{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"}],"enrichment":{"capability":"PeakUtils detects peaks in 1D data and refines their positions through baseline estimation, Gaussian fitting, and centroid computation.","skillfed_tags":["signal-processing","peak-detection","spectroscopy"],"use_cases":["Locate absorption or emission peaks in spectroscopic data and refine their positions with Gaussian fitting","Detect chromatography peaks and compute their centroids for quantitative analysis","Find local maxima in time-series or sensor data and estimate their true positions","Estimate and remove baseline drift before peak detection in noisy signals","Identify multiple peaks in a single pass and rank them by prominence or height"],"what_it_does":"PeakUtils is a signal processing library focused on locating and characterizing peaks in one-dimensional data. It provides functions to estimate signal baselines, identify peak positions by index, and refine peak locations using Gaussian fitting or centroid calculation. The library sits on top of numpy and scipy, making it suitable for scientific and engineering workflows where precise peak localization matters\u2014spectroscopy, chromatography, time-series analysis, and similar domains.\n\nThe package is stable and has been in production use since 2014, though it is no longer actively maintained. It targets straightforward peak-finding tasks where you need both the raw peak indices and optionally higher-resolution position estimates. Installation is lightweight, and the API is minimal, making it easy to integrate into existing analysis pipelines.","worth_installing":"Yes, if your peak-detection task is straightforward and you are comfortable with a dormant but stable library. The low install friction, permissive license, and proven track record make it a reasonable choice for standard 1D peak finding. However, verify compatibility with your current numpy and scipy versions, and consider whether the library's Gaussian and centroid methods suit your data. If you need active maintenance or advanced features, evaluate alternatives."},"id":"peakutils","links":{"html":"https://skillfed.io/packages/peakutils","md":"https://skillfed.io/packages/peakutils.md","pypi":"https://pypi.org/project/peakutils/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-06-27","license_spdx":null,"license_treatment":"permissive","name":"PeakUtils","python_support":"unspecified","summary":"Peak detection utilities for 1D data"},"popularity":{"monthly_downloads":75808,"position":14680,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.5"}