Welcome to pyMSDtorch's documentation!
======================================
pyMSDtorch provides easy access to a number of segmentation and denoising methods using convolution neural networks.
The tools available are build for microscopy and synchrotron-imaging/scattering data in mind, but can be used elsewhere
as well.
The easiest way to start playing with the code is to install pyMSDtorch and perform denoising/segmenting using custom
neural networks in our tutorial notebooks located in the pyMSDtorch/tutorials folder.
Check the readthedocs page or the README.md file for more information.
=======
History
=======
0.1.0 (2021-08-10)
------------------
* First release.
0.1.1 (2022-11-28)
------------------
* Second release
* getting ready for 'pip install pyMSDtorch'
* added docstrings as much as we can
* building template notebooks
0.1.2-6 (2022-12-15)
--------------------
* Second release - but now for real
* Ready for 'pip install pyMSDtorch'
* Documentation update
* Added notebooks and functionality for image classification
Raw data
{
"_id": null,
"home_page": "https://bitbucket.org/berkeleylab/pymsdtorch/",
"name": "pyMSDtorch",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "pyMSDtorch",
"author": "Petrus H. Zwart, Eric J. Roberts",
"author_email": "PHZwart@lbl.gov, EJroberts@lbl.gov",
"download_url": "https://files.pythonhosted.org/packages/d1/6d/08ad47e55549fd4fb2a28b0741f888b14ceae1614b918f08550d5dcd61cf/pyMSDtorch-0.1.6.tar.gz",
"platform": null,
"description": "Welcome to pyMSDtorch's documentation!\n======================================\n\npyMSDtorch provides easy access to a number of segmentation and denoising methods using convolution neural networks.\nThe tools available are build for microscopy and synchrotron-imaging/scattering data in mind, but can be used elsewhere\nas well.\n\nThe easiest way to start playing with the code is to install pyMSDtorch and perform denoising/segmenting using custom\nneural networks in our tutorial notebooks located in the pyMSDtorch/tutorials folder.\n\nCheck the readthedocs page or the README.md file for more information.\n\n=======\nHistory\n=======\n\n0.1.0 (2021-08-10)\n------------------\n\n* First release.\n\n\n0.1.1 (2022-11-28)\n------------------\n\n* Second release\n* getting ready for 'pip install pyMSDtorch'\n* added docstrings as much as we can\n* building template notebooks\n\n0.1.2-6 (2022-12-15)\n--------------------\n\n* Second release - but now for real\n* Ready for 'pip install pyMSDtorch'\n* Documentation update\n* Added notebooks and functionality for image classification\n",
"bugtrack_url": null,
"license": "BSD License",
"summary": "Python Boilerplate contains all the boilerplate you need to create a Python package.",
"version": "0.1.6",
"split_keywords": [
"pymsdtorch"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "8af0f92dd6d9591b16ac741558a61217",
"sha256": "0f0c68fdd3032818e6f641788a82c644de4a737890ed3b15a51718b259903955"
},
"downloads": -1,
"filename": "pyMSDtorch-0.1.6.tar.gz",
"has_sig": false,
"md5_digest": "8af0f92dd6d9591b16ac741558a61217",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 278661,
"upload_time": "2022-12-16T19:40:25",
"upload_time_iso_8601": "2022-12-16T19:40:25.071595Z",
"url": "https://files.pythonhosted.org/packages/d1/6d/08ad47e55549fd4fb2a28b0741f888b14ceae1614b918f08550d5dcd61cf/pyMSDtorch-0.1.6.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-16 19:40:25",
"github": false,
"gitlab": false,
"bitbucket": true,
"bitbucket_user": "berkeleylab",
"bitbucket_project": "pymsdtorch",
"lcname": "pymsdtorch"
}
JSON