't enough available points don't do it
-                    volume=neilson_volumes.volume_available(chainage, elevation, LL_limit)
-                    if volume<0:
-                        volume=0
-                        print('%s %s has a negative volume available' % (profile, date))
-                else:
-                    volume=0
-
-                results_volume[profile][date]=volume
-
-    #write an excel sheet which summarises the data
-    workbook = xlsxwriter.Workbook(output_xlsx)
-    worksheet=workbook.add_worksheet('Volumes')
-
-    row=0
-    col=0
-
-    worksheet.write(row, col, 'Profile')
-    for date in all_dates:
-        col=col+1
-        worksheet.write(row, col, date)
-
-    col=0
-    row=1
-
-    for prof in results_volume.keys():
-
-        worksheet.write(row, col, prof)
-
-        for date in all_dates:
-            col=col+1
-            try:
-                vol=results_volume[prof][date]
-            except KeyError:
-                print("error with profile %s on %s" % (prof, date))
-                vol=None
-            worksheet.write(row, col, vol)
-        col=0
-        row=row+1
-    return results_volume
-
-
 def remove_temp_files(directory):
     for f in os.listdir(directory):
         os.unlink(os.path.join(directory, f))