labtoolbox 1.1.2

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.

Library Structure

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

• LabToolbox.basics
  Contains fundamental functions for statistical analysis, such as computation of means, variances, and covariances. These tools provide the basis for most data pre-processing tasks and error analysis.

• LabToolbox.fit
  Provides routines for linear and non-linear curve fitting, including uncertainty-aware methods. This module also includes tools for computing and visualizing fit residuals and statistical indicators such as the reduced chi-squared and p-values.

• LabToolbox.misc
  A collection of utility functions for general data handling, including outlier removal, histogram analysis, and formatted display of values with uncertainties.

• LabToolbox.uncertainty
  Implements numerical propagation of uncertainties for multivariate functions. The functions in this module use numerical derivatives and covariance matrices to return reliable error estimates for complex expressions.

• LabToolbox.posterior
  Contains tools for Bayesian analysis of model parameters. This module allows you to visualize posterior distributions using MCMC sampling (powered by the emcee library), enabling a probabilistic interpretation of the fit results.

Disclaimer

The functions my_cov, my_var, my_mean, my_line, my_lin_fit, 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”).
These functions are available at https://baltig.infn.it/LabMeccanica/PythonJupyter.

Additionally, 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 lin_fit and model_fit functions include an option to display fit residuals. The code responsible for this feature is adapted from the VoigtFit library.

Installation

You can install LabToolbox easily using pip:

pip install LabToolbox