I haven't seen this done before so I don't know if there's a standard

way. The idea seems to be to take some points which are real data,

create a random variable for each point with the points' position as

the mean, then choose some number of points from each distribution to

create some new points clustered around the original data. Some

examples online seem to use uniform distributions and Poisson

distributions or mixtures of these (uniform for the x-variable and

Poisson for the y). If my take on this is correct, you can use

scipy.stats to do this - an example is in the attached file which

creates Gaussian distributions for each of the x and y coordinates

then creates an equal number of new points for each of the seed

points. The online examples I saw seem to choose random numbers of new

points for each seed point. I didn't bother trying to cover all the

possibilities. Hopefully this is helpful,

Gary R.

jitter.py (451 Bytes)

## ···

On Thu, Feb 24, 2011 at 3:04 PM, Uri Laserson <laserson@...1166...> wrote:

Hi all,

I am interested in jittering points in a plot. I searched the forum, but I

am amazed at the dearth of results on the topic. I am referring to

something like this:

http://goo.gl/Db47s

or

http://goo.gl/BjIZt

Is there a standard way people do this with MPL?

Thanks!

Uri

...................................................................................

Uri Laserson

Graduate Student, Biomedical Engineering

Harvard-MIT Division of Health Sciences and Technology

M +1 917 742 8019

laserson@...1166...