{ "info": { "author": "T.L. Holch, C. Steppa", "author_email": "holchtim@physik.hu-berlin.de, steppa@uni-potsdam.de", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Image Recognition" ], "description": "# HexagDLy\n\nHexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It can be used to build convolutional neural networks for applications that rely on hexagonally sampled data. More information is avialable on [GitHub](https://github.com/ai4iacts/hexagdly \"HexagDLy - Project Page\")\n\n\n\n\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/ai4iacts/hexagdly", "keywords": "hexagonal convolution", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "hexagdly", "package_url": "https://pypi.org/project/hexagdly/", "platform": "", "project_url": "https://pypi.org/project/hexagdly/", "project_urls": { "Homepage": "https://github.com/ai4iacts/hexagdly" }, "release_url": "https://pypi.org/project/hexagdly/2.0.2/", "requires_dist": [ "torch", "torchvision", "numpy", "scipy ; extra == 'dev'", "matplotlib ; extra == 'dev'", "jupyter ; extra == 'dev'", "pytest ; extra == 'dev'" ], "requires_python": ">=3.6", "summary": "Utilising CNNs for hexagonally sampled data with PyTorch", "version": "2.0.2" }, "last_serial": 5096922, "releases": { "2.0.1": [ { "comment_text": "", "digests": { "md5": "34f9c2d908be737943ad6db0ced9d955", "sha256": "73dde75cfeee226cd28ddc8f1b24cf3d9b2478474ad3c2717e8a1f51b7932125" }, "downloads": -1, "filename": "hexagdly-2.0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "34f9c2d908be737943ad6db0ced9d955", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 7309, "upload_time": "2019-02-27T15:26:04", "url": "https://files.pythonhosted.org/packages/60/54/e09d8ad0be5825692620c494e2cbba2a37c3cbee2c53469ef76f5464dab7/hexagdly-2.0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "ccb639669cd600c856d4b2f7dca885d1", "sha256": "3641345c3fd164aa6112355258ba4f2e0e4f3b5c3981aae8f6c5aa7ba023c63a" }, "downloads": -1, "filename": "hexagdly-2.0.1.tar.gz", "has_sig": false, "md5_digest": "ccb639669cd600c856d4b2f7dca885d1", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 6675, "upload_time": "2019-02-27T15:26:06", "url": "https://files.pythonhosted.org/packages/14/7b/1dd7efb08f4d10a7c2ed395effadca458ef7e791eea53399af57c0143bb9/hexagdly-2.0.1.tar.gz" } ], "2.0.2": [ { "comment_text": "", "digests": { "md5": "67e2285fcc0c5560bededffd58fef97a", "sha256": "9dffa265c1636d958929bfc03f56183bc0eaeed3e73645638827e3fba37955a3" }, "downloads": -1, "filename": "hexagdly-2.0.2-py3-none-any.whl", "has_sig": false, "md5_digest": "67e2285fcc0c5560bededffd58fef97a", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 7373, "upload_time": "2019-04-04T12:27:18", "url": "https://files.pythonhosted.org/packages/73/d5/005976bf8b53cd9be6f51e054f576e76041fd425488a52f1d883494916b1/hexagdly-2.0.2-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "7dc127aadd68abf2795c0cad40081808", "sha256": "54b6e2de4d594f15ddcabd6ad018c057dbd65c4f94b4bfa71cd7a9701422a5ef" }, "downloads": -1, "filename": "hexagdly-2.0.2.tar.gz", "has_sig": false, "md5_digest": "7dc127aadd68abf2795c0cad40081808", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 6713, "upload_time": "2019-04-04T12:27:20", "url": "https://files.pythonhosted.org/packages/3c/c9/55df3940e07dd50ffa1aac5f910b3363c753a775efff0ccf7aab69a3a997/hexagdly-2.0.2.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "67e2285fcc0c5560bededffd58fef97a", "sha256": "9dffa265c1636d958929bfc03f56183bc0eaeed3e73645638827e3fba37955a3" }, "downloads": -1, "filename": "hexagdly-2.0.2-py3-none-any.whl", "has_sig": false, "md5_digest": "67e2285fcc0c5560bededffd58fef97a", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 7373, "upload_time": "2019-04-04T12:27:18", "url": "https://files.pythonhosted.org/packages/73/d5/005976bf8b53cd9be6f51e054f576e76041fd425488a52f1d883494916b1/hexagdly-2.0.2-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "7dc127aadd68abf2795c0cad40081808", "sha256": "54b6e2de4d594f15ddcabd6ad018c057dbd65c4f94b4bfa71cd7a9701422a5ef" }, "downloads": -1, "filename": "hexagdly-2.0.2.tar.gz", "has_sig": false, "md5_digest": "7dc127aadd68abf2795c0cad40081808", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 6713, "upload_time": "2019-04-04T12:27:20", "url": "https://files.pythonhosted.org/packages/3c/c9/55df3940e07dd50ffa1aac5f910b3363c753a775efff0ccf7aab69a3a997/hexagdly-2.0.2.tar.gz" } ] }