generate sample data, draw baseline

data.py

#!/usr/bin/python

import json
import random
import statistics
import matplotlib.pyplot as plot
import numpy
import sys

def g():
  #return random.expovariate(0.02)
  return sum([random.expovariate(0.02) for x in xrange(3)])/3
  #return numpy.random.poisson(100)

data=[[g() for x in xrange(30)] for x in xrange(120)]

#j=json.loads(open('sample.txt').read())
#data=[[ d['value'] for d in j['data'][k] ] for k in j['data']]

def b1(d,a,b):
  m1=statistics.mean(d)
  s1=statistics.stdev(d)
  u=m1+a*s1
  l=m1-a*s1
  d2=filter(lambda x:x>l and x < u, d)
  m2=statistics.mean(d2)
  s2=statistics.stdev(d2)
  return m2, m2+b*s2, m2-b*s2

def b2(d,a,b):
  d2=sorted(d)[a:-a]
  m=statistics.mean(d2)
  s=statistics.stdev(d2)
  return m, m+b*s, m-b*s

def b3(d, n):
  d2=sorted(d)
  return d2[len(d2)/2], d2[n], d2[-n]

def conv(d, n):
   return [ statistics.mean(d[i:i+n]) for i in xrange(len(d)-n+1) ]


c=10
dd2=map(lambda d: b1(d, 1, 3), data)
dd3=map(lambda d: b1(d, 3, 3), data)
dd1=map(lambda d: b2(d, 3, 3), data)
dd4=map(lambda d: b3(d, 2), data)
dd5=map(lambda d: b1(d, 3, 3), data)

dd = dd3

def col(d, i):
  return map(lambda t:t[i], d)


def histogram(d):
  m={}
  for n in d:
    n = int(n)
    v=m.get(n, 0)
    m[n]=v+1
  ks = sorted(m.keys())
  vs = [ m[k] for k in ks ]
  return ks, vs

#h = histogram(sum(data,[]))
#plot.plot(h[0], h[1])
#plot.show()
#sys.exit()

#plot.ion()
x=xrange(len(dd)-c+1)
plot.plot(x, conv(col(dd, 0), c), 'k', conv(col(dd, 1), c), 'r', conv(col(dd, 2), c), 'b')

x=xrange(len(dd))
#plot.plot(x, col(dd, 0), 'k', col(dd, 1), 'r', col(dd, 2), 'b')

plot.plot(x, col(data, 11), 'g')
#plot.plot(x, col(data, 0), 'g')

plot.plot(x, col(dd, 0), 'k', col(dd, 1), 'r', col(dd, 2), 'b')
plot.show()