old_contents: # Predict the background radiation level at a certain location
  
  import numpy as np
  import Image
  import sys
  from scipy import interpolate
  import geopy.distance
  import os
  
  # Predicts the (electon, proton) count per frame per second under NORMAL conditions in the given location
  def predict_background((lookup_lat, lookup_lng)):
      predicted_counts = {}
      for t in ["beta", "proton"]:
          counts = []
          data = open(os.path.dirname(os.path.abspath(__file__)) + "/" + t + "-calgorithm-counts.csv").readlines()
  
          for datum in data:
          	datum = datum[:-1]  # Trim off newline char
          	if datum:
          		fields = datum.split(",")
          		timestamp, latitude, longitude, num_frames, beta = fields
          		latitude, longitude, beta, num_frames = float(latitude), float(longitude), int(beta), float(int(num_frames))
                  counts.append((latitude, longitude, beta/num_frames))
  
          counts_sorted_distance = sorted(counts, key=lambda count:geopy.distance.great_circle((lookup_lat, lookup_lng), (count[0], count[1])))
          count = np.mean([c[2] for c in counts_sorted_distance[:3]])
          # Convert this number into something more 'scientific'
          count /= 2.0 # Single detector
          count /= 0.03 # Per second
          predicted_counts[t] = count
  
      return(predicted_counts["beta"], predicted_counts["proton"])
  
new_contents: # Predict the background radiation level at a certain location
  # Betabetabetabetabetabetabetabetabetabetabetabetabetabeta, so don't rely on this just yet :)
  
  import numpy as np
  import Image
  import sys
  from scipy import interpolate
  import geopy.distance
  import os
  
  # Predicts the (electon, proton) count per frame per second under NORMAL conditions in the given location
  def predict_background((lookup_lat, lookup_lng)):
      predicted_counts = {}
      for t in ["beta", "proton"]:
          counts = []
          data = open(os.path.dirname(os.path.abspath(__file__)) + "/" + t + "-calgorithm-counts.csv").readlines()
  
          for datum in data:
          	datum = datum[:-1]  # Trim off newline char
          	if datum:
          		fields = datum.split(",")
          		timestamp, latitude, longitude, num_frames, beta = fields
          		latitude, longitude, beta, num_frames = float(latitude), float(longitude), int(beta), float(int(num_frames))
                  counts.append((latitude, longitude, beta/num_frames))
  
          counts_sorted_distance = sorted(counts, key=lambda count:geopy.distance.great_circle((lookup_lat, lookup_lng), (count[0], count[1])))
          count = np.mean([c[2] for c in counts_sorted_distance[:3]])
          # Convert this number into something more 'scientific'
          count /= 2.0 # Single detector
          count /= 0.03 # Per second
          predicted_counts[t] = count
  
      return(predicted_counts["beta"], predicted_counts["proton"])
  
subject: Add betaness warning