Python version of non-parametric hypothesis testing using Vargha and Delaney's A12 statistic.

a12.py

class Rx:
  "has the nums of a treatment, its name and rank"
  def __init__(i,lst):
     i.rx, i.lst = lst[0], lst[1:]
     i.mean = sum(i.lst)/len(i.lst)
     i.rank = 0
  def __repr__(i):
    return 'rank #%s %s at %s'%(i.rank,i.rx,i.mean) 

def a12s(lst,rev=True,enough=0.66):
  "sees if lst[i+1] has rank higher than lst[i]"
  lst = [Rx(one) for one in lst]
  lst = sorted(lst,key=lambda x:x.mean,reverse=rev)
  one   = lst[0]
  rank = one.rank = 1
  for two in lst[1:]:
    if a12(one.lst,two.lst,rev) > enough: rank += 1
    two.rank = rank
    one = two
  return lst

def a12(lst1,lst2,rev=True):
  "how often is x in lst1 more than y in lst2?"
  more = same = 0.0
  for x in lst1:
    for y in lst2:
      if   x==y : same += 1
      elif rev     and x > y : more += 1
      elif not rev and x < y : more += 1
  return (more + 0.5*same)  / (len(lst1)*len(lst2))

def fromFile(f="a12.dat",rev=True,enough=0.66):
  "utility for reading sample data from disk"
  import re
  cache = {} 
  num, space = r'^\+?-?[0-9]', r'[ \t\n]+'
  for line in open(f): 
    line = line.strip()
    if line:
      for word in re.split(space,line):
        if re.match(num,word[0]):
          cache[now] += [float(word)]
        else:
          now  = word
          cache[now] = [now]
  return a12s(cache.values(),rev,enough)

; Also, the "fromFile" supports reading from file. For example, if the file is this...

x1 0.34 0.49 0.51 0.60
x2 0.9 0.7 0.8 0.60
x3 0.15 0.25 0.4 0.35
x4 0.6 0.7 0.8 0.90
x5 0.1 0.2 0.3 0.40

; then this call will print the stats:
; for rx in fromFile(): print rx