https://github.com/mmagnuski/borsar

Various tools for EEG/MEG data analysis.

https://github.com/mmagnuski/borsar

Science Score: 26.0%

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  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
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  • Scientific vocabulary similarity
    Low similarity (16.0%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Various tools for EEG/MEG data analysis.

Basic Info
Statistics
  • Stars: 10
  • Watchers: 2
  • Forks: 1
  • Open Issues: 26
  • Releases: 0
Created almost 8 years ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.md

CircleCI Coverage Status

Various tools, objects and functions for EEG/MEG data analysis and visualisation. Some functionality that is available here may be later moved to mne-python.

borsar includes: * PSD object for manipulation of power spectral results * Clusters object for storage, manipulation and plotting of clutser-based results, both in channel and sourcee space * efficient regression for multichannel data (compute_regression_t) * cluster_based_regression to perform regression tests in cluster-based permutation framework * numpy and numba implementations of cluster-based permutation tests in 3d space (for example in channels x frequency x time space) with optional filtering by minimum number of adjacent channels (min_adj_ch, equivalent of minnbchan in fieldtrip). * Topo object for topomap plots that retains the topomap state, allows to mark channels, efficiently update data, change contour line width and style for one or multiple topomaps.

Installation

To get the official borsar version install using pip: pip install borsar The PyPI version may be sometimes behind the GitHub version so to install from GitHub you can use pip in the following way: pip install git+https://github.com/mmagnuski/borsar However, if you plan to frequently update the dev version and contribute to borsar, install by cloning the repo with git and installing in dev mode: cd where_you_want_to_download_borsar git clone https://github.com/mmagnuski/borsar cd borsar pip install -e . the second and third method require git to be installed.

Documentation

Go to the online documentation for more information about usage examples or full API docs. :construction: be warned that documentation is under contstruction :construction:

Owner

  • Name: Mikolaj Magnuski
  • Login: mmagnuski
  • Kind: user
  • Location: Poland

GitHub Events

Total
  • Issues event: 1
  • Watch event: 1
  • Delete event: 1
  • Issue comment event: 3
  • Push event: 8
  • Pull request event: 4
  • Create event: 2
Last Year
  • Issues event: 1
  • Watch event: 1
  • Delete event: 1
  • Issue comment event: 3
  • Push event: 8
  • Pull request event: 4
  • Create event: 2

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 31
  • Total pull requests: 77
  • Average time to close issues: 9 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.9
  • Average comments per pull request: 1.17
  • Merged pull requests: 68
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 5
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 months
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.6
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • mmagnuski (32)
Pull Request Authors
  • mmagnuski (79)
  • sappelhoff (1)
Top Labels
Issue Labels
DOCs (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 270 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
pypi.org: borsar

tools for electrophysiological analysis, especially cluster-based tests.

  • Versions: 2
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 270 Last month
Rankings
Dependent packages count: 9.5%
Stargazers count: 18.5%
Forks count: 19.2%
Average: 28.8%
Dependent repos count: 68.1%
Maintainers (1)
Last synced: 11 months ago

Dependencies

environment.yml conda
  • flake8
  • h5py
  • joblib
  • matplotlib
  • mkl >=2021.4.0
  • numpy >=1.20.1
  • numpydoc
  • pandas
  • pip
  • psutil
  • pytest
  • pytest-cov
  • python >=3.10
  • scikit-image
  • scikit-learn
  • scipy
  • statsmodels >=0.13.2
  • xlrd
setup.py pypi