{"id":25306530,"url":"https://github.com/lamalab-org/mofdscribe","last_synced_at":"2025-10-28T10:31:50.909Z","repository":{"id":58167745,"uuid":"471285607","full_name":"lamalab-org/mofdscribe","owner":"lamalab-org","description":"An ecosystem for digital reticular chemistry","archived":false,"fork":false,"pushed_at":"2024-09-10T16:42:21.000Z","size":12412,"stargazers_count":51,"open_issues_count":121,"forks_count":9,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-10-20T16:43:24.363Z","etag":null,"topics":["artificial-intelligence","benchmark","data-science","descriptors","featurization","hacktoberfest","machine-learning","metal-organic-frameworks","metrics","ml","mof","porous-materials","reticular-chemistry","splitting"],"latest_commit_sha":null,"homepage":"https://mofdscribe.readthedocs.io/en/latest/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lamalab-org.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.rst","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-03-18T08:13:49.000Z","updated_at":"2025-10-13T06:45:19.000Z","dependencies_parsed_at":"2024-09-10T18:21:11.175Z","dependency_job_id":"18ff1b1e-0787-4e47-a0d8-9d14795f85ae","html_url":"https://github.com/lamalab-org/mofdscribe","commit_stats":{"total_commits":454,"total_committers":7,"mean_commits":64.85714285714286,"dds":"0.22687224669603523","last_synced_commit":"b5ce7a504a959a8ac0483f1e2aae3ca8b2592936"},"previous_names":["lamalab-org/mofdscribe","kjappelbaum/mofdscribe"],"tags_count":8,"template":false,"template_full_name":null,"purl":"pkg:github/lamalab-org/mofdscribe","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lamalab-org%2Fmofdscribe","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lamalab-org%2Fmofdscribe/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lamalab-org%2Fmofdscribe/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lamalab-org%2Fmofdscribe/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lamalab-org","download_url":"https://codeload.github.com/lamalab-org/mofdscribe/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lamalab-org%2Fmofdscribe/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281424764,"owners_count":26499021,"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","status":"online","status_checked_at":"2025-10-28T02:00:06.022Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["artificial-intelligence","benchmark","data-science","descriptors","featurization","hacktoberfest","machine-learning","metal-organic-frameworks","metrics","ml","mof","porous-materials","reticular-chemistry","splitting"],"created_at":"2025-02-13T10:14:52.395Z","updated_at":"2025-10-28T10:31:49.611Z","avatar_url":"https://github.com/lamalab-org.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/kjappelbaum/mofdscribe/raw/main/docs/source/figures/logo.png\" height=\"300\"\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://github.com/kjappelbaum/mofdscribe/actions?query=workflow%3ATests\"\u003e\n        \u003cimg alt=\"Tests\" src=\"https://github.com/kjappelbaum/mofdscribe/workflows/Tests/badge.svg\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://pypi.org/project/mofdscribe\"\u003e\n        \u003cimg alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/mofdscribe\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://pypi.org/project/mofdscribe\"\u003e\n        \u003cimg alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/mofdscribe\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/kjappelbaum/mofdscribe/blob/main/LICENSE\"\u003e\n        \u003cimg alt=\"PyPI - License\" src=\"https://img.shields.io/pypi/l/mofdscribe\" /\u003e\n    \u003c/a\u003e\n    \u003ca href='https://mofdscribe.readthedocs.io/en/latest/?badge=latest'\u003e\n        \u003cimg src='https://readthedocs.org/projects/mofdscribe/badge/?version=latest' alt='Documentation Status' /\u003e\n    \u003c/\n    \u003ca href='https://github.com/psf/black'\u003e\n        \u003cimg src='https://img.shields.io/badge/code%20style-black-000000.svg' alt='Code style: black' /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://matsci.org/c/mofdscribe/56\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/matsci-discuss%20%26%20get%20help-yellowgreen\" alt=\"Matsci\"\u003e\n    \u003ca href='http://commitizen.github.io/cz-cli/'\u003e\n        \u003cimg src='https://img.shields.io/badge/commitizen-friendly-brightgreen.svg' alt='Commitizen friendly' /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://colab.research.google.com/github/kjappelbaum/mofdscribe/\"\u003e\n        \u003cimg src=https://colab.research.google.com/assets/colab-badge.svg \u003e\n    \u003c/a\u003e\n\u003c/p\u003e\n\nFeaturizing metal-organic frameworks (MOFs) made simple! This package builds on the power of [matminer](https://hackingmaterials.lbl.gov/matminer/) to make featurization of MOFs as easy as possible. Now, you can use features that are mostly used for porous materials in the same way as all other matminer featurizers.\nmofdscribe additionally includes routines that help with model validation.\n\n## 💪 Getting Started\n\n```python\n\nfrom mofdscribe.featurizers.chemistry import RACS\nfrom pymatgen.core import Structure\n\nstructure = Structure.from_file(\u003cmy_cif.cif\u003e)\nfeaturizer = RACS()\nracs_features = featurizer.featurize(structure)\n```\n\n## 🚀 Installation\n\nWhile we are in the process of trying to make mofdscribe work on all operating system (we're waiting for conda recipies getting merged),\nit is currently not easy on Windows (and there might be potential issues on ARM-based Macs).\nFor this reason, we recommend installing mofdscribe on a UNIX machine.\n\n\u003c!-- The most recent release can be installed from\n[PyPI](https://pypi.org/project/mofdscribe/) with:\n\n```bash\n$ pip install mofdscribe\n``` --\u003e\n\u003c!--\n\nThe most recent code and data can be installed directly from GitHub with:\n\n```bash\n$ pip install git+https://github.com/kjappelbaum/mofdscribe.git\n``` --\u003e\n\nTo install in development mode, use the following:\n\n```bash\ngit clone git+https://github.com/kjappelbaum/mofdscribe.git\ncd mofdscribe\npip install -e .\n```\n\nif you want to use all utilities, you can use the `all` extra: `pip install -e \".[all]\"`\n\nWe depend on many other external tools. Most external tools are automatically installed if you install mofdscribe via conda:\n\n```bash\nconda install -c conda-forge mofdscribe\n```\n\n## 👐 Contributing\n\nContributions, whether filing an issue, making a pull request, or forking, are appreciated. See\n[CONTRIBUTING.rst](https://github.com/kjappelbaum/mofdscribe/blob/master/CONTRIBUTING.rst) for more information on getting involved.\n\n## 👋 Attribution\n\n### ⚖️ License\n\nThe code in this package is licensed under the MIT License.\n\n\n### 📖 Citation\n\nSee the [ChemRxiv preprint](https://chemrxiv.org/engage/chemrxiv/article-details/630d1f6f90802d52c16eceb2).\n\n```\n@article{Jablonka_2022,\n    doi = {10.26434/chemrxiv-2022-4g7rx},\n    url = {https://doi.org/10.26434%2Fchemrxiv-2022-4g7rx},\n    year = 2022,\n    month = {sep},\n    publisher = {American Chemical Society ({ACS})},\n    author = {Kevin Maik Jablonka and Andrew S. Rosen and Aditi S. Krishnapriyan and Berend Smit},\n    title = {An ecosystem for digital reticular chemistry}\n}\n```\n\n\n\u003c!--\n### 🎁 Support\n\nThis project has been supported by the following organizations (in alphabetical order):\n\n- [Harvard Program in Therapeutic Science - Laboratory of Systems Pharmacology](https://hits.harvard.edu/the-program/laboratory-of-systems-pharmacology/)\n\n--\u003e\n\n### 💰 Funding\n\nThe research was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme ([grant agreement 666983, MaGic](https://cordis.europa.eu/project/id/666983)), by the [NCCR-MARVEL](https://www.nccr-marvel.ch/), funded by the Swiss National Science Foundation, and by the Swiss National Science Foundation (SNSF) under Grant 200021_172759.\n\n### 🍪 Cookiecutter\n\nThis package was created with [@audreyfeldroy](https://github.com/audreyfeldroy)'s\n[cookiecutter](https://github.com/cookiecutter/cookiecutter) package using [@cthoyt](https://github.com/cthoyt)'s\n[cookiecutter-snekpack](https://github.com/cthoyt/cookiecutter-snekpack) template.\n\n## 🛠️ For Developers\n\n\u003cdetails\u003e\n  \u003csummary\u003eSee developer instructions\u003c/summary\u003e\n\nThe final section of the README is for if you want to get involved by making a code contribution.\n\n### ❓ Testing\n\nAfter cloning the repository and installing `tox` with `pip install tox`, the unit tests in the `tests/` folder can be\nrun reproducibly with:\n\n```shell\ntox\n```\n\nAdditionally, these tests are automatically re-run with each commit in a [GitHub Action](https://github.com/kjappelbaum/mofdscribe/actions?query=workflow%3ATests).\n\n### 📦 Making a Release\n\nAfter installing the package in development mode and installing\n`tox` with `pip install tox`, the commands for making a new release are contained within the `finish` environment\nin `tox.ini`. Run the following from the shell:\n\n```shell\ntox -e finish\n```\n\nThis script does the following:\n\n1. Uses BumpVersion to switch the version number in the `setup.cfg` and\n   `src/mofdscribe/version.py` to not have the `-dev` suffix\n2. Packages the code in both a tar archive and a wheel\n3. Uploads to PyPI using `twine`. Be sure to have a `.pypirc` file configured to avoid the need for manual input at this\n   step\n4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.\n5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can\n   use `tox -e bumpversion minor` after.\n\n\u003c/details\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flamalab-org%2Fmofdscribe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flamalab-org%2Fmofdscribe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flamalab-org%2Fmofdscribe/lists"}