# *DIRESA-Torch*
### Overview
*DIRESA-Torch* is a Python package for dimension reduction based on
[PyTorch](https://pytorch.org). The distance-regularized
Siamese twin autoencoder architecture is designed to preserve distance
(ordering) in latent space while capturing the non-linearities in
the datasets.
### Install *DIRESA-Torch*
Install *DIRESA-Torch* with the following command:
``` bash
pip install diresa-torch
```
### Documentation
The *DIRESA* documentation can be found on [Read the Docs](https://diresa-torch.readthedocs.io)
Raw data
{
"_id": null,
"home_page": null,
"name": "diresa-torch",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "climate, learning, machine, pytorch, weather",
"author": null,
"author_email": "Lars Bonnefoy <lars.bonnefoy@vub.be>, Geert De Paepe <geert.de.paepe@vub.be>",
"download_url": "https://files.pythonhosted.org/packages/57/d5/23ec0422ee56aaafd2c0a086518d0ec48163420d429fa13d89c7275ce68a/diresa_torch-0.1.0.tar.gz",
"platform": null,
"description": "# *DIRESA-Torch*\n\n\n### Overview\n\n*DIRESA-Torch* is a Python package for dimension reduction based on \n[PyTorch](https://pytorch.org). The distance-regularized \nSiamese twin autoencoder architecture is designed to preserve distance \n(ordering) in latent space while capturing the non-linearities in\nthe datasets.\n\n\n### Install *DIRESA-Torch*\n\nInstall *DIRESA-Torch* with the following command:\n\n``` bash\n pip install diresa-torch\n```\n\n### Documentation\n\nThe *DIRESA* documentation can be found on [Read the Docs](https://diresa-torch.readthedocs.io)",
"bugtrack_url": null,
"license": null,
"summary": "Diresa - distance-regularized siamese twin autoencoder",
"version": "0.1.0",
"project_urls": {
"Homepage": "https://gitlab.com/etrovub/ai4wcm/public/diresa-torch",
"Issues": "https://gitlab.com/etrovub/ai4wcm/public/diresa-torch/-/issues"
},
"split_keywords": [
"climate",
" learning",
" machine",
" pytorch",
" weather"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "3ce69e905b937f4b72d0879ee1097300ac4f0e350648e4411e64316bc7754248",
"md5": "fff01b811cba0b859dd0e3e0e4fa0cee",
"sha256": "3334d540afeea06c1e18d9171c5d27842e4b8b51a272acb2b4023f97f9d5b38a"
},
"downloads": -1,
"filename": "diresa_torch-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "fff01b811cba0b859dd0e3e0e4fa0cee",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 14357,
"upload_time": "2025-08-22T13:24:01",
"upload_time_iso_8601": "2025-08-22T13:24:01.096000Z",
"url": "https://files.pythonhosted.org/packages/3c/e6/9e905b937f4b72d0879ee1097300ac4f0e350648e4411e64316bc7754248/diresa_torch-0.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "57d523ec0422ee56aaafd2c0a086518d0ec48163420d429fa13d89c7275ce68a",
"md5": "3c733a349519dfe0c62b217cdbf143ae",
"sha256": "de7064a21e9f587363c369cc0ceebc090c66287ffecac3e138b856ebcc683336"
},
"downloads": -1,
"filename": "diresa_torch-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "3c733a349519dfe0c62b217cdbf143ae",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 11298,
"upload_time": "2025-08-22T13:24:23",
"upload_time_iso_8601": "2025-08-22T13:24:23.072643Z",
"url": "https://files.pythonhosted.org/packages/57/d5/23ec0422ee56aaafd2c0a086518d0ec48163420d429fa13d89c7275ce68a/diresa_torch-0.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-22 13:24:23",
"github": false,
"gitlab": true,
"bitbucket": false,
"codeberg": false,
"gitlab_user": "etrovub",
"gitlab_project": "ai4wcm",
"lcname": "diresa-torch"
}
JSON