higra


Namehigra JSON
Version 0.6.12 PyPI version JSON
download
home_pagehttps://github.com/higra/Higra
SummaryHierarchical Graph Analysis
upload_time2024-12-21 11:20:00
maintainerNone
docs_urlNone
authorBenjamin Perret
requires_pythonNone
licenseCeCILL-B
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            

# Higra: Hierarchical Graph Analysis

[![Build Status](https://perretb.visualstudio.com/AzurePipelines/_apis/build/status/higra.Higra?branchName=master)](https://perretb.visualstudio.com/AzurePipelines/_build/latest?definitionId=2&branchName=master)
[![Build status](https://ci.appveyor.com/api/projects/status/oo0v2uepcxihvwno?svg=true)](https://ci.appveyor.com/project/PerretB/higra-21ed3)
[![codecov](https://codecov.io/gh/higra/Higra/branch/master/graph/badge.svg)](https://codecov.io/gh/higra/Higra)
[![Documentation Status](https://readthedocs.org/projects/higra/badge/?version=latest)](https://higra.readthedocs.io/en/stable/?badge=stable)

Higra is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods. Some of the main features are:

- efficient methods and data structures to handle the dual representations of hierarchical clustering: trees (dendrograms) and saliency maps (ultrametric distances);
- hierarchical clusterings: quasi-flat zone hierarchy, hierarchical watersheds, agglomerative clustering (single-linkage, average-linkage, complete-linkage, exponential-linkage, Ward, or user provided linkage rule), constrained connectivity hierarchy;
- component trees: min and max trees;
- manipulate and explore hierarchies:  simplification, accumulators, cluster extraction, various attributes (size, volume, dynamics, perimeter, compactness, moments, etc.), horizontal and non-horizontal cuts, hierarchies alignment;
- optimization on hierarchies: optimal cuts, energy hierarchies;
- algorithms on graphs: accumulators, vertices and clusters dissimilarities, region adjacency graphs, minimum spanning trees and forests, watershed cuts;
- assessment: supervised assessment of graph clusterings and hierarchical clusterings;
- image toolbox: special methods for grid graphs, tree of shapes, hierarchical clustering methods dedicated to image analysis, optimization of Mumford-Shah energy.

Higra is thought for modularity, performance and seamless integration with classical data analysis pipelines. The data structures (graphs and trees) are decoupled from data (vertex and edge weights ) which are simply arrays ([xtensor](https://github.com/QuantStack/xtensor) arrays in C++ and [numpy](https://github.com/numpy/numpy) arrays in Python).

## Installation

The Python package can be installed with Pypi:

```bash
pip install higra
```

Supported systems: 

 - Python 3.9, 3.10, 3.11, 3.12, 3.13 (amd64)
 - Linux 64 bits, macOS, Windows 64 bits (requires [Visual C++ Redistributable for Visual Studio 2015](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads))

macOS ARM64 is currently only supported through conda ``conda install higra -c conda-forge``

## Documentation

[https://higra.readthedocs.io/](https://higra.readthedocs.io/)

### Demonstration and tutorials

A collection of demonstration notebooks is available in the [documentation](https://higra.readthedocs.io/en/stable/notebooks.html). 
Notebooks are stored in a dedicated repository [Higra-Notebooks](https://github.com/higra/Higra-Notebooks).

### Code samples

This example demonstrates the construction of a single-linkage hierarchical clustering and its simplification by a cluster size criterion.

[![Example on clustering](doc/source/fig/example_graph_filtering.png)](https://github.com/higra/Higra-Notebooks/blob/master/Illustrations%20of%20SoftwareX%202019%20article.ipynb)

This example demonstrates the use of hierarchical clustering for image filtering.

[![Example on image filtering](doc/source/fig/example_image_filtering.png)](https://github.com/higra/Higra-Notebooks/blob/master/Illustrations%20of%20SoftwareX%202019%20article.ipynb)

## Developing C++ extensions

While Higra provides many vectorized operators to implement algorithms efficiently in Python, it is possible that
some operations cannot be done efficiently in Python. 
In such case, the [Higra-cppextension-cookiecutter](https://github.com/higra/Higra-cppextension-cookiecutter) enables
to easily setup and generate c++ extension using Higra with Python bindings.

## License and how-to cite

The license [Cecill-B](http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.txt) is fully compatible with BSD-like licenses (BSD, X11, MIT) with an attribution requirement.

The recommended way to give attribution is by citing the following presentation article:

>  B. Perret, G. Chierchia, J. Cousty, S.J. F. GuimarĂ£es, Y. Kenmochi, L. Najman, [Higra: Hierarchical Graph Analysis](http://www.sciencedirect.com/science/article/pii/S235271101930247X), SoftwareX, Volume 10, 2019. DOI: 10.1016/j.softx.2019.100335

<details> 
 <summary><b>Bibtex</b></summary>
 
    @article{PCCGKN:softwarex2019,
         title = "Higra: Hierarchical Graph Analysis",
         journal = "SoftwareX",
         volume = "10",
         pages = "1--6",
         year = "2019",
         issn = "2352-7110",
         doi = "10.1016/j.softx.2019.100335",
         author = "B. Perret and G. Chierchia and J. Cousty and S.J. F. Guimar\~{a}es and Y. Kenmochi and L. Najman",
     }
        
</details>


 
## Third-party libraries

Higra bundles several third-party libraries (inside the `lib` folder): 

- [pybind11](https://github.com/pybind/pybind11) helps to create Python bindings of c++ methods and classes - [BSD-style license](https://github.com/pybind/pybind11/blob/master/LICENSE)
- [xtensor](https://github.com/QuantStack/xtensor) (with [xtl](https://github.com/QuantStack/xtl), [xsimd](https://github.com/QuantStack/xsimd), and [xtensor-python](https://github.com/QuantStack/xtensor-python) provides `numpy` like arrays for c++ with seamless integration with Python - all under the [BSD-3-Clause license](https://github.com/QuantStack/xtensor/blob/master/LICENSE)
- [Catch2](https://github.com/catchorg/Catch2) is a unit test framework - [Boost Software License 1.0](https://github.com/catchorg/Catch2/blob/master/LICENSE.txt)



            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/higra/Higra",
    "name": "higra",
    "maintainer": null,
    "docs_url": null,
    "requires_python": null,
    "maintainer_email": null,
    "keywords": null,
    "author": "Benjamin Perret",
    "author_email": "benjamin.perret@esiee.fr",
    "download_url": null,
    "platform": null,
    "description": "\n\n# Higra: Hierarchical Graph Analysis\n\n[![Build Status](https://perretb.visualstudio.com/AzurePipelines/_apis/build/status/higra.Higra?branchName=master)](https://perretb.visualstudio.com/AzurePipelines/_build/latest?definitionId=2&branchName=master)\n[![Build status](https://ci.appveyor.com/api/projects/status/oo0v2uepcxihvwno?svg=true)](https://ci.appveyor.com/project/PerretB/higra-21ed3)\n[![codecov](https://codecov.io/gh/higra/Higra/branch/master/graph/badge.svg)](https://codecov.io/gh/higra/Higra)\n[![Documentation Status](https://readthedocs.org/projects/higra/badge/?version=latest)](https://higra.readthedocs.io/en/stable/?badge=stable)\n\nHigra is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods. Some of the main features are:\n\n- efficient methods and data structures to handle the dual representations of hierarchical clustering: trees (dendrograms) and saliency maps (ultrametric distances);\n- hierarchical clusterings: quasi-flat zone hierarchy, hierarchical watersheds, agglomerative clustering (single-linkage, average-linkage, complete-linkage, exponential-linkage, Ward, or user provided linkage rule), constrained connectivity hierarchy;\n- component trees: min and max trees;\n- manipulate and explore hierarchies:  simplification, accumulators, cluster extraction, various attributes (size, volume, dynamics, perimeter, compactness, moments, etc.), horizontal and non-horizontal cuts, hierarchies alignment;\n- optimization on hierarchies: optimal cuts, energy hierarchies;\n- algorithms on graphs: accumulators, vertices and clusters dissimilarities, region adjacency graphs, minimum spanning trees and forests, watershed cuts;\n- assessment: supervised assessment of graph clusterings and hierarchical clusterings;\n- image toolbox: special methods for grid graphs, tree of shapes, hierarchical clustering methods dedicated to image analysis, optimization of Mumford-Shah energy.\n\nHigra is thought for modularity, performance and seamless integration with classical data analysis pipelines. The data structures (graphs and trees) are decoupled from data (vertex and edge weights ) which are simply arrays ([xtensor](https://github.com/QuantStack/xtensor) arrays in C++ and [numpy](https://github.com/numpy/numpy) arrays in Python).\n\n## Installation\n\nThe Python package can be installed with Pypi:\n\n```bash\npip install higra\n```\n\nSupported systems: \n\n - Python 3.9, 3.10, 3.11, 3.12, 3.13 (amd64)\n - Linux 64 bits, macOS, Windows 64 bits (requires [Visual C++ Redistributable for Visual Studio 2015](https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads))\n\nmacOS ARM64 is currently only supported through conda ``conda install higra -c conda-forge``\n\n## Documentation\n\n[https://higra.readthedocs.io/](https://higra.readthedocs.io/)\n\n### Demonstration and tutorials\n\nA collection of demonstration notebooks is available in the [documentation](https://higra.readthedocs.io/en/stable/notebooks.html). \nNotebooks are stored in a dedicated repository [Higra-Notebooks](https://github.com/higra/Higra-Notebooks).\n\n### Code samples\n\nThis example demonstrates the construction of a single-linkage hierarchical clustering and its simplification by a cluster size criterion.\n\n[![Example on clustering](doc/source/fig/example_graph_filtering.png)](https://github.com/higra/Higra-Notebooks/blob/master/Illustrations%20of%20SoftwareX%202019%20article.ipynb)\n\nThis example demonstrates the use of hierarchical clustering for image filtering.\n\n[![Example on image filtering](doc/source/fig/example_image_filtering.png)](https://github.com/higra/Higra-Notebooks/blob/master/Illustrations%20of%20SoftwareX%202019%20article.ipynb)\n\n## Developing C++ extensions\n\nWhile Higra provides many vectorized operators to implement algorithms efficiently in Python, it is possible that\nsome operations cannot be done efficiently in Python. \nIn such case, the [Higra-cppextension-cookiecutter](https://github.com/higra/Higra-cppextension-cookiecutter) enables\nto easily setup and generate c++ extension using Higra with Python bindings.\n\n## License and how-to cite\n\nThe license [Cecill-B](http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.txt) is fully compatible with BSD-like licenses (BSD, X11, MIT) with an attribution requirement.\n\nThe recommended way to give attribution is by citing the following presentation article:\n\n>  B. Perret, G. Chierchia, J. Cousty, S.J. F. Guimar\u00e3es, Y. Kenmochi, L. Najman, [Higra: Hierarchical Graph Analysis](http://www.sciencedirect.com/science/article/pii/S235271101930247X), SoftwareX, Volume 10, 2019. DOI: 10.1016/j.softx.2019.100335\n\n<details> \n <summary><b>Bibtex</b></summary>\n \n    @article{PCCGKN:softwarex2019,\n         title = \"Higra: Hierarchical Graph Analysis\",\n         journal = \"SoftwareX\",\n         volume = \"10\",\n         pages = \"1--6\",\n         year = \"2019\",\n         issn = \"2352-7110\",\n         doi = \"10.1016/j.softx.2019.100335\",\n         author = \"B. Perret and G. Chierchia and J. Cousty and S.J. F. Guimar\\~{a}es and Y. Kenmochi and L. Najman\",\n     }\n        \n</details>\n\n\n \n## Third-party libraries\n\nHigra bundles several third-party libraries (inside the `lib` folder): \n\n- [pybind11](https://github.com/pybind/pybind11) helps to create Python bindings of c++ methods and classes - [BSD-style license](https://github.com/pybind/pybind11/blob/master/LICENSE)\n- [xtensor](https://github.com/QuantStack/xtensor) (with [xtl](https://github.com/QuantStack/xtl), [xsimd](https://github.com/QuantStack/xsimd), and [xtensor-python](https://github.com/QuantStack/xtensor-python) provides `numpy` like arrays for c++ with seamless integration with Python - all under the [BSD-3-Clause license](https://github.com/QuantStack/xtensor/blob/master/LICENSE)\n- [Catch2](https://github.com/catchorg/Catch2) is a unit test framework - [Boost Software License 1.0](https://github.com/catchorg/Catch2/blob/master/LICENSE.txt)\n\n\n",
    "bugtrack_url": null,
    "license": "CeCILL-B",
    "summary": "Hierarchical Graph Analysis",
    "version": "0.6.12",
    "project_urls": {
        "Homepage": "https://github.com/higra/Higra"
    },
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "6493adb2a4adaea9048efda0afcce324bd123845bad13e8d9ecb28fe053e2e00",
                "md5": "27555c7da6410e313303b7b36b4a5603",
                "sha256": "0a86f991abecd2a3732719e8abd1a78232f4bbed3a021587221c29829adffb9c"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp310-cp310-macosx_10_9_x86_64.whl",
            "has_sig": false,
            "md5_digest": "27555c7da6410e313303b7b36b4a5603",
            "packagetype": "bdist_wheel",
            "python_version": "cp310",
            "requires_python": null,
            "size": 9115944,
            "upload_time": "2024-12-21T11:20:00",
            "upload_time_iso_8601": "2024-12-21T11:20:00.926728Z",
            "url": "https://files.pythonhosted.org/packages/64/93/adb2a4adaea9048efda0afcce324bd123845bad13e8d9ecb28fe053e2e00/higra-0.6.12-cp310-cp310-macosx_10_9_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "1e51f871162dc2143fba169c6a0197d146c75a8b3d617c0b082c614ad9a297d6",
                "md5": "8ef28bb63c56ecf9033f55db5a7c4373",
                "sha256": "d5e24d48d8da4f358b216ff4cad9f31018585de98b7794d22d42e516add454c4"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "has_sig": false,
            "md5_digest": "8ef28bb63c56ecf9033f55db5a7c4373",
            "packagetype": "bdist_wheel",
            "python_version": "cp310",
            "requires_python": null,
            "size": 10919526,
            "upload_time": "2024-12-21T11:08:58",
            "upload_time_iso_8601": "2024-12-21T11:08:58.600231Z",
            "url": "https://files.pythonhosted.org/packages/1e/51/f871162dc2143fba169c6a0197d146c75a8b3d617c0b082c614ad9a297d6/higra-0.6.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "31aead1e9b31b6ca68fa8c7c441617234eba5ae8f732909788148c95e6da5064",
                "md5": "d99e81dcaf0b86e1fe4f4bfae3ffc9e0",
                "sha256": "ffdfb904cb349bbb36a3b981b6bbcf2d6ab6fcc2fa75ecd03438a85a035f106a"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp311-cp311-macosx_10_9_x86_64.whl",
            "has_sig": false,
            "md5_digest": "d99e81dcaf0b86e1fe4f4bfae3ffc9e0",
            "packagetype": "bdist_wheel",
            "python_version": "cp311",
            "requires_python": null,
            "size": 9118106,
            "upload_time": "2024-12-21T11:17:57",
            "upload_time_iso_8601": "2024-12-21T11:17:57.572496Z",
            "url": "https://files.pythonhosted.org/packages/31/ae/ad1e9b31b6ca68fa8c7c441617234eba5ae8f732909788148c95e6da5064/higra-0.6.12-cp311-cp311-macosx_10_9_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "190a6eec40f898d501108128ee8230281cddd77418b4ed56b75f932127ac1eee",
                "md5": "02016672f54565438e9c15a4eb1c2b1c",
                "sha256": "eac8cb76f9491ccaee6d7dfdf81318baec94411a65554fa0c396e46707780eb8"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "has_sig": false,
            "md5_digest": "02016672f54565438e9c15a4eb1c2b1c",
            "packagetype": "bdist_wheel",
            "python_version": "cp311",
            "requires_python": null,
            "size": 10923798,
            "upload_time": "2024-12-21T11:10:34",
            "upload_time_iso_8601": "2024-12-21T11:10:34.106819Z",
            "url": "https://files.pythonhosted.org/packages/19/0a/6eec40f898d501108128ee8230281cddd77418b4ed56b75f932127ac1eee/higra-0.6.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "f25fbe53b0b6dbe3b3b9d20e3ba1b61950ec175f2a5a5532a6d058d774d90f2c",
                "md5": "f04a88353d5324211e105f373a067188",
                "sha256": "b2bc4b9d684dfed94cd7d82959420b07a503a94426754f4b2b31497901434d29"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp312-cp312-macosx_10_13_x86_64.whl",
            "has_sig": false,
            "md5_digest": "f04a88353d5324211e105f373a067188",
            "packagetype": "bdist_wheel",
            "python_version": "cp312",
            "requires_python": null,
            "size": 9025916,
            "upload_time": "2024-12-21T11:19:12",
            "upload_time_iso_8601": "2024-12-21T11:19:12.070437Z",
            "url": "https://files.pythonhosted.org/packages/f2/5f/be53b0b6dbe3b3b9d20e3ba1b61950ec175f2a5a5532a6d058d774d90f2c/higra-0.6.12-cp312-cp312-macosx_10_13_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "2ba004a13c136d0a63cfe6648d0e097d3a48155d96c30ececda00f2f2da67c67",
                "md5": "1273f810c9e025973bacff2902cdeb59",
                "sha256": "7d1c6a83cc647e24bbb4e0735b5c4f21e0bc4d13d6f67867936198e9a50fd46c"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "has_sig": false,
            "md5_digest": "1273f810c9e025973bacff2902cdeb59",
            "packagetype": "bdist_wheel",
            "python_version": "cp312",
            "requires_python": null,
            "size": 10959701,
            "upload_time": "2024-12-21T11:15:31",
            "upload_time_iso_8601": "2024-12-21T11:15:31.343881Z",
            "url": "https://files.pythonhosted.org/packages/2b/a0/04a13c136d0a63cfe6648d0e097d3a48155d96c30ececda00f2f2da67c67/higra-0.6.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "5368d40f01d3ce5ad49abe989666a96e04a514133666fa7a0a239b8bbb2f0827",
                "md5": "24b76924d20aeb19f1306efadec68fe9",
                "sha256": "80e6f4fe53895b41df6120afc0456f6f632d475743d8cf86b9ed9584895c343e"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp313-cp313-macosx_10_13_x86_64.whl",
            "has_sig": false,
            "md5_digest": "24b76924d20aeb19f1306efadec68fe9",
            "packagetype": "bdist_wheel",
            "python_version": "cp313",
            "requires_python": null,
            "size": 9025974,
            "upload_time": "2024-12-21T11:19:18",
            "upload_time_iso_8601": "2024-12-21T11:19:18.705064Z",
            "url": "https://files.pythonhosted.org/packages/53/68/d40f01d3ce5ad49abe989666a96e04a514133666fa7a0a239b8bbb2f0827/higra-0.6.12-cp313-cp313-macosx_10_13_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "5a0135e697aa1d87399a32943f549ef7d12ac87add9be79b98c674157dd2406b",
                "md5": "8a08a297b8509a25b34abc1268b7fd5c",
                "sha256": "26c8fc78388daab1d7406dc1f2997761801187234f104f51055b5ecbec886175"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "has_sig": false,
            "md5_digest": "8a08a297b8509a25b34abc1268b7fd5c",
            "packagetype": "bdist_wheel",
            "python_version": "cp313",
            "requires_python": null,
            "size": 10967460,
            "upload_time": "2024-12-21T11:19:14",
            "upload_time_iso_8601": "2024-12-21T11:19:14.844671Z",
            "url": "https://files.pythonhosted.org/packages/5a/01/35e697aa1d87399a32943f549ef7d12ac87add9be79b98c674157dd2406b/higra-0.6.12-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "96344d33fe5e8362ec29e797df959cb89d7e42c4f59238be8c26a73fb660da1d",
                "md5": "3c941db184e123cc16ac589340688d83",
                "sha256": "002e89127fb20523604ca7151942805ca824bbee0ff03f556da40ac7f4c7d6ec"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp39-cp39-macosx_10_9_x86_64.whl",
            "has_sig": false,
            "md5_digest": "3c941db184e123cc16ac589340688d83",
            "packagetype": "bdist_wheel",
            "python_version": "cp39",
            "requires_python": null,
            "size": 9116570,
            "upload_time": "2024-12-21T11:17:40",
            "upload_time_iso_8601": "2024-12-21T11:17:40.401222Z",
            "url": "https://files.pythonhosted.org/packages/96/34/4d33fe5e8362ec29e797df959cb89d7e42c4f59238be8c26a73fb660da1d/higra-0.6.12-cp39-cp39-macosx_10_9_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "e2beb15d43e24c6b428362d984df2f7b5bef0ea6bfb1962ac82361603ea9a105",
                "md5": "8b42196bbc090ff627fadaa2b1518168",
                "sha256": "5c101f79afe71ef59fe19b169812c4b542fd30c4ec70448df91e14f05e7c1457"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "has_sig": false,
            "md5_digest": "8b42196bbc090ff627fadaa2b1518168",
            "packagetype": "bdist_wheel",
            "python_version": "cp39",
            "requires_python": null,
            "size": 10936170,
            "upload_time": "2024-12-21T11:19:50",
            "upload_time_iso_8601": "2024-12-21T11:19:50.641127Z",
            "url": "https://files.pythonhosted.org/packages/e2/be/b15d43e24c6b428362d984df2f7b5bef0ea6bfb1962ac82361603ea9a105/higra-0.6.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "d4f0602d6a7c6dc3708725df98ebe57a87aa2d25168bcacc63587185b4c24814",
                "md5": "e0882cdc12554f85743626281fcb447b",
                "sha256": "207066f6dfec4121ef1b159699dbc7f9a7f5dccf23aa56d6f24abedc4a351274"
            },
            "downloads": -1,
            "filename": "higra-0.6.12-cp39-cp39-win_amd64.whl",
            "has_sig": false,
            "md5_digest": "e0882cdc12554f85743626281fcb447b",
            "packagetype": "bdist_wheel",
            "python_version": "cp39",
            "requires_python": null,
            "size": 6033316,
            "upload_time": "2024-12-21T11:28:03",
            "upload_time_iso_8601": "2024-12-21T11:28:03.327633Z",
            "url": "https://files.pythonhosted.org/packages/d4/f0/602d6a7c6dc3708725df98ebe57a87aa2d25168bcacc63587185b4c24814/higra-0.6.12-cp39-cp39-win_amd64.whl",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-12-21 11:20:00",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "higra",
    "github_project": "Higra",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "appveyor": true,
    "lcname": "higra"
}
        
Elapsed time: 0.35528s