Skip to main content

Python implementation of Friedman's Supersmoother

Project description

This is an efficient implementation of Friedman’s SuperSmoother [1] algorithm in pure Python. It makes use of [numpy](http://numpy.org) for fast numerical computation.

Installation

Installation is simple: To install the released version, type ` $ pip install supersmoother ` To install the bleeding-edge source, download the source code from http://github.com/jakevdp/supersmoother and type: ` $ python setup.py install `

Example

You can see an example of the code in action [on nbviewer](http://nbviewer.ipython.org/github/jakevdp/supersmoother/blob/master/examples/Supersmoother.ipynb)

Testing

This code has full unit tests implemented in [nose](https://nose.readthedocs.org/en/latest/). With nose installed, you can run the test suite using ` $ nosetests supersmoother `

[1]: Friedman, J. H. (1984) A variable span scatterplot smoother. Laboratory for Computational Statistics, Stanford University Technical Report No. 5. ([pdf](http://www.slac.stanford.edu/cgi-wrap/getdoc/slac-pub-3477.pdf))

Project details


Download files

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

Source Distribution

supersmoother-0.2.1.tar.gz (304.8 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page