LOESS: Local Regression Smoothing in One or Two Dimensions
Project description
Local Regression Smoothing in One or Two Dimensions
LOESS is a Python implementation of the Local Regression Smoothing method of Cleveland (1979) (in 1-dim) and Cleveland & Devlin (1988) (in 2-dim).
Attribution
If you use this software for your research, please cite the LOESS package of Cappellari et al. (2013b), where the implementation was described. The BibTeX entry for the paper is:
@ARTICLE{Cappellari2013b, author = {{Cappellari}, M. and {McDermid}, R.~M. and {Alatalo}, K. and {Blitz}, L. and {Bois}, M. and {Bournaud}, F. and {Bureau}, M. and {Crocker}, A.~F. and {Davies}, R.~L. and {Davis}, T.~A. and {de Zeeuw}, P.~T. and {Duc}, P.-A. and {Emsellem}, E. and {Khochfar}, S. and {Krajnovi{\'c}}, D. and {Kuntschner}, H. and {Morganti}, R. and {Naab}, T. and {Oosterloo}, T. and {Sarzi}, M. and {Scott}, N. and {Serra}, P. and {Weijmans}, A.-M. and {Young}, L.~M.}, title = "{The ATLAS$^{3D}$ project - XX. Mass-size and mass-{$\sigma$} distributions of early-type galaxies: bulge fraction drives kinematics, mass-to-light ratio, molecular gas fraction and stellar initial mass function}", journal = {MNRAS}, eprint = {1208.3523}, year = 2013, volume = 432, pages = {1862-1893}, doi = {10.1093/mnras/stt644} }
Installation
install with:
pip install loess
Without writing access to the global site-packages directory, use:
pip install --user loess
Documentation
See loess/examples and the files headers.
License
Copyright (c) 2010-2018 Michele Cappellari
This software is provided as is without any warranty whatsoever. Permission to use, for non-commercial purposes is granted. Permission to modify for personal or internal use is granted, provided this copyright and disclaimer are included in all copies of the software. All other rights are reserved. In particular, redistribution of the code is not allowed.
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