| Name | mne-pipeline-hd JSON |
| Version |
0.3.5
JSON |
| download |
| home_page | None |
| Summary | A Pipeline-GUI for MNE-Python from MEG-Lab Heidelberg |
| upload_time | 2024-09-14 17:00:00 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2019-2024, authors of mne-pipeline-hd All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
eeg
gui
heidelberg
meg
mne-python
pipeline
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# mne-pipeline-hd
### A Pipeline-GUI for [MNE-Python](https://mne.tools/stable/index.html) from MEG-Lab Heidelberg
## Installation
1. Install MNE-python as instructed on
the [website](https://www.martinos.org/mne/stable/install_mne_python.html)
2. To install `mne_pipeline_hd` in the conda-enviroment you created in step 1 you can either
- Install the stable version with `pip install mne_pipeline_hd`
- Install the development version with `pip install git+https://github.com/marsipu/mne_pipeline_hd.git@main`
## Update
Run `pip install --upgrade --no-deps --force-reinstall git+https://github.com/marsipu/mne_pipeline_hd.git@main`
for an update to the development version
or `pip install --upgrade mne-pipeline-hd` for the latest stable release.
## Start
Run `mne_pipeline_hd` in your conda-environment where you installed mne-python and mne-pipeline-hd.
**or**
run \_\_main\_\_.py from the terminal or an IDE like PyCharm, VSCode, Atom,
etc.
***When using the pipeline and its functions bear in mind that the pipeline is
still in development!
The basic functions supplied are just a suggestion and you should verify before
usage if they do what you need.
They are also partly still adjusted to specific requirements which may not
apply to all data.***
## Bug-Report/Feature-Request
Please report bugs on GitHub as an issue or to me (dev@mgschulz.de)
directly.
And if you got ideas on how to improve the pipeline or some feature-requests,
you are welcome to open an issue too or send an e-mail (dev@mgschulz.de)
## Contribute and build your own functions/fix bugs
I you want to help by contributing, I would be very happy:
You need a [GitHub-Account](https://github.com/)
and should
have [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
installed.
1. Fork this repository on GitHub
2. Move to the folder where you want to clone to
3. Clone **your forked repository** with git from a
terminal: `git clone <url you get from the green clone-button from your forked repository on GitHub>`
4. Add upstream to git for
updates: `git remote add upstream git://github.com/marsipu/mne-pipeline-hd.git`
5. Install development version with pip: `pip install -e .[tests]`
6. Install the pre-commit hooks with: `pre-commit install`
7. Create a branch for changes: `git checkout -b <branch-name>`
8. Commit changes: `git commit -am "<your commit message>"`
9. Push changes to your forked repository on GitHub: `git push`
10. Make "New pull request" from your new feature branch
You can always [write me an e-mail](mailto:dev@mgschulz.de), if you have questions
about the contribution-process
or about the program-structure.
## Acknowledgments
This Pipeline is build on top
of [MNE-Python](https://mne.tools/stable/index.html)
> A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck,
> L. Parkkonen, M. Hämäläinen,
> MNE software for processing MEG and EEG data, NeuroImage, Volume 86, 1
> February 2014, Pages 446-460, ISSN 1053-8119,
> [DOI](https://doi.org/10.1016/j.neuroimage.2013.10.027)
It was originally inspired by a pipeline
from [Lau M. Andersen](https://doi.org/10.3389/fnins.2018.00006)
> Andersen LM. Group Analysis in MNE-Python of Evoked Responses from a Tactile
> Stimulation Paradigm: A Pipeline for
> Reproducibility at Every Step of Processing, Going from Individual Sensor
> Space Representations to an across-Group
> Source Space Representation. Front Neurosci. 2018 Jan 22;12:6. doi:
> 10.3389/fnins.2018.00006. PMID: 29403349;
> PMCID: PMC5786561.
This program also
integrates [autoreject](https://doi.org/10.1016/j.neuroimage.2017.06.030)
> Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre
> Gramfort. 2017.
> “Autoreject: Automated artifact rejection for MEG and EEG data”. NeuroImage,
> 159, 417-429.
The colorpalettes for light and dark theme are inspired from [PyQtDarkTheme](https://github.com/5yutan5/PyQtDarkTheme).
Many ideas and basics for GUI-Programming where taken
from [LearnPyQt](https://www.learnpyqt.com/) and numerous
stackoverflow-questions/solutions.
The development is financially supported
by [Heidelberg University](https://www.uni-heidelberg.de/de/forschung/forschungsprofil/fields-of-focus/field-of-focus-i).
Thank you to the members of my laboratory (especially my
supervisor [Andre Rupp](https://www.klinikum.uni-heidelberg.de/personen/pd-dr-phil-andre-rupp-271))
for their feedback and testing in the early stages of development.
Raw data
{
"_id": null,
"home_page": null,
"name": "mne-pipeline-hd",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "eeg, gui, heidelberg, meg, mne-python, pipeline",
"author": null,
"author_email": "\"Martin G. Schulz\" <dev@mgschulz.de>",
"download_url": "https://files.pythonhosted.org/packages/02/16/c73de03c49efe2ceb50a39878b296fb76c8fe2e6018c553eefd6cef70be3/mne_pipeline_hd-0.3.5.tar.gz",
"platform": null,
"description": "# mne-pipeline-hd\n\n### A Pipeline-GUI for [MNE-Python](https://mne.tools/stable/index.html) from MEG-Lab Heidelberg\n\n\n\n## Installation\n\n1. Install MNE-python as instructed on\n the [website](https://www.martinos.org/mne/stable/install_mne_python.html)\n2. To install `mne_pipeline_hd` in the conda-enviroment you created in step 1 you can either\n - Install the stable version with `pip install mne_pipeline_hd`\n - Install the development version with `pip install git+https://github.com/marsipu/mne_pipeline_hd.git@main`\n\n## Update\n\nRun `pip install --upgrade --no-deps --force-reinstall git+https://github.com/marsipu/mne_pipeline_hd.git@main`\nfor an update to the development version\nor `pip install --upgrade mne-pipeline-hd` for the latest stable release.\n\n## Start\n\nRun `mne_pipeline_hd` in your conda-environment where you installed mne-python and mne-pipeline-hd.\n\n**or**\n\nrun \\_\\_main\\_\\_.py from the terminal or an IDE like PyCharm, VSCode, Atom,\netc.\n\n***When using the pipeline and its functions bear in mind that the pipeline is\nstill in development!\nThe basic functions supplied are just a suggestion and you should verify before\nusage if they do what you need.\nThey are also partly still adjusted to specific requirements which may not\napply to all data.***\n\n## Bug-Report/Feature-Request\n\nPlease report bugs on GitHub as an issue or to me (dev@mgschulz.de)\ndirectly.\nAnd if you got ideas on how to improve the pipeline or some feature-requests,\nyou are welcome to open an issue too or send an e-mail (dev@mgschulz.de)\n\n## Contribute and build your own functions/fix bugs\n\nI you want to help by contributing, I would be very happy:\n\nYou need a [GitHub-Account](https://github.com/)\nand should\nhave [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)\ninstalled.\n\n1. Fork this repository on GitHub\n2. Move to the folder where you want to clone to\n3. Clone **your forked repository** with git from a\n terminal: `git clone <url you get from the green clone-button from your forked repository on GitHub>`\n4. Add upstream to git for\n updates: `git remote add upstream git://github.com/marsipu/mne-pipeline-hd.git`\n5. Install development version with pip: `pip install -e .[tests]`\n6. Install the pre-commit hooks with: `pre-commit install`\n7. Create a branch for changes: `git checkout -b <branch-name>`\n8. Commit changes: `git commit -am \"<your commit message>\"`\n9. Push changes to your forked repository on GitHub: `git push`\n10. Make \"New pull request\" from your new feature branch\n\nYou can always [write me an e-mail](mailto:dev@mgschulz.de), if you have questions\nabout the contribution-process\nor about the program-structure.\n\n## Acknowledgments\n\nThis Pipeline is build on top\nof [MNE-Python](https://mne.tools/stable/index.html)\n> A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck,\n> L. Parkkonen, M. H\u00e4m\u00e4l\u00e4inen,\n> MNE software for processing MEG and EEG data, NeuroImage, Volume 86, 1\n> February 2014, Pages 446-460, ISSN 1053-8119,\n> [DOI](https://doi.org/10.1016/j.neuroimage.2013.10.027)\n\nIt was originally inspired by a pipeline\nfrom [Lau M. Andersen](https://doi.org/10.3389/fnins.2018.00006)\n> Andersen LM. Group Analysis in MNE-Python of Evoked Responses from a Tactile\n> Stimulation Paradigm: A Pipeline for\n> Reproducibility at Every Step of Processing, Going from Individual Sensor\n> Space Representations to an across-Group\n> Source Space Representation. Front Neurosci. 2018 Jan 22;12:6. doi:\n> 10.3389/fnins.2018.00006. PMID: 29403349;\n> PMCID: PMC5786561.\n\nThis program also\nintegrates [autoreject](https://doi.org/10.1016/j.neuroimage.2017.06.030)\n> Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre\n> Gramfort. 2017.\n> \u201cAutoreject: Automated artifact rejection for MEG and EEG data\u201d. NeuroImage,\n> 159, 417-429.\n\nThe colorpalettes for light and dark theme are inspired from [PyQtDarkTheme](https://github.com/5yutan5/PyQtDarkTheme).\n\nMany ideas and basics for GUI-Programming where taken\nfrom [LearnPyQt](https://www.learnpyqt.com/) and numerous\nstackoverflow-questions/solutions.\n\nThe development is financially supported\nby [Heidelberg University](https://www.uni-heidelberg.de/de/forschung/forschungsprofil/fields-of-focus/field-of-focus-i).\n\nThank you to the members of my laboratory (especially my\nsupervisor [Andre Rupp](https://www.klinikum.uni-heidelberg.de/personen/pd-dr-phil-andre-rupp-271))\nfor their feedback and testing in the early stages of development.\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License Copyright (c) 2019-2024, authors of mne-pipeline-hd All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.",
"summary": "A Pipeline-GUI for MNE-Python from MEG-Lab Heidelberg",
"version": "0.3.5",
"project_urls": {
"Homepage": "https://github.com/marsipu/mne-pipeline-hd",
"Repository": "https://github.com/marsipu/mne-pipeline-hd.git"
},
"split_keywords": [
"eeg",
" gui",
" heidelberg",
" meg",
" mne-python",
" pipeline"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "60cb4ee4e73058fbf1bd3ebdc27407ece5279f6c207729c7173b80c46317333f",
"md5": "486d318f81254538cae6587bd07b89ef",
"sha256": "a1e53e1ac70c173e76e7f6c2e3582f0c2c78068462db9b55a8cfe0abcbf1b222"
},
"downloads": -1,
"filename": "mne_pipeline_hd-0.3.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "486d318f81254538cae6587bd07b89ef",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 238205,
"upload_time": "2024-09-14T16:59:58",
"upload_time_iso_8601": "2024-09-14T16:59:58.204720Z",
"url": "https://files.pythonhosted.org/packages/60/cb/4ee4e73058fbf1bd3ebdc27407ece5279f6c207729c7173b80c46317333f/mne_pipeline_hd-0.3.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0216c73de03c49efe2ceb50a39878b296fb76c8fe2e6018c553eefd6cef70be3",
"md5": "bf3a2effd13de05cd394804da664e2fa",
"sha256": "a76c851d7efc577701ebfe239052c64c28951f41d73813eadab0d3ee765ce7d1"
},
"downloads": -1,
"filename": "mne_pipeline_hd-0.3.5.tar.gz",
"has_sig": false,
"md5_digest": "bf3a2effd13de05cd394804da664e2fa",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 216043,
"upload_time": "2024-09-14T17:00:00",
"upload_time_iso_8601": "2024-09-14T17:00:00.222987Z",
"url": "https://files.pythonhosted.org/packages/02/16/c73de03c49efe2ceb50a39878b296fb76c8fe2e6018c553eefd6cef70be3/mne_pipeline_hd-0.3.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-14 17:00:00",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "marsipu",
"github_project": "mne-pipeline-hd",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "mne-pipeline-hd"
}
