ACOR

This is a direct port of a C++ routine by Jonathan Goodman <http://www.math.nyu.edu/faculty/goodman/index.html>_ (NYU) called ACOR <http://www.math.nyu.edu/faculty/goodman/software/acor/>_ that estimates the autocorrelation time of time series data very quickly.

Dan Foreman-Mackey <http://danfm.ca>_ (NYU) made a few surface changes to the interface in order to write a Python wrapper (with the permission of the original author).

Installation

Just run ::

pip install acor

with sudo if you really need it.

Otherwise, download the source code as a tarball <https://github.com/dfm/acor/tarball/master>_ or clone the git repository from GitHub <https://github.com/dfm/acor>_: ::

git clone https://github.com/dfm/acor.git

Then run ::

cd acor
python setup.py install

to compile and install the module acor in your Python path. The only dependency is NumPy <http://numpy.scipy.org/>_ (including the python-dev and python-numpy-dev packages which you might have to install separately on some systems).

Usage

Given some time series x, you can estimate the autocorrelation time (tau) using: ::

import acor
tau, mean, sigma = acor.acor(x)

References

• http://www.math.nyu.edu/faculty/goodman/software/acor/index.html
• http://www.stat.unc.edu/faculty/cji/Sokal.pdf