old_contents: import glob
  from avidaspatial import *
  import random
  import scipy.stats
  
  dir_list = glob.glob("env_*_1*")
  
  data = ["env, ud, task, x, y, div, rank"]
  
  for dir_name in dir_list:
      env_num = dir_name.split("_")[-2]
      task_loc_file = open(dir_name + "/task_locs.csv")
      task_locs = task_loc_file.readlines()
      task_loc_file.close()
  
      task_dict = {}
      for line in task_locs:
          sline = line.split(",")
          task_dict[int(sline[3])] = [int(i) for i in sline[:3]]
  
      grids = glob.glob(dir_name+"/data/grid_task.*.dat")
      envs = load_grid_data(grids)
  
      for j, env in enumerate(envs):
          ud = int(grids[j].split(".")[-2])
          env = agg_grid(env, mode)
          div_map = diversity_map(env, neighbor_func=get_25_neighbors)
          task = task_dict[ud+1]
          print(task)
          div = div_map[task[2]][task[1]]
          rank = scipy.stats.percentileofscore(flatten_array(div_map), div)
  
          data.append(",".join([str(i) for i in [env_num, ud, task[0], taxk[1], task[2], div, rank]]))
  
  outfile = open("diversity_ranks.csv")
  outfile.write("\n".join(data))
  outfile.close()
  
new_contents: import glob
  from avidaspatial import *
  import random
  import scipy.stats
  
  dir_list = glob.glob("env_*_1*")
  
  data = ["env, ud, task, x, y, div, rank"]
  
  for dir_name in dir_list:
      env_num = dir_name.split("_")[-2]
      task_loc_file = open(dir_name + "/task_locs.csv")
      task_locs = task_loc_file.readlines()
      task_loc_file.close()
  
      task_dict = {}
      for line in task_locs:
          sline = line.split(",")
          task_dict[int(sline[3])] = [int(i) for i in sline[:3]]
  
      grids = glob.glob(dir_name+"/data/grid_task.*.dat")
      envs = load_grid_data(grids)
  
      for j in len(envs):
          ud = int(grids[j].split(".")[-2])
          env = load_grid_data(grids[j])
          env = agg_grid(env, mode)
          div_map = diversity_map(env, neighbor_func=get_25_neighbors)
          task = task_dict[ud+1]
          print(task)
          div = div_map[task[2]][task[1]]
          rank = scipy.stats.percentileofscore(flatten_array(div_map), div)
  
          data.append(",".join([str(i) for i in [env_num, ud, task[0], taxk[1], task[2], div, rank]]))
  
  outfile = open("diversity_ranks.csv")
  outfile.write("\n".join(data))
  outfile.close()
  
subject: load data correctly