diff --git a/outputs_2017088_Survey2.py b/outputs_2017088_Survey2.py
index 812b3c5..879973e 100644
--- a/outputs_2017088_Survey2.py
+++ b/outputs_2017088_Survey2.py
@@ -23,7 +23,7 @@ import xlsxwriter
 import math
 from cycler import cycler
 
-from survey_tools import call_lastools
+from survey_tools import call_lastools, extract_pts, update_survey_output
 
 ###############################################################################
 ########################## FIXED INPUTS #######################################
@@ -99,91 +99,6 @@ def make_raster(las, output, lastools_loc, keep_only_ground=False, step=0.2):
         return None
 
 
-def extract_pts(las_in, cp_in, survey_date, beach, keep_only_ground=True):
-    """Extract elevations from a las surface based on x and y coordinates.
-
-    Requires lastools in system path.
-
-    Args:
-        las_in:            input point cloud (las)
-        cp_in:             point coordinates with columns: id, x, y, z (csv)
-        survey_date:       survey date string, e.g. '19700101'
-        beach:             beach name
-        keep_only_ground:  only keep points classified as 'ground' (boolean)
-
-    Returns:
-        Dataframe containing input coordinates with extracted elevations
-    """
-
-    cmd = ['lascontrol', '-i', las_in, '-cp', cp_in, '-parse', 'sxyz']
-
-    if keep_only_ground == True:
-        cmd += ['-keep_class', '2']
-
-    # Call lastools
-    process = subprocess.Popen(
-        cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-    stdout, stderr = process.communicate()
-    errcode = process.returncode
-
-    # Handle errors, if detected
-    if errcode != 0:
-        print("Error. lascontrol failed on {}".format(
-            os.path.basename(las_in)))
-        print(stderr.decode())
-
-    # Load result into pandas dataframe
-    df = pd.read_csv(io.BytesIO(stdout))
-
-    # Tidy up dataframe
-    df = df.drop(columns=['diff'])
-    df['lidar_z'] = pd.to_numeric(df['lidar_z'], errors='coerce')
-    df['Beach'] = beach
-    df = df[[
-        'Beach', 'ProfileNum', 'Easting', 'Northing', 'Chainage', 'lidar_z'
-    ]]
-
-    # Rename columns
-    new_names = {
-        'ProfileNum': 'Profile',
-        'lidar_z': 'Elevation_{}'.format(survey_date),
-    }
-    df = df.rename(columns=new_names)
-
-    return df
-
-
-def update_survey_output(df, output_dir):
-    """Update survey profile output csv files with current survey.
-
-    Args:
-        df:         dataframe containing current survey elevations
-        output_dir: directory where csv files are saved
-
-    Returns:
-        None
-    """
-    # Merge current survey with existing data
-    profiles = df['Profile'].unique()
-    for profile in profiles:
-        csv_name = os.path.join(output_csv_dir, profile + '.csv')
-
-        # Extract survey data for current profile
-        current_profile = df[df['Profile'] == profile]
-        try:
-            # Load existing results
-            master = pd.read_csv(csv_name)
-        except FileNotFoundError:
-            master = current_profile.copy()
-
-        # Add (or update) current survey
-        current_survey_col = df.columns[-1]
-        master[current_survey_col] = current_profile[current_survey_col]
-
-        # Export updated results
-        master.to_csv(csv_name)
-
-
 def plot_profiles(profile_info, profile, output_loc, LL_limit):
     #plot the profile.  expects output from CC_split_profile
 
@@ -377,13 +292,11 @@ for i in range(0, len(params_file)): #0, len(params_file)
     output_poly_dir=params_file['SHP RASTER FOLDER'][i]
     output_tif_dir=params_file['TIF OUTPUT FOLDER'][i]
     cp_csv=params_file['INPUT CSV'][i]
-    # tmp_csv = params_file['TMP CSV'][i]
-    LL_file=params_file['LL FILE'][i]
-    # csv_loc=params_file['OUT CSV LOC'][i]
-    graph_loc = params_file['GRAPH LOC'][i]
-    volume_output=params_file['VOLUME OUTPUT'][i]
-    tmp_dir=params_file['TEMP DIR'][i]
-    int_dir=params_file['INTERIM DIR'][i]
+    profile_limit_file=params_file['PROFILE LIMIT FILE'][i]
+    csv_output_dir=params_file['CSV OUTPUT FOLDER'][i]
+    graph_loc = params_file['PNG OUTPUT FOLDER'][i]
+    volume_output=params_file['CSV VOLUMES FOLDER'][i]
+    tmp_dir=params_file['TMP FOLDER'][i]
 
     # Get base name of input las
     las_basename = os.path.splitext(os.path.basename(original_las))[0]
@@ -410,11 +323,22 @@ for i in range(0, len(params_file)): #0, len(params_file)
     # las_boundary(heatmap_las, output_poly_name, output_poly_dir, path_2_lastools, zone_MGA)
 
     #make a raster
-    make_raster(heatmap_las, output_raster, path_2_lastools, keep_only_ground=True)
+    # make_raster(heatmap_las, output_raster, path_2_lastools, keep_only_ground=True)
+    tif_name = os.path.join(output_tif_dir, las_basename + '.tif')
+    call_lastools('blast2dem', input=input_las, output=tif_name,
+                  args=['-step', 0.2], verbose=False)
 
     #extract the points and get volumes
-    df = extract_pts(final_las, cp_csv, survey_date, beach, keep_only_ground=True)
-    update_survey_output(df, csv_loc)
+    # df = extract_pts(final_las, cp_csv, survey_date, beach, keep_only_ground=True)
+    df = extract_pts(
+        las_data,
+        cp_csv,
+        survey_date,
+        beach,
+        args=['-parse', 'sxyz', '-keep_class', '2'],
+        verbose=False)
+    update_survey_output(df, csv_output_dir)
+
 
     #colourise the point cloud