{"id":37078429,"url":"https://github.com/giusesorrentino/labtoolbox","last_synced_at":"2026-01-14T09:10:48.256Z","repository":{"id":290201678,"uuid":"784196890","full_name":"giusesorrentino/labtoolbox","owner":"giusesorrentino","description":"LabToolbox is a collection of tools for the analysis and processing of experimental data in scientific research.","archived":false,"fork":false,"pushed_at":"2025-07-02T15:20:39.000Z","size":3853,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-05T06:53:00.965Z","etag":null,"topics":["bayesian-inference","curve-fitting","data-analysis-python","data-visualisation","error-propagation","experimental-physics","fourier-analysis","linear-algebra","open-source","scientific-computing-library","signal-processing","statistics"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/LabToolbox/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/giusesorrentino.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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":"2024-04-09T11:32:47.000Z","updated_at":"2025-07-02T15:20:42.000Z","dependencies_parsed_at":null,"dependency_job_id":"e396f706-c069-486e-83ff-869b8b1f9475","html_url":"https://github.com/giusesorrentino/labtoolbox","commit_stats":null,"previous_names":["giusesorrentino/labtoolbox"],"tags_count":8,"template":false,"template_full_name":null,"purl":"pkg:github/giusesorrentino/labtoolbox","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giusesorrentino%2Flabtoolbox","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giusesorrentino%2Flabtoolbox/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giusesorrentino%2Flabtoolbox/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giusesorrentino%2Flabtoolbox/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/giusesorrentino","download_url":"https://codeload.github.com/giusesorrentino/labtoolbox/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/giusesorrentino%2Flabtoolbox/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28414834,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-14T08:38:59.149Z","status":"ssl_error","status_checked_at":"2026-01-14T08:38:43.588Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["bayesian-inference","curve-fitting","data-analysis-python","data-visualisation","error-propagation","experimental-physics","fourier-analysis","linear-algebra","open-source","scientific-computing-library","signal-processing","statistics"],"created_at":"2026-01-14T09:10:47.461Z","updated_at":"2026-01-14T09:10:48.250Z","avatar_url":"https://github.com/giusesorrentino.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!-- # LabToolbox --\u003e\n\n[![PyPI - Version](https://img.shields.io/pypi/v/LabToolbox?label=PyPI)](https://pypi.org/project/LabToolbox/)\n![Python Versions](https://img.shields.io/pypi/pyversions/LabToolbox)\n![PyPI - Downloads](https://img.shields.io/pypi/dm/LabToolbox)\n[![License](https://img.shields.io/pypi/l/LabToolbox)](https://github.com/giusesorrentino/LabToolbox/blob/master/LICENSE.txt)\n[![GitHub Issues](https://img.shields.io/github/issues/giusesorrentino/LabToolbox)](https://github.com/giusesorrentino/LabToolbox/issues)\n[![GitHub Pull Requests](https://img.shields.io/github/issues-pr/giusesorrentino/LabToolbox)](https://github.com/giusesorrentino/LabToolbox/pulls)\n![GitHub Repo stars](https://img.shields.io/github/stars/giusesorrentino/LabToolbox)\n![GitHub Forks](https://img.shields.io/github/forks/giusesorrentino/LabToolbox)\n\n\u003cp align=\"left\"\u003e\n  \u003cpicture\u003e\n    \u003csource srcset=\"https://github.com/giusesorrentino/LabToolbox/raw/master/docs/logo_dark.png\" media=\"(prefers-color-scheme: dark)\"\u003e\n    \u003cimg src=\"https://github.com/giusesorrentino/LabToolbox/raw/master/docs/logo_light.png\" alt=\"LabToolbox logo\" width=\"700\"\u003e\n  \u003c/picture\u003e\n\u003c/p\u003e\n\n**LabToolbox** is a Python package that provides a collection of useful tools for laboratory data analysis. It offers intuitive and optimized functions for curve fitting, uncertainty propagation, data handling, and graphical visualization, enabling a faster and more rigorous approach to experimental data processing. Designed for students, researchers, and anyone working with experimental data, it combines ease of use with methodological accuracy.\n\n\u003c!-- The `example.ipynb` notebook, available on the package's [GitHub page](https://github.com/giusesorrentino/LabToolbox/blob/main/example.ipynb), includes usage examples for the main functions of `LabToolbox`. --\u003e\n\n## Installation\n\nYou can install **LabToolbox** from PyPI using `pip`:\n\n```bash\npip install LabToolbox\n```\n\u003c!-- \nAlternatively, you can install the latest development version directly from GitHub:\n\n```bash\npip install git+https://github.com/giusesorrentino/LabToolbox.git\n``` --\u003e\n\n\u003c!-- If you prefer to clone the repository and install manually: --\u003e\nAlternatively, you can clone the repository and install it manually:\n\n```bash\ngit clone https://github.com/giusesorrentino/LabToolbox.git\ncd LabToolbox\npip install .\n```\n\n## Dependencies\n\nLabToolbox relies on a set of well-established scientific Python libraries. When installed via `pip`, these dependencies are automatically handled. However, for reference or manual setup, here is the list of core dependencies:\n\n- **numpy** – fundamental package for numerical computing.\n- **scipy** – scientific and technical computing tools.\n- **matplotlib** – for plotting and data visualization.\n- **statsmodels** – statistical modeling and inference.\n- **emcee** – affine-invariant ensemble sampler for MCMC.\n- **corner** – corner plots for visualizing multidimensional distributions.\n- **lmfit** – flexible curve-fitting with parameter constraints.\n- **astropy** – core astronomy library for Python.\n\n\u003e **Note**: Up to version 2.0.3, the package was tested and validated on Python 3.9.6. Starting from version 3.0.0, it has been tested only on Python 3.13.3. While compatibility with earlier Python versions (≥ 3.9.6) is still expected, it is no longer officially guaranteed. The minimum required version remains Python 3.9.\n\n## Library Structure\n\nThe **LabToolbox** package is organized into multiple submodules, each dedicated to a specific aspect of experimental data analysis:\n\n\u003c!-- ### `LabToolbox.utils`\nA collection of helper functions for tasks like data formatting and general-purpose utilities used throughout the package.\n\n### `LabToolbox.stats`\nStatistical tools for experimental data analysis, including generation of synthetic datasets, histogram construction, outlier removal, residual analysis (normality, skewness, kurtosis), and likelihood/posterior computation for parametric models.\n\n### `LabToolbox.fit`\nRoutines for linear and non-linear curve fitting, with support for uncertainty-aware methods.\n\n### `LabToolbox.uncertainty`\nMethods for estimating and propagating uncertainties in experimental contexts, allowing quantification of how input errors affect model outputs.\n\n### `LabToolbox.signals`\nSignal analysis tools tailored for laboratory experiments, featuring frequency domain analysis and post-processing of acquired data. --\u003e\n- **LabToolbox.fit**: Routines for linear and non-linear curve fitting.\n\n- **LabToolbox.signals**: Signal analysis tools tailored for laboratory experiments, featuring frequency domain analysis and post-processing of acquired data.\n\n- **LabToolbox.stats**: Statistical tools for experimental data analysis, including generation of synthetic datasets, histogram construction, outlier removal, residual analysis (normality, skewness, kurtosis), and likelihood/posterior computation for parametric models.\n\n- **LabToolbox.uncertainty**: Methods for estimating and propagating uncertainties in experimental contexts, allowing quantification of how input errors affect model outputs.\n\n- **LabToolbox.utils**: A collection of helper functions for tasks like data formatting and general-purpose utilities used throughout the package.\n\n## Documentation\n\nDetailed documentation for all modules and functions is available in the [GitHub Wiki](https://github.com/giusesorrentino/LabToolbox/wiki). The wiki includes function descriptions, usage examples, and practical guidance to help you get the most out of the library.\n\n## Citation\n\nIf you use this software, please cite it using the metadata in [CITATION.cff](https://github.com/giusesorrentino/LabToolbox/blob/main/CITATION.cff). You can also use GitHub’s “Cite this repository” feature (available in the sidebar of the repository page).\n\n\u003c!-- ## License \n\nMIT License – See the [LICENSE.txt](https://github.com/giusesorrentino/LabToolbox/blob/main/LICENSE.txt) file. --\u003e\n\n## Code of Conduct\n\nThis project includes a [Code of Conduct](https://github.com/giusesorrentino/LabToolbox/blob/main/CODE_OF_CONDUCT.md), which all users and contributors are expected to read and follow.\n\nAdditionally, the Code of Conduct contains a section titled “Author’s Ethical Requests” outlining the author's personal expectations regarding responsible and respectful use, especially in commercial or large-scale contexts. While not legally binding, these principles reflect the spirit in which this software was developed, and users are kindly asked to consider them when using the project.\n\n## Disclaimer\n\nLabToolbox makes use of the **uncertainty_class** package, available on [GitHub](https://github.com/yiorgoskost/Uncertainty-Propagation/tree/master), which provides functionality for uncertainty propagation in calculations. Manual installation is not required, as it is included as a module within LabToolbox.\n\nSome utility functions — namely `my_cov`, `my_var`, `my_mean`, `my_line`, and `y_estrapolato` — are adapted from the [**my_lib_santanastasio**](https://baltig.infn.it/LabMeccanica/PythonJupyter) package, originally developed by F. Santanastasio for the *Laboratorio di Meccanica* course at the University of Rome “La Sapienza”.\n\nAdditionally, the `lin_fit` and `model_fit` functions provide the option to visualize fit residuals. This feature draws inspiration from the [**VoigtFit**](https://github.com/jkrogager/VoigtFit) library, with the relevant portions of code clearly annotated within the source.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgiusesorrentino%2Flabtoolbox","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgiusesorrentino%2Flabtoolbox","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgiusesorrentino%2Flabtoolbox/lists"}