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))
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