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BackgroundSubtraction

A collection of background subtraction algorithms for spectroscopic data

https://github.com/sebastianament/backgroundsubtraction.jl

Science Score: 31.0%

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    Low similarity (9.7%) to scientific vocabulary
Last synced: 11 months ago · JSON representation ·

Repository

A collection of background subtraction algorithms for spectroscopic data

Basic Info
  • Host: GitHub
  • Owner: SebastianAment
  • License: mit
  • Language: Julia
  • Default Branch: main
  • Size: 12.5 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 5
  • Open Issues: 3
  • Releases: 0
Created about 6 years ago · Last pushed over 3 years ago
Metadata Files
Readme License Citation

README.md

BackgroundSubtraction.jl

CI codecov

A collection of background subtraction algorithms for spectroscopic data

Getting Started

To install the package, simply type "]" followed up by "add BackgroundSubtraction" in the Julia REPL.

The main function is based on the multi-component background learning model (MCBL), with the corresponding function mcbl: mcbl(A::AbstractMatrix, k::Int, x::AbstractVector, l::Real) * A is the data matrix, each column of which is assumed to be a spectrogram. * k is the number of components in the multi-component background model. * x is the index vector corresponding to rows of A. For example, if a column of A is an X-ray diffraction spectrogram, x should be the angle of diffraction of each row. * l is the length scale of the background component. It controls how quickly the background model is allowed to vary with x. This functions as an important regularization for medium-sized data (100s-1000s spectrograms).

There are 3 parameters controlling the algorithm, which can optionally be passed as keyword arguments: * minres is the minimum residual standard deviation after which the algorithms terminates. * nsigma is the number of standard deviations above the noise level after which a data point is classified as a peak. A smaller number will be more agressive in classifying points as peaks. * maxiter is the maximum number of iterations between updating the noise and background model.

Citing this work

If you use the MCBL for work or a publication, please cite the original article:

Ament, S.E., Stein, H.S., Guevarra, D. et al. Multi-component background learning automates signal detection for spectroscopic data. npj Comput Mater 5, 77 (2019). https://doi.org/10.1038/s41524-019-0213-0

Owner

  • Name: Sebastian Ament
  • Login: SebastianAment
  • Kind: user
  • Company: Meta

Research Scientist @ Meta

Citation (CITATION.bib) 

@article{ament2019multi,
  title={Multi-component background learning automates signal detection for spectroscopic data},
  author={Ament, Sebastian E and Stein, Helge S and Guevarra, Dan and Zhou, Lan and Haber, Joel A and Boyd, David A and Umehara, Mitsutaro and Gregoire, John M and Gomes, Carla P},
  journal={npj Computational Materials},
  volume={5},
  number={1},
  pages={1--7},
  year={2019},
  publisher={Nature Publishing Group}
}

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 22
  • Total Committers: 2
  • Avg Commits per committer: 11.0
  • Development Distribution Score (DDS): 0.091
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Sebastian Ament s****t@g****m 20
MingChiangChang 7****g 2

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 2
  • Total pull requests: 5
  • Average time to close issues: less than a minute
  • Average time to close pull requests: 22 days
  • Total issue authors: 2
  • Total pull request authors: 4
  • Average comments per issue: 4.0
  • Average comments per pull request: 0.6
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • SebastianAment (1)
  • JuliaTagBot (1)
Pull Request Authors
  • SebastianAment (2)
  • MingChiangChang (1)
  • github-actions[bot] (1)
  • drs378 (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • julia 6 total
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
juliahub.com: BackgroundSubtraction

A collection of background subtraction algorithms for spectroscopic data

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 6 Total
Rankings
Dependent repos count: 9.9%
Forks count: 21.7%
Average: 30.9%
Dependent packages count: 38.9%
Stargazers count: 52.9%
Last synced: 11 months ago

Dependencies

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