Jitter from exponential distribution

Comments

• LJGS

  April 2018 edited April 2018

  Hmm, no idea what a jitter time is! I can maybe help a bit with the distribution. The below will generate a truncated exponential distribution between 0-3500ms using the inverse cdf method, then add on 500ms. This almost gives you what you want. The mean will be a little shy of 1000 (990 something) because you are cutting a tiny bit of the tail off.

  import numpy as np
  #0.5 is 500ms, desired mean before we add on the initial 500
  p= 1 - np.exp(-1/0.5)
  #generate uniform random numbers
  r=np.random.rand( 100000)
  # N is the highest delay allowed (in seconds). 3500ms until we add on initial 500 ms at theend
  N=3.5
   #x1000 to return in milliseconds as opensesame expects.
  gen_trunc=np.log(1-r*(1-(1-p)**N))/np.log(1-p) * 1000
  #add on 500 to get max 4000 and mean (almost) 1000
  jitter = gen_trunc + 500
  print np.mean(jitter)
  
  import matplotlib.pyplot as plt
  bins=np.arange(0,4500,1)
  plt.hist(jitter,bins=bins,alpha=.8)
  plt.show()
  
• S_H

  April 2018

  Wow. Thank you so much!

  How can I randomly sample the function you've created?
• LJGS

  April 2018

  Sorry for slow reply, the variable jitter is a big array of random numbers drawn from the distribution you wanted, so just take as many numbers out as you need starting from 1 to 100,000

  cheers
• S_H

  April 2018

  I'll try that.. Thanks!!!!!