This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

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)
Release History

Release History

This version
History Node

1.1.1

History Node

1.1.0

History Node

1.0.2

History Node

1.0.1

History Node

1.0.0

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
acor-1.1.1.tar.gz (6.1 kB) Copy SHA256 Checksum SHA256 Source Aug 5, 2014

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting