py-uwerr 0.1

Pure Python library that implements Wolff's method to compute autocorrelation timesof Monte Carlo series.

Author: Dirk Hesse <[e-mail]>

We implement the method to estimate autocorrelation times of Monte Carlo data presented in

U. Wolff [ALPHA Collaboration], Monte Carlo errors with less errors, Comput. Phys. Commun. 156, 143 (2004) [hep-lat/0306017].

PUBLICATIONS MAKING USE OF THIS CODE MUST CITE THE PAPER.

The main objective is the following: Data coming from a Monte Carlo simulation usually suffers from autocorrelation. It is not straight-forward to estimate this autocorrelation, which is required to give robust estimates for errors. This program implements a method proposed by Wolff to estimate autocorrelations in a safe way.

Quick start

This package contains code to generate correlated data, so we can conveniently demonstrate the basic functionality of the code in a short example:

>>> from puwr import tauint, correlated_data
>>> correlated_data(2, 10)
[[array([ 1.02833043,  1.08615234,  1.16421776,  1.15975754,
          1.23046603,  1.13941114,  1.1485227 ,  1.13464388,
          1.12461557,  1.15413354])]]
>>> mean, delta, tint, d_tint = tauint(correlated_data(10, 200), 0)
>>> print "mean = {0} +/- {1}".format(mean, delta)
mean = 1.42726267057 +/- 0.03013853
>>> print "tau_int = {0} +/- {1}".format(tint, d_tint)
tau_int = 9.89344869217 +/- 4.10466090332

The data is expected to be in the format data[observable][replicum][measurement]. See the documentation that comes with this code for more information.

License

See LICENSE file.