A Python library of algorithms for the baseline correction of experimental data.
Python package that provides a full range of functionality to process and analyze vibrational spectra (Raman, SERS, FTIR, etc.).
Especially useful for preprocessing of datasets like Raman spectra, infrared spectra, UV/Vis spectra, but also HPLC data and many other types of data. pyPreprocessing includes baseline correction, smoothing, filtering, normalization and transformation.
Python library for hyperspectral analysis focused on spectroscopic approach.
Open-source FTIR software featuring a beginner-friendly graphical interface for publication-quality spectrum processing, analysis, automated peak assignment, and visualization. Designed for reproducible scientific research.
Professional Python toolkit for EPR spectroscopy data analysis. Load Bruker files, apply baseline correction, analyze lineshapes, and convert to FAIR formats. Complete CLI suite with comprehensive testing.
Locally connected deep neural network on a Boolean hypercube: the cube's edges are the wiring, and every weight trains. C++ core + Python SDK.
A cascaded preprocessor architecture built on the hypercube substrate: an etalon-style geometric mixing stage followed by a synthetic-orbit dynamical encoding stage, feeding a hypercube CNN.
Empirical baseline correction for strong-motion records
baseline correction using arPLS algorithm
Open-source Raman and FTIR spectroscopy software for spectral analysis, phase identification, peak detection, database search, and publication-ready plotting.
RamanAnalyzer is a Raman spectra preprocessing and reference-library matching tool for microplastic identification, developed at the University of Birmingham within the PlasticUnderground Doctoral Network, funded by the European Union’s Horizon Europe programme.
Frozen hypercube reservoir for static high-dimensional fields (spectra, sensors, images). Short synthetic orbit, HypercubeCNN on the end state only. C++23 + Python.
The Asymmetric Least Squares performs baseline correction by penalizing positive residuals.
Cyclic voltammetry peak analysis for 3D-printed screen-printed electrodes: onset-based baseline, three peak-current readings, experiment.json export
HPLC chromatogram peak detection, baseline correction, deconvolution and calibration-curve quantification for Agilent ChemStation data — pip install hplc-peakpicker
EEG data collection and processing in matlab. Proposed data collection algorithm and Processing pipeline for evoked potentials of EEG signals or regular EEG signals. Furthermore
LAsLS applies localized Whittaker smoothing and asymmetry parameters for accurate spectral baseline correction.
EMSC algorithm for correcting multiplicative/additive effects in spectral data.
Local Asymmetric Least Squares (LAsLS) allows complex baseline corrections with per-interval parameters.
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