Correctly handle profiles that are outside survey area

las_outputs.py

@@ -83,8 +83,12 @@ def plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits, delta_vol,
     # Set figure dimensions based on beach size
     vertical_exag = 8
     m_per_inch = 8
+    try:
         fig_h = profiles.dropna().values.max() / m_per_inch * vertical_exag
         fig_w = (profiles.index.max() - ch_min) / m_per_inch
+    except ValueError:
+        fig_h = 5
+        fig_w = 10
 
     if scale_figures:
         fig, ax = plt.subplots(figsize=(fig_w, fig_h))
@@ -160,7 +164,10 @@ def calculate_volumes(profile_name, survey_date, csv_output_dir, ch_limits, volu
         # Get Nielsen erosion volumes
         chainage = profiles.loc[:, current_date].dropna().index
         elevation = profiles.loc[:, current_date].dropna().values
+        try:
             volume = nielsen_volumes.volume_available(chainage, elevation, ch_min)
+        except ValueError:
+            volume = np.nan        
 
         # Update spreadsheet
         volumes.loc[profile_name, 'Volume_' + survey_date] = volume

survey_tools.py

@@ -117,6 +117,12 @@ def extract_pts(las_in, cp_in, survey_date, beach, args=None, verbose=True):
     # Load result into pandas dataframe
     df = pd.read_csv(io.BytesIO(las_data))
     
+    # Create empty dataframe if no control points intersect point cloud
+    if (df.iloc[:, 0] == '-').all():
+        df = pd.read_csv(cp_in)
+        df['diff'] = '-'
+        df['lidar_z'] = '-'
+
     # Tidy up dataframe
     df = df.drop(columns=['diff'])
     df['lidar_z'] = pd.to_numeric(df['lidar_z'], errors='coerce')