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‎tests/packagedcode/data/pypi/metadata/v22/PKG-INFO‎

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Requires-Dist: bayesian_torch
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Requires-Dist: blitz-bayesian-pytorch
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Requires-Dist: tables>=3.7.0
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<div align="left"><img src="https://raw.githubusercontent.com/grimmlab/ForeTiS/master/docs/image/Logo_ForeTiS_Text.png" height="80"/></div>
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# ForeTiS: A Forecasting Time Series framework
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[![Python 3.8](https://img.shields.io/badge/Python-3.8-3776AB)](https://www.python.org/downloads/release/python-388/)
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ForeTiS is a Python framework that enables the rigorous training, comparison and analysis of time series forecasting for a variety of different models.
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ForeTiS includes multiple state-of-the-art prediction models or machine learning methods, respectively.
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These range from classical models, such as regularized linear regression over ensemble learners, e.g. XGBoost, to deep learning-based architectures, such as Multilayer Perceptron (MLP).
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To enable automatic hyperparameter optimization, we leverage state-of-the-art and efficient Bayesian optimization techniques.
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In addition, our framework is designed to allow an easy and straightforward integration and benchmarking of further prediction models.
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## Documentation
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For more information, installation guides, tutorials and much more, see our documentation: https://foretis.readthedocs.io/
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## Contributors
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This pipeline is developed and maintained by members of the [Bioinformatics lab](https://bit.cs.tum.de) lead by [Prof. Dr. Dominik Grimm](https://bit.cs.tum.de/team/dominik-grimm/):
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- [Josef Eiglsperger, M.Sc.](https://bit.cs.tum.de/team/josef-eiglsperger/)
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- [Florian Haselbeck, M.Sc.](https://bit.cs.tum.de/team/florian-haselbeck/)
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## Citation
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When using ForeTiS, please cite our publication:
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**ForeTiS: A comprehensive time series forecasting framework in Python.** <br />
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Josef Eiglsperger*, Florian Haselbeck* and Dominik G. Grimm. <br />
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*Machine Learning with Applications, 2023.* [doi: 10.1016/j.mlwa.2023.100467](https://doi.org/10.1016/j.mlwa.2023.100467) <br />
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**These authors have contributed equally to this work and share first authorship.* <br />

‎tests/packagedcode/data/pypi/metadata/v22/PKG-INFO-expected.json‎

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"qualifiers": {},
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"primary_language": "Python",
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"description": "state-of-the-art and easy-to-use time series forecasting\n<div align=\"left\"><img src=\"https://raw.githubusercontent.com/grimmlab/ForeTiS/master/docs/image/Logo_ForeTiS_Text.png\" height=\"80\"/></div>\n\n# ForeTiS: A Forecasting Time Series framework\n\n[![Python 3.8](https://img.shields.io/badge/Python-3.8-3776AB)](https://www.python.org/downloads/release/python-388/)\n\nForeTiS is a Python framework that enables the rigorous training, comparison and analysis of time series forecasting for a variety of different models. \nForeTiS includes multiple state-of-the-art prediction models or machine learning methods, respectively. \nThese range from classical models, such as regularized linear regression over ensemble learners, e.g. XGBoost, to deep learning-based architectures, such as Multilayer Perceptron (MLP). \nTo enable automatic hyperparameter optimization, we leverage state-of-the-art and efficient Bayesian optimization techniques. \nIn addition, our framework is designed to allow an easy and straightforward integration and benchmarking of further prediction models.\n\n## Documentation\nFor more information, installation guides, tutorials and much more, see our documentation: https://foretis.readthedocs.io/\n\n## Contributors\nThis pipeline is developed and maintained by members of the [Bioinformatics lab](https://bit.cs.tum.de) lead by [Prof. Dr. Dominik Grimm](https://bit.cs.tum.de/team/dominik-grimm/):\n- [Josef Eiglsperger, M.Sc.](https://bit.cs.tum.de/team/josef-eiglsperger/)\n- [Florian Haselbeck, M.Sc.](https://bit.cs.tum.de/team/florian-haselbeck/)\n\n## Citation\nWhen using ForeTiS, please cite our publication:\n\n**ForeTiS: A comprehensive time series forecasting framework in Python.** <br />\nJosef Eiglsperger*, Florian Haselbeck* and Dominik G. Grimm. <br />\n*Machine Learning with Applications, 2023.* [doi: 10.1016/j.mlwa.2023.100467](https://doi.org/10.1016/j.mlwa.2023.100467) <br />\n**These authors have contributed equally to this work and share first authorship.* <br />",
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"description": "state-of-the-art and easy-to-use time series forecasting",
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"release_date": null,
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"parties": [
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"datasource_id": "pypi_sdist_pkginfo",
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"purl": "pkg:pypi/[email protected]"
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}
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]
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]

‎tests/packagedcode/data/pypi/metadata/v23/PKG-INFO‎

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Requires-Dist: tomli>=1.2.2; python_version < '3.11'
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Requires-Dist: trove-classifiers
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Description-Content-Type: text/markdown
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# Hatchling
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<div align="center">
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<img src="https://raw.githubusercontent.com/pypa/hatch/master/docs/assets/images/logo.svg" alt="Hatch logo" width="500" role="img">
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| | |
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| --- | --- |
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| Package | [![PyPI - Version](https://img.shields.io/pypi/v/hatchling.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/hatchling/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/hatchling.svg?color=blue&label=Downloads&logo=pypi&logoColor=gold)](https://pypi.org/project/hatchling/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hatchling.svg?logo=python&label=Python&logoColor=gold)](https://pypi.org/project/hatchling/) |
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| Meta | [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch) [![linting - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![code style - Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![types - Mypy](https://img.shields.io/badge/types-Mypy-blue.svg)](https://github.com/python/mypy) [![License - MIT](https://img.shields.io/badge/license-MIT-9400d3.svg)](https://spdx.org/licenses/) [![GitHub Sponsors](https://img.shields.io/github/sponsors/ofek?logo=GitHub%20Sponsors&style=social)](https://github.com/sponsors/ofek) |
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</div>
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-----
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This is the extensible, standards compliant build backend used by [Hatch](https://github.com/pypa/hatch).
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## Usage
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The following snippet must be present in your project's `pyproject.toml` file in order to use Hatchling as your build backend:
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```toml
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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```
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Then a build frontend like [pip](https://github.com/pypa/pip), [build](https://github.com/pypa/build), or Hatch itself can build or install your project automatically:
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```console
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# install using pip
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pip install /path/to/project
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# build
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python -m build /path/to/project
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# build with Hatch
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hatch build /path/to/project
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```
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## Documentation
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- [Project metadata](https://hatch.pypa.io/latest/config/metadata/)
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- [Dependencies](https://hatch.pypa.io/latest/config/dependency/)
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- [Packaging](https://hatch.pypa.io/latest/config/build/)

‎tests/packagedcode/data/pypi/metadata/v23/PKG-INFO-expected.json‎

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"qualifiers": {},
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"primary_language": "Python",
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"description": "Modern, extensible Python build backend\n# Hatchling\n\n<div align=\"center\">\n\n<img src=\"https://raw.githubusercontent.com/pypa/hatch/master/docs/assets/images/logo.svg\" alt=\"Hatch logo\" width=\"500\" role=\"img\">\n\n| | |\n| --- | --- |\n| Package | [![PyPI - Version](https://img.shields.io/pypi/v/hatchling.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/hatchling/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/hatchling.svg?color=blue&label=Downloads&logo=pypi&logoColor=gold)](https://pypi.org/project/hatchling/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hatchling.svg?logo=python&label=Python&logoColor=gold)](https://pypi.org/project/hatchling/) |\n| Meta | [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch) [![linting - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![code style - Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![types - Mypy](https://img.shields.io/badge/types-Mypy-blue.svg)](https://github.com/python/mypy) [![License - MIT](https://img.shields.io/badge/license-MIT-9400d3.svg)](https://spdx.org/licenses/) [![GitHub Sponsors](https://img.shields.io/github/sponsors/ofek?logo=GitHub%20Sponsors&style=social)](https://github.com/sponsors/ofek) |\n\n</div>\n\n-----\n\nThis is the extensible, standards compliant build backend used by [Hatch](https://github.com/pypa/hatch).\n\n## Usage\n\nThe following snippet must be present in your project's `pyproject.toml` file in order to use Hatchling as your build backend:\n\n```toml\n[build-system]\nrequires = [\"hatchling\"]\nbuild-backend = \"hatchling.build\"\n```\n\nThen a build frontend like [pip](https://github.com/pypa/pip), [build](https://github.com/pypa/build), or Hatch itself can build or install your project automatically:\n\n```console\n# install using pip\npip install /path/to/project\n\n# build\npython -m build /path/to/project\n\n# build with Hatch\nhatch build /path/to/project\n```\n\n## Documentation\n\n- [Project metadata](https://hatch.pypa.io/latest/config/metadata/)\n- [Dependencies](https://hatch.pypa.io/latest/config/dependency/)\n- [Packaging](https://hatch.pypa.io/latest/config/build/)",
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"description": "Modern, extensible Python build backend",
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"parties": [
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"datasource_id": "pypi_sdist_pkginfo",
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"purl": "pkg:pypi/[email protected]"
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}
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]
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]

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