| Name | borsar JSON |
| Version |
0.2
JSON |
| download |
| home_page | None |
| Summary | tools for electrophysiological analysis, especially cluster-based tests. |
| upload_time | 2024-08-14 12:42:48 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | BSD-3-Clause |
| keywords |
neuroscience
neuroimaging
meg
eeg
brain
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
|
| coveralls test coverage |
No coveralls.
|
[](https://dl.circleci.com/status-badge/redirect/gh/mmagnuski/borsar/tree/master)
[](https://codecov.io/gh/mmagnuski/borsar)
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](https://martinos.org/mne/dev/index.html).
`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](https://git-scm.com/) to be installed.
## Documentation
Go to the [online documentation](https://mmagnuski.github.io/borsar.github.io/index.html) for more information about usage examples or full API docs.
:construction: be warned that documentation is under contstruction :construction:
Raw data
{
"_id": null,
"home_page": null,
"name": "borsar",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "Miko\u0142aj Magnuski <mmagnuski@swps.edu.pl>",
"keywords": "neuroscience, neuroimaging, MEG, EEG, brain",
"author": null,
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/4e/75/d41a11fee514cd11cb85021ffda948be383cf6252f4f25314461bd6588a3/borsar-0.2.tar.gz",
"platform": null,
"description": "[](https://dl.circleci.com/status-badge/redirect/gh/mmagnuski/borsar/tree/master)\r\n[](https://codecov.io/gh/mmagnuski/borsar)\r\n\r\nVarious tools, objects and functions for EEG/MEG data analysis and visualisation. Some functionality that is available here may\r\nbe later moved to [mne-python](https://martinos.org/mne/dev/index.html).\r\n\r\n`borsar` includes:\r\n* `PSD` object for manipulation of power spectral results\r\n* `Clusters` object for storage, manipulation and plotting of clutser-based results, both in channel and sourcee space\r\n* efficient regression for multichannel data (`compute_regression_t`)\r\n* `cluster_based_regression` to perform regression tests in cluster-based permutation framework\r\n* 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).\r\n* `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.\r\n\r\n\r\n## Installation\r\nTo get the official borsar version install using pip:\r\n```\r\npip install borsar\r\n```\r\nThe PyPI version may be sometimes behind the GitHub version so to install from GitHub you can use pip in the following way:\r\n```\r\npip install git+https://github.com/mmagnuski/borsar\r\n```\r\nHowever, if you plan to frequently update the dev version and contribute to `borsar`, install by cloning the repo with\r\ngit and installing in dev mode:\r\n```\r\ncd where_you_want_to_download_borsar\r\ngit clone https://github.com/mmagnuski/borsar\r\ncd borsar\r\npip install -e .\r\n```\r\nthe second and third method require [git](https://git-scm.com/) to be installed.\r\n\r\n## Documentation\r\nGo to the [online documentation](https://mmagnuski.github.io/borsar.github.io/index.html) for more information about usage examples or full API docs.\r\n:construction: be warned that documentation is under contstruction :construction:\r\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "tools for electrophysiological analysis, especially cluster-based tests.",
"version": "0.2",
"project_urls": {
"Homepage": "https://github.com/mmagnuski/borsar"
},
"split_keywords": [
"neuroscience",
" neuroimaging",
" meg",
" eeg",
" brain"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "a0dc0c9bdc3d6e18e9837e78f830196fc8ab97661bd48035f93c41c6f3f83467",
"md5": "26bed2de3d5b57c6abe8b2a6a3320beb",
"sha256": "cce2eceacd6d626e01db48b312e66e64e25088a0529a45faa04c84fee305dc3b"
},
"downloads": -1,
"filename": "borsar-0.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "26bed2de3d5b57c6abe8b2a6a3320beb",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 108762,
"upload_time": "2024-08-14T12:42:46",
"upload_time_iso_8601": "2024-08-14T12:42:46.719257Z",
"url": "https://files.pythonhosted.org/packages/a0/dc/0c9bdc3d6e18e9837e78f830196fc8ab97661bd48035f93c41c6f3f83467/borsar-0.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4e75d41a11fee514cd11cb85021ffda948be383cf6252f4f25314461bd6588a3",
"md5": "a64a1288cee2d0cb0f8d81a8317b563a",
"sha256": "f68776824d0de30e95b872411437c709fad3db72efaeced3df6e1354c1dabc27"
},
"downloads": -1,
"filename": "borsar-0.2.tar.gz",
"has_sig": false,
"md5_digest": "a64a1288cee2d0cb0f8d81a8317b563a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 98115,
"upload_time": "2024-08-14T12:42:48",
"upload_time_iso_8601": "2024-08-14T12:42:48.383323Z",
"url": "https://files.pythonhosted.org/packages/4e/75/d41a11fee514cd11cb85021ffda948be383cf6252f4f25314461bd6588a3/borsar-0.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-08-14 12:42:48",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mmagnuski",
"github_project": "borsar",
"travis_ci": true,
"coveralls": false,
"github_actions": false,
"circle": true,
"lcname": "borsar"
}
