old_contents: __author__ = 'VestalNetSciHigh'
  
  """ Read in data (and array of distance metrics) and calculate various stats """
  
  import networkx as nx
  import time
  from numpy import loadtxt
  import settings
  
  start_time = time.time()
  output_string = ""
  
  # load output from parseCSV
  distances = loadtxt(settings.OUTPUT+"\\real_distances.dat")
  
  # sort all distances
  ordered_distances = []
  for i in xrange(distances.__len__()):
      for id in distances[i]:
          ordered_distances.append(id)
  ordered_distances.sort()
  output_string += "Total distances: " + str(ordered_distances.__len__()) + "\n"
  
  # compute mean distance
  mean_distance = sum(ordered_distances) / ordered_distances.__len__()
  output_string += "Mean distance: " + str(mean_distance) + "\n"
  
  # compute root mean square
  sum_squares = 0
  for dist in ordered_distances:
      sum_squares += dist * dist
  root_mean_square = pow(sum_squares / float(ordered_distances.__len__()), 0.5)
  output_string += "Root mean square: " + str(root_mean_square) + "\n"
  
  # minimum distance (most dissimilar)
  most_dissimilar = ordered_distances[0]
  output_string += "Most dissimilar (min): " + str(most_dissimilar) + "\n"
  settings.output(str(most_dissimilar), "most_dissimilar", ext=".dat", mode="write")
  
  # compute median distance
  median_distance = 0
  index_float = ordered_distances.__len__() / 2.0
  index_whole = ordered_distances.__len__() / 2  # intentional truncation
  if index_float - index_whole < 0.001:
      median_distance = (ordered_distances[index_whole] + ordered_distances[index_whole + 1]) / 2.0
  else:
      median_distance = ordered_distances[index_whole]
  output_string += "Median distance: " + str(median_distance) + "\n"
  
  # Create a networkx graph
  G = nx.from_numpy_matrix(distances)
  # print G.edges()
  
  total_seconds = time.time() - start_time
  output_string += "Finished 'calculate_stats.py' in " + str(total_seconds) + " seconds!"
  settings.output(output_string, "stats_real_valued", mode="write")
  print "Finished 'calculate_stats.py' in " + str(total_seconds) + " seconds!"
new_contents: __author__ = 'VestalNetSciHigh'
  
  """ Read in data (and array of distance metrics) and calculate various stats """
  
  import networkx as nx
  import time
  from numpy import loadtxt
  import settings
  
  start_time = time.time()
  output_string = ""
  
  # load output from parseCSV
  distances = loadtxt(settings.OUTPUT+"\\real_distances.dat")
  
  # sort all distances
  ordered_distances = []
  for i in xrange(distances.__len__()):
      for id in distances[i]:
          ordered_distances.append(id)
  ordered_distances.sort()
  output_string += "Total distances: " + str(ordered_distances.__len__()) + "\n"
  
  # compute mean distance
  mean_distance = sum(ordered_distances) / ordered_distances.__len__()
  output_string += "Mean distance: " + str(mean_distance) + "\n"
  
  # compute root mean square
  sum_squares = 0
  for dist in ordered_distances:
      sum_squares += dist * dist
  root_mean_square = pow(sum_squares / float(ordered_distances.__len__()), 0.5)
  output_string += "Root mean square: " + str(root_mean_square) + "\n"
  
  # minimum distance (most dissimilar)
  most_dissimilar = ordered_distances[0]
  output_string += "Most dissimilar (min): " + str(most_dissimilar) + "\n"
  settings.output(str(most_dissimilar), "most_dissimilar", ext=".dat", mode="write")
  
  # compute median distance
  median_distance = 0
  index_float = ordered_distances.__len__() / 2.0
  index_whole = ordered_distances.__len__() / 2  # intentional truncation
  if index_float - index_whole < 0.001:
      median_distance = (ordered_distances[index_whole] + ordered_distances[index_whole + 1]) / 2.0
  else:
      median_distance = ordered_distances[index_whole]
  output_string += "Median distance: " + str(median_distance) + "\n"
  
  total_seconds = time.time() - start_time
  output_string += "Finished 'calculate_stats.py' in " + str(total_seconds) + " seconds!"
  settings.output(output_string, "stats_real_valued", mode="write")
  print "Finished 'calculate_stats.py' in " + str(total_seconds) + " seconds!"
subject: Delete obsolete code