Estimate the autocorrelation time of a time series quickly.
Project description
This is a direct port of a C++ routine by Jonathan Goodman (NYU) called ACOR that estimates the autocorrelation time of time series data very quickly.
Dan Foreman-Mackey (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 or clone the git repository from GitHub:
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 (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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file acor-1.0.1.tar.gz.
File metadata
- Download URL: acor-1.0.1.tar.gz
- Upload date:
- Size: 4.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ccaa5d68a98e1dbf377aafbcb6d3e7a542086f68fc9f5968b11ea81c84c21ce5 |
|
| MD5 | 1df35fb4ad1d28e5a320b94f83a88bc1 |
|
| BLAKE2b-256 | f5bac2ba4abea13fa7a364a028c0077c2b20bb5cd8d4197b0e6ae111f6bcf658 |
