Python-Based Tutorial Recreates LabPlot-Style Scientific Data Analysis Workflow

The tutorial begins by constructing an object-oriented model that mirrors LabPlot's aspect tree. It defines a Column class to store typed vectors with plot designations, and provides methods for cleaning invalid values and generating sparklines. A Spreadsheet class manages multiple columns and computes a comprehensive set of descriptive statistics—twenty in total, including arithmetic, geometric, harmonic, and contraharmonic means, mode, quartiles, median, trimean, variance, standard deviation, skewness, kurtosis, mean absolute deviation, median absolute deviation, and entropy. These statistics align with LabPlot's Column Statistics dialog. The code also includes a tree() method to display the hierarchy of aspects and enums for column modes and plot designations.

For signal processing, the implementation includes smoothing, differentiation, Fourier analysis and filtering, peak detection, curve integration, and data reduction, relying on NumPy, SciPy, and Matplotlib. The tutorial also demonstrates nonlinear model fitting with statistical diagnostics, using a multi-Gaussian model as an example. The workflow is applied to a realistic spectroscopy case: removing periodic interference, identifying overlapping peaks, fitting the model, inspecting residuals, and visualizing the results through themed worksheets. Figures can be exported, and the project can be saved in a LabPlot-compatible .lml-style file.

A key extension is batch processing, which enables the analysis of multiple temperature-dependent spectra in one pass and the fitting of secondary trends across the resulting measurements. All code is provided in the tutorial and runs both locally and in Google Colab, making the approach accessible for reproducible research.