| Analysis of Monte Carlo data |
|---|
Author: Dirk Hesse <herr.dirk.hesse@gmail.com> |
| 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) |
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 +/- 4.10466090332The data is expected to be in the format data[observable][replicum][measurement]. See the documentation that comes with this code for more information.
See LICENSE file.