# behavysis_pipeline
[Documentation](https://tlee08.github.io/behavysis_pipeline/)
## Installation
### Dev installation
```bash
conda env create -f conda_env.yaml
conda activate behavysis_pipeline_env
pip install poetry
poetry install
```
### User installation
```bash
conda env create -f conda_env.yaml
```
## References
Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018, August 20). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience. Springer Science and Business Media LLC. http://doi.org/10.1038/s41593-018-0209-y
Nath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., & Mathis, M. W. (2019, June 21). Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols. Springer Science and Business Media LLC. http://doi.org/10.1038/s41596-019-0176-0
Lauer, J., Zhou, M., Ye, S., Menegas, W., Schneider, S., Nath, T., … Mathis, A. (2022, April). Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods. Springer Science and Business Media LLC. http://doi.org/10.1038/s41592-022-01443-0
Nilsson, S., Goodwin, N., Choong, J. J., Hwang, S., Wright, H., Norville, Z., Tong, X., Lin, D., Bentzley, B., Eshel, N., McLaughlin, R., & Golden, S. Simple Behavioral Analysis (SimBA): an open source toolkit for computer classification of complex social behaviors in experimental animals [Computer software]. https://github.com/sgoldenlab/simba
Raw data
{
"_id": null,
"home_page": "https://tlee08.github.io/behavysis_pipeline",
"name": "behavysis_pipeline",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.12",
"maintainer_email": null,
"keywords": null,
"author": "BowenLab",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/dd/ff/74ef8c3dec5049b8f57d76be7134d3d1f90f9c96c447f353927371bb276e/behavysis_pipeline-0.1.20.tar.gz",
"platform": null,
"description": "# behavysis_pipeline\n\n[Documentation](https://tlee08.github.io/behavysis_pipeline/)\n\n## Installation\n\n### Dev installation\n\n```bash\nconda env create -f conda_env.yaml\nconda activate behavysis_pipeline_env\npip install poetry\npoetry install\n```\n\n### User installation\n\n```bash\nconda env create -f conda_env.yaml\n```\n\n## References\n\nMathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018, August 20). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience. Springer Science and Business Media LLC. http://doi.org/10.1038/s41593-018-0209-y\n\nNath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., & Mathis, M. W. (2019, June 21). Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols. Springer Science and Business Media LLC. http://doi.org/10.1038/s41596-019-0176-0\n\nLauer, J., Zhou, M., Ye, S., Menegas, W., Schneider, S., Nath, T., \u2026 Mathis, A. (2022, April). Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods. Springer Science and Business Media LLC. http://doi.org/10.1038/s41592-022-01443-0\n\nNilsson, S., Goodwin, N., Choong, J. J., Hwang, S., Wright, H., Norville, Z., Tong, X., Lin, D., Bentzley, B., Eshel, N., McLaughlin, R., & Golden, S. Simple Behavioral Analysis (SimBA): an open source toolkit for computer classification of complex social behaviors in experimental animals [Computer software]. https://github.com/sgoldenlab/simba\n",
"bugtrack_url": null,
"license": "LGPL-3.0-or-later",
"summary": "An animal behaviour processing and analysis package",
"version": "0.1.20",
"project_urls": {
"Documentation": "https://tlee08.github.io/behavysis_pipeline",
"Homepage": "https://tlee08.github.io/behavysis_pipeline",
"Repository": "https://github.com/tlee08/behavysis_pipeline"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0492e8c807168bb50985679923f4f683929dbc96604df291e2d6fdc8c69104c3",
"md5": "fd2969b6cd4ccc44354d7fd11e6c5d6e",
"sha256": "d28f679f8cbd895275ebd79f031d69ce5acaac04988777f2b991e4a270375dea"
},
"downloads": -1,
"filename": "behavysis_pipeline-0.1.20-py3-none-any.whl",
"has_sig": false,
"md5_digest": "fd2969b6cd4ccc44354d7fd11e6c5d6e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.12",
"size": 65018,
"upload_time": "2024-08-16T07:38:13",
"upload_time_iso_8601": "2024-08-16T07:38:13.764731Z",
"url": "https://files.pythonhosted.org/packages/04/92/e8c807168bb50985679923f4f683929dbc96604df291e2d6fdc8c69104c3/behavysis_pipeline-0.1.20-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ddff74ef8c3dec5049b8f57d76be7134d3d1f90f9c96c447f353927371bb276e",
"md5": "b90cf8c77b8952c7d7ca5344412b4a16",
"sha256": "4a5d777a93268ba8aee7018d06165ac9fa83af1d1eec7d5db81ffd2df1c365ae"
},
"downloads": -1,
"filename": "behavysis_pipeline-0.1.20.tar.gz",
"has_sig": false,
"md5_digest": "b90cf8c77b8952c7d7ca5344412b4a16",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.12",
"size": 51917,
"upload_time": "2024-08-16T07:38:16",
"upload_time_iso_8601": "2024-08-16T07:38:16.485482Z",
"url": "https://files.pythonhosted.org/packages/dd/ff/74ef8c3dec5049b8f57d76be7134d3d1f90f9c96c447f353927371bb276e/behavysis_pipeline-0.1.20.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-08-16 07:38:16",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "tlee08",
"github_project": "behavysis_pipeline",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "behavysis_pipeline"
}