{"id":28653848,"url":"https://github.com/cbueth/infomeasure","last_synced_at":"2025-08-25T11:04:54.419Z","repository":{"id":288253615,"uuid":"810694665","full_name":"cbueth/infomeasure","owner":"cbueth","description":"Python package for calculating various information measures, including entropy, mutual information, transfer entropy, and more, with support for both discrete and continuous variables.","archived":false,"fork":false,"pushed_at":"2025-05-31T13:38:27.000Z","size":2912,"stargazers_count":9,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-08T06:17:28.735Z","etag":null,"topics":["complex-networks","conditional-probability","data-science","entropy","entropy-measures","information-theory","machine-learning","mathematical-modelling","mutual-information","numpy","physics","research","statistical-analysis","transfer-entropy"],"latest_commit_sha":null,"homepage":"https://infomeasure.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cbueth.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-06-05T07:31:26.000Z","updated_at":"2025-06-04T07:33:06.000Z","dependencies_parsed_at":"2025-04-16T14:23:26.766Z","dependency_job_id":"682f4194-8d53-4e99-bbcf-211feb8f65f5","html_url":"https://github.com/cbueth/infomeasure","commit_stats":null,"previous_names":["cbueth/infomeasure"],"tags_count":10,"template":false,"template_full_name":null,"purl":"pkg:github/cbueth/infomeasure","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbueth%2Finfomeasure","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbueth%2Finfomeasure/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbueth%2Finfomeasure/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbueth%2Finfomeasure/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cbueth","download_url":"https://codeload.github.com/cbueth/infomeasure/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbueth%2Finfomeasure/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259599319,"owners_count":22882352,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["complex-networks","conditional-probability","data-science","entropy","entropy-measures","information-theory","machine-learning","mathematical-modelling","mutual-information","numpy","physics","research","statistical-analysis","transfer-entropy"],"created_at":"2025-06-13T07:02:23.168Z","updated_at":"2025-08-25T11:04:54.389Z","avatar_url":"https://github.com/cbueth.png","language":"Python","readme":"\u003cdiv style=\"text-align: center; max-width: 700px; margin: 0 auto;\"\u003e\n  \u003ca href=\"https://infomeasure.readthedocs.io/\"\u003e\n    \u003cpicture\u003e\n      \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/cbueth/infomeasure/refs/heads/main/docs/_static/im_logo_transparent.png\"\u003e\n      \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/cbueth/infomeasure/refs/heads/main/docs/_static/im_logo_transparent_dark.png\"\u003e\n      \u003cimg src=\"https://raw.githubusercontent.com/cbueth/infomeasure/refs/heads/main/docs/_static/im_logo_transparent.png\" style=\"max-width: 100%; height: auto;\" alt=\"infomeasure logo\"\u003e\n    \u003c/picture\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n\u003ca href=\"\"\u003e[![Documentation](https://readthedocs.org/projects/infomeasure/badge/)](https://infomeasure.readthedocs.io/)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![PyPI Version](https://badge.fury.io/py/infomeasure.svg)](https://pypi.org/project/infomeasure/)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![Python Version](https://img.shields.io/pypi/pyversions/infomeasure)](https://pypi.org/project/infomeasure/)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![Anaconda Version](https://anaconda.org/conda-forge/infomeasure/badges/version.svg)](https://anaconda.org/conda-forge/infomeasure)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![PyPI Downloads](https://static.pepy.tech/badge/infomeasure)](https://pepy.tech/projects/infomeasure)\u003c/a\u003e\n\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n\u003ca href=\"\"\u003e[![arXiv](https://img.shields.io/badge/arXiv-2505.14696-b31b1b.svg)](https://arxiv.org/abs/2505.14696)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15241810.svg)](https://doi.org/10.5281/zenodo.15241810)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-1.2-4baaaa.svg)](CODE_OF_CONDUCT.md)\u003c/a\u003e\n\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n\u003ca href=\"\"\u003e[![pipeline status](https://gitlab.ifisc.uib-csic.es/carlson/infomeasure/badges/main/pipeline.svg)](https://gitlab.ifisc.uib-csic.es/carlson/infomeasure/-/commits/main)\u003c/a\u003e\n\u003ca href=\"\"\u003e[![coverage report](https://gitlab.ifisc.uib-csic.es/carlson/infomeasure/badges/main/coverage.svg)](https://gitlab.ifisc.uib-csic.es/carlson/infomeasure/-/jobs)\u003c/a\u003e\n\n\u003c/div\u003e\n\nContinuous and discrete entropy and information measures using different estimation\ntechniques.\n\n---\n\nFor details on how to use this package, see the\n[Guide](https://infomeasure.readthedocs.io/en/latest/guide/) or\nthe [Documentation](https://infomeasure.readthedocs.io/).\n\n## Setup\n\nThis package can be installed from PyPI using pip:\n\n```bash\npip install infomeasure\n```\n\nThis will automatically install all the necessary dependencies as specified in the\n`pyproject.toml` file. It is recommended to use a virtual environment, e.g. using\n`conda`, `mamba` or `micromamba` (they can be used interchangeably).\n`infomeasure` can be installed from the `conda-forge` channel.\n\n```bash\nconda create -n im_env -c conda-forge python\nconda activate im_env\nconda install -c conda-forge infomeasure\n```\n\n## Development Setup\n\nFor development, we recommend using `micromamba` to create a virtual\nenvironment (`conda` or `mamba` also work)\nand installing the package in editable mode.\nAfter cloning the repository, navigate to the root folder and\ncreate the environment with the desired python version and the dependencies.\n\n```bash\nmicromamba create -n im_env -c conda-forge python\nmicromamba activate im_env\n```\n\nTo let `micromamba` handle the dependencies, use the `requirements` files\n\n```bash\nmicromamba install -f requirements/build_requirements.txt \\\n  -f requirements/linter_requirements.txt \\\n  -f requirements/test_requirements.txt \\\n  -f requirements/doc_requirements.txt\npip install --no-build-isolation --no-deps -e .\n```\n\nAlternatively, if you prefer to use `pip`, installing the package in editable mode will\nalso install the\ndevelopment dependencies.\n\n```bash\npip install -e \".[all]\"\n```\n\nNow, the package can be imported and used in the python environment, from anywhere on\nthe system if the environment is activated.\nFor new changes, the repository only needs to be updated, but the package does not need\nto be reinstalled.\n\n## Set up Jupyter kernel\n\nIf you want to use `infomeasure` with its environment `im_env` in Jupyter, run:\n\n```bash\npip install --user ipykernel\npython -m ipykernel install --user --name=im_env\n```\n\nThis allows you to run Jupyter with the kernel `im_env` (Kernel \u003e Change Kernel \u003e\nim_env)\n\n## Acknowledgments\n\nThis project has received funding from the European Research Council (ERC) under the\nEuropean Union's Horizon 2020 research and innovation programme (grant agreement No\n851255).\nThis work was partially supported by the María de Maeztu project CEX2021-001164-M funded\nby the MICIU/AEI/10.13039/501100011033 and FEDER, EU.\n","funding_links":[],"categories":["Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbueth%2Finfomeasure","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcbueth%2Finfomeasure","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbueth%2Finfomeasure/lists"}