https://github.com/giusesorrentino/labtoolbox
LabToolbox is a collection of tools for the analysis and processing of experimental data in scientific research.
https://github.com/giusesorrentino/labtoolbox
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
Last synced: 2 months ago
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LabToolbox is a collection of tools for the analysis and processing of experimental data in scientific research.
- Host: GitHub
- URL: https://github.com/giusesorrentino/labtoolbox
- Owner: giusesorrentino
- License: bsd-3-clause
- Created: 2024-04-09T11:32:47.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2025-07-02T15:20:39.000Z (9 months ago)
- Last Synced: 2025-09-05T06:53:00.965Z (6 months ago)
- 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
- Language: Python
- Homepage: https://pypi.org/project/LabToolbox/
- Size: 3.67 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
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README
[](https://pypi.org/project/LabToolbox/)
[](https://github.com/giusesorrentino/LabToolbox/blob/master/LICENSE.txt)
[](https://github.com/giusesorrentino/LabToolbox/issues)
[](https://github.com/giusesorrentino/LabToolbox/pulls)
**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`:
```bash
pip install LabToolbox
```
Alternatively, you can clone the repository and install it manually:
```bash
git clone https://github.com/giusesorrentino/LabToolbox.git
cd LabToolbox
pip install .
```
## 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.
- **statsmodels** – statistical modeling and inference.
- **emcee** – affine-invariant ensemble sampler for MCMC.
- **corner** – corner plots for visualizing multidimensional distributions.
- **lmfit** – flexible curve-fitting with parameter constraints.
- **astropy** – core astronomy library for Python.
> **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.
## Library Structure
The **LabToolbox** package is organized into multiple submodules, each dedicated to a specific aspect of experimental data analysis:
- **LabToolbox.fit**: Routines for linear and non-linear curve fitting.
- **LabToolbox.signals**: Signal analysis tools tailored for laboratory experiments, featuring frequency domain analysis and post-processing of acquired data.
- **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.uncertainty**: Methods for estimating and propagating uncertainties in experimental contexts, allowing quantification of how input errors affect model outputs.
- **LabToolbox.utils**: A collection of helper functions for tasks like data formatting and general-purpose utilities used throughout the package.
## Documentation
Detailed 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.
## Citation
If 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).
## Code of Conduct
This 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.
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](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.
Some 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”.
Additionally, 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.