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lowess

A LOWESS function for Pandas Series that replicates STATA Lowess

https://github.com/ccge-cambridge/lowess

Science Score: 23.0%

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Repository

A LOWESS function for Pandas Series that replicates STATA Lowess

Basic Info
  • Host: GitHub
  • Owner: CCGE-Cambridge
  • License: gpl-3.0
  • Language: Python
  • Default Branch: master
  • Size: 349 KB
Statistics
  • Stars: 7
  • Watchers: 3
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Created over 6 years ago · Last pushed about 4 years ago
Metadata Files
Readme License

README.md

The lowess Package

This package provides a function to perform a LOWESS on Pandas Series objects. LOWESS (locally weighted scatterplot smoothing) [1, 2] as defined by STATA [3]. The regressions utilises some of the methods in [4].

Description

Methods and Formula

Let x and y be the two variables each of length N, and assume that the data are ordered so that xi ≤ xi+1 for i = 1,...,N-1. For each yi, a smoothed value yis is calculated. The subset of points used in calculating yis is i- = max(1, i-k) through i+ = min(i+k, N), where

k = Floor((N × bandwidth - 0.5) / 2).

The weights for each of the observations between j = i-,...,i+ are the tricube

wj = [1 - (|xj - xi| / ∆)3]3,

where ∆ = 1.0001 max(xi+-xi, xi-xi-). The smoothed value yis is then the weighted polynomial regression prediction at xi.

NB: In this implemtation x and y should be Pandas Series objects. The series need not be sorted and x and y can be in different orders, so long as their indexes have the same elements.

Usage

Once the package has been installed it can be imported into a python script

python import lowess

The package provides a single module lowess with a single function lowess.lowess. This function has the signiture:

python lowess.lowess(x, y, bandwidth=0.2, polynomialDegree=1)

where the arguments are: 1. x (pandas.core.series.Series): a Pandas Series containing the x (independent/covariat) values. The indices must be unique. 2. y (pandas.core.series.Series): a Pandas Series containing the y (dependent) values. It must have the same index as x (although not necessarily in the same order.) 3. bandwidth (float, optional): the bandwidth for smoothing. It must be between 0 and 1. Default is 0.2 4. polynomialDegree (int, optional): The degree of polynomial to use in the regression. It must be >= 0. Default is 1.

It returns a Pandas Series containing the smoothed y values, with the same index as y.

If the input is not valid or an error occurs, a LowessError exception is raised.

Examples

Some examples are given in the directory examples.

Installation

Via the PyPI package manager

The package can be installed with pip via the command: 

$ pip install lowess

Via GitHub

The package can be installed from source via GitHub. First download the repository, either via SSH 

$ git clone git@github.com:CCGE-Cambridge/lowess.git

or via HTTPS 

$ git clone https://github.com/CCGE-Cambridge/lowess.git

Then install the package via

$ cd lowess
$ pip install .

Uninstall

To uninstall use the command

$ pip uninstall lowess

Requirements

This package is built on several Python packages, which are listed in requirements.txt. They can be installed using the command

$ pip install -r requirements.txt

Documentaion

Documentaion of the API is provided via Sphinx. To make the documentaion

$ cd docs
$ make html
$ open build/html/index.html

This may require installation of the package sphinx.

Testing

Unit tests are implemented via unittest and are in the file tests/test_lowess.py. To run the tests first download the source code and then run the command: 

$ python -m unittest discover

Coverage can be tested using coverage using:

$ coverage run -m unittest discover
$ coverage report -m

This may require installation of the package coverage.

License

Copyright (c) 2020 Andrew Lee

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.

References

  1. Cleveland, W. S. 1979. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74: 829–836. [https://www.jstor.org/stable/2286407]
  2. Wikipedia: Local Regression - https://en.wikipedia.org/wiki/Local_regression
  3. STATA: Lowess - https://www.stata.com/manuals13/rlowess.pdf
  4. Cappellari et al. 2013 The ATLAS3D project - XX. Mass-size and mass-σ distributions of early-type galaxies: bulge fraction drives kinematics, mass-to-light ratio, molecular gas fraction and stellar initial mass function Monthly Notices of the Royal Astronomical Society 432: 1862-1893 [https://doi.org/10.1093/mnras/stt644]

Owner

  • Name: CCGE-Cambridge
  • Login: CCGE-Cambridge
  • Kind: organization

GitHub Events

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Committers

Last synced: over 2 years ago

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  • Total Committers: 2
  • Avg Commits per committer: 20.0
  • Development Distribution Score (DDS): 0.025
Past Year
  • Commits: 0
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  • Avg Commits per committer: 0.0
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Top Committers
Name Email Commits
Andrew Lee a****5@c****k 39
Andrew Lee a****5@m****k 1
Committer Domains (Top 20 + Academic)
medschl.cam.ac.uk: 1 cam.ac.uk: 1

Issues and Pull Requests

Last synced: about 1 year ago

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  • Total issues: 1
  • Total pull requests: 8
  • Average time to close issues: 1 day
  • Average time to close pull requests: 30 minutes
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  • Total pull request authors: 3
  • Average comments per issue: 1.0
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  • Bot pull requests: 1
Past Year
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Top Authors
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  • ka10ryu1 (1)
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  • ajl65 (6)
  • dependabot[bot] (1)
  • Alyetama (1)
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dependencies (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 1,067 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 3
  • Total maintainers: 1
pypi.org: lowess

Lowess smoothed as defined for STATA 13.

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 1,067 Last month
Rankings
Downloads: 7.5%
Dependent packages count: 10.0%
Average: 16.4%
Stargazers count: 20.3%
Dependent repos count: 21.7%
Forks count: 22.6%
Maintainers (1)
ajl65
Last synced: 12 months ago

Dependencies

requirements.txt pypi
  • numpy ==1.18.2
  • pandas ==1.0.3
  • python-dateutil ==2.8.1
  • pytz ==2019.3
  • six ==1.14.0