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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).


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:

    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
    journal = {MNRAS},
    eprint = {1208.3523},
     year = 2013,
    volume = 432,
    pages = {1862-1893},
      doi = {10.1093/mnras/stt644}


install with:

pip install loess

Without writing access to the global site-packages directory, use:

pip install --user loess


See loess/examples and the files headers.


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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