labtoolbox 2.0.2

LabToolbox is a collection of tools for the analysis and processing of experimental data in scientific research.

LabToolbox

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.

The example.ipynb notebook, available on the package's GitHub page, includes usage examples for the main functions of LabToolbox.

Installation

You can install LabToolbox easily using pip:

pip install LabToolbox

Library Structure

The LabToolbox package is organized into multiple submodules, each dedicated to a specific aspect of experimental data analysis:

• LabToolbox.utils: A collection of helper functions for tasks like data formatting and general-purpose utilities used throughout the package.

• 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.

• LabToolbox.fit: Routines for linear and non-linear curve fitting, with support for uncertainty-aware methods.

• LabToolbox.uncertainty: Methods for estimating and propagating uncertainties in experimental contexts, allowing quantification of how input errors affect model outputs.

Documentation

Detailed documentation for all modules and functions is available in the GitHub Wiki. The wiki includes function descriptions, usage examples, and practical guidance to help you get the most out of the library.

Citation

If you use this software, please cite it using the metadata in CITATION.cff. You can also use GitHub’s “Cite this repository” feature (available in the sidebar of the repository page).

License

MIT License – See the LICENSE.txt file.

Code of Conduct

This project includes a Code of Conduct, which all users and contributors are expected to read and follow.

Additionally, 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.

Disclaimer

This package makes use of the uncertainty_class library, available on GitHub, which provides functionality for uncertainty propagation in calculations. Manual installation is not required, as it is included as a module within LabToolbox.

The functions my_cov, my_var, my_mean, my_line and y_estrapolato, found in the modules LabToolbox.utils and LabToolbox.fit, originate from the my_lib_santanastasio library, developed by F. Santanastasio (professor of the Laboratorio di Meccanica course at the University of Rome “La Sapienza”), available at this link.

Tools such as lin_fit and model_fit include an option to display fit residuals. This functionality incorporates elements from the VoigtFit library. The relevant portions of code are clearly marked in the source with a dedicated comment.