labtoolbox 2.0.1

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

LabToolbox

LabToolbox is a Python library 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 library'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 library is organized into multiple submodules, each dedicated to a specific aspect of experimental data analysis:

• LabToolbox.utils
  A collection of helper functions for general tasks that support various parts of the library, such as data formatting and other utility operations.

• LabToolbox.stats Provides statistical tools for experimental data analysis. This includes functions for generating synthetic datasets from known distributions, creating histograms, removing outliers, performing residual analysis with statistical tests (e.g., normality, skewness, kurtosis), and computing likelihoods and posterior probabilities for parametric models.

• LabToolbox.fit
  Provides routines for linear and non-linear curve fitting, including uncertainty-aware methods.

• LabToolbox.uncertainty
  Provides tools for estimating and propagating uncertainties in experimental data and models, helping to quantify the effect of input errors on the final results.

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 and Ethical Use

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.basics 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.