Estimate the autocorrelation time of time-series data very quickly
MIT License
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).
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).
Given some time series x
, you can estimate the autocorrelation time
(tau
) using: ::
import acor
tau, mean, sigma = acor.acor(x)