How to Turn Per-Spectrum Fitting Into NumPy Batch Fitting — SoftwareX Accepted It in 17 Days
Lab code that fits spectra one by one in a Python loop gets slow once you have thousands of curves. SPECTROview, an open-source Python desktop app for spectroscopic processing and fitting, replaces that loop with a vectorized batch fitting (VBF) engine built on NumPy. On an Intel Core i5-8365U laptop with 16 GB of RAM, the authors report 16×–62× speedups versus lmfit one-by-one fitting, depending…
Per-spectrum fitting in Python loops becomes slow when dealing with thousands of curves. SPECTROview, an open-source Python desktop application for spectroscopic processing and fitting, offers a solution through a vectorized batch fitting (VBF) engine built on NumPy. In a test on an Intel Core i5-8365U laptop with 16 GB RAM, VBF achieved speedups of 16× to 62× compared to lmfit one-by-one fitting, depending on the dataset size and model complexity.
The largest test used 16,384 spectra, with peak memory of 845 MB for under a second, then returning to approximately 80 MB after convergence. The SoftwareX paper, describing the software, was received on June 17, 2026, accepted on July 4, 2026, and published on July 8, 2026, marking a 17-day journey from submission to acceptance in CrushSCI’s SoftwareX records.
SoftwareX, published by Elsevier as an open-access journal, aims to recognize research software through peer-reviewed citations, inspection, and reuse with developer credit. The journal accepts multidisciplinary software, including mathematics, physics, environment, medicine, biology, and others. The paper's scope includes original software publications, software updates, and domain-independent building blocks like math libraries, image-processing libraries, and visualization tools.
SPECTROview's pipeline includes importing spectra from various formats, baseline correction, multi-peak fitting, and exporting publication-ready figures. Its VBF engine stacks spectra into an N×M matrix, builds the Jacobian for Levenberg-Marquardt optimization as an N×M×K tensor, and solves all equations in a single vectorized LAPACK call, offering closed-form analytic Jacobians and a convergence mask.
The authors avoided using PyTorch or TensorFlow batch LM implementations due to their reliance on autodiff for neural-net training and difficulty handling spectra that converge at different iteration steps. The speedups of VBF were measured on an Intel Core i5-8365U laptop with 16 GB RAM against lmfit one-by-one fitting, demonstrating its effectiveness for handling large-scale spectroscopic data processing.
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