This python program is an attempt to translate the R function ros from the package NADA (Lee 2013) into python 2. The function accepts 2 arguments - 
     obs: a list or numpy array of numbers
     cens: a boolean or list of 0,1 to indicate if obs values are censored (i.e. less than)
The expected return is a array containing the uncensored obs values, and the ros-imputed values for the censored values. 

I have not incorporated other functionality into the program yet, but would like to add:
  * data.frame method to return a data.frame type object similar to ros, which will contain obs, cens, plotting position and imputed values. 
  * a plotting method that will allow for display of the uncensored data and the regression line (and the imputed values)
  * methods to calculate mean, standard deviation, and standard error of mean
  * method to calculate confidence intervals using either bootstrapping or some other method appropriate when bootstrapping is infeasible due to small numbers.
  * the current tranformation assumes log-normal data (log foward transform, exponential back transform). I would like to add the ability to specify other transformations.
  * currently the main() does not work, and the function ros must be called from within python.  

for other details of ROS, see help file for ROS which can be found at http://www.inside-r.org/packages/cran/NADA/docs/ros


References:
Lopaka Lee (2013). NADA: Nondetects And Data Analysis for
  environmental data. R package version 1.5-6.
  http://CRAN.R-project.org/package=NADA

Lee and Helsel (2005) Statistical analysis of environmental data
  containing multiple detection limits: S-language software for
  regression on order statistics, Computers in Geoscience vol. 31,
  pp. 1241-1248.

Lee and Helsel (2005) Baseline models of trace elements in major
  aquifers of the United States. Applied Geochemistry vol. 20, pp.
  1560-1570.

Dennis R. Helsel (2005), Nondetects And Data Analysis: John Wiley
  and Sons, New York.

Dennis R. Helsel (1990), Less Than Obvious: Statistical Methods
 for, Environmental Science and Technology, vol.24, no. 12, pp.
 1767-1774

Dennis R. Helsel and Timothy A. Cohn (1988), Estimation of
 descriptive statistics for multiply censored water quality data,
 Water Resources Research vol. 24, no. 12, pp.1997-2004