Scientific Data Analysis with LabPlot in Python: Signal Processing, Spectral Peak Fitting, Visualization, and Batch Automation

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In this tutorial, we explore a LabPlot-inspired scientific data analysis workflow in Python while preserving the structure and terminology of LabPlot’s aspect tree, analysis kernels, plotting system, and project model. We build reusable components to import tabular data, compute descriptive statistics, smooth and differentiate signals, perform Fourier analysis and filtering, detect peaks, integrate curves, reduce data, and fit nonlinear models with detailed statistical diagnostics. We then apply these tools to a realistic spectroscopy example: removing periodic interference, identifying overlapping peaks, fitting a multi-Gaussian model, inspecting residuals, visualizing results through themed worksheets, exporting figures, and saving project data in LabPlot-compatible .lml-style files. Finally, we extend the same workflow to batch processing so we can analyze multiple temperature-dependent spectra and fit secondary trends across the resulting measurements.

Copy CodeCopiedUse a different Browserimport os, sys, gzip, bz2, lzma, time, math, textwrap, warnings import xml.etree.ElementTree as ET from dataclasses import dataclass, field from enum import Enum import numpy as np, pandas as pd, matplotlib, matplotlib.pyplot as plt from matplotlib.ticker import AutoMinorLocator import scipy from scipy import signal, stats, optimize warnings.filterwarnings("ignore", category=RuntimeWarning) np.random.seed(20260815) IN_COLAB = "google.colab" in sys.modules OUT = "/content/labplot_out" if IN_COLAB else os.path.join(os.getcwd(), "labplot_out") os.makedirs(OUT, exist_ok=True) try: from …

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