Labtoolbox (stylized as 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.

Installation

You can install labtoolbox from PyPI using pip:

pip install labtoolbox

Alternatively, you can clone the repository and install it manually:

git clone https://github.com/giusesorrentino/labtoolbox.git
cd labtoolbox
pip install .

As of now, the package is not available on conda-forge. If you’re using Anaconda or Jupyter Notebook, you can still install labtoolbox by running the following code in the first cell of your notebook (only once per environment, not every time or for each .ipynb file):

!pip install labtoolbox

and then you can import the library.

Important Note

As of version 3.1.0, the library must be imported using lowercase:

import labtoolbox

The previous form with capitalization (import LabToolbox) is no longer supported.

If you have already installed a previous version of the package, you may encounter an issue where only the old import works, while the new one fails. This is typically caused by residual files from the previous installation. To resolve this, navigate to your site-packages directory (where Python packages are installed), then either:

• Rename the old package folder (e.g., from LabToolbox/ to labtoolbox/);
• Delete the old folder along with any associated metadata directories (e.g., .egg-info) and reinstall the new version using pip.

Dependencies

LabToolbox 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:

• numpy – fundamental package for numerical computing.
• scipy – scientific and technical computing tools.
• matplotlib – for plotting and data visualization.

Note: Up to version 2.0.3, the package was tested and validated on Python 3.9. Starting from version 3.1.0, it has been tested only on Python 3.11. While compatibility with earlier Python versions (≥ 3.9.6) is still expected, it is no longer officially guaranteed. The minimum required version remains Python 3.9.6.

Library Structure

The labtoolbox package is organized into multiple submodules, each dedicated to a specific aspect of experimental data analysis. Below is an overview of the submodules and their functionalities:

Subpackage	Description
numerical	General-purpose numerical routines, such as numerical integration and root finding
signals	Tools for post-processing and analysis of acquired signals.
special	Special mathematical functions.
stats	Tools for statistical analysis and data modeling.
utils	A collection of general-purpose utilities used throughout the package.

Documentation

Complete API documentation and tutorials are available at ReadtheDocs. The documentation is automatically built from the source code docstrings and includes:

• API Reference: Detailed documentation for all modules and functions
• Deprecation Notices: Information about removed and deprecated features

• Module Overview: Signal processing, statistics, numerical methods, uncertainty quantification, and utilities

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).

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

Labtoolbox makes use of the uncertainty_class package, 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.

Some utility functions are adapted from the my_lib_santanastasio package, available at this link, originally developed by F. Santanastasio for the Laboratorio di Meccanica course at the University of Rome “La Sapienza”.

Additionally, the lin_fit and model_fit functions provide the option to visualize fit residuals. This feature draws inspiration from the VoigtFit package, available on GitHub, with the relevant portions of code clearly annotated within the source.