{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"Provides 50+ baseline correction algorithms for experimental data from spectroscopy and analytical techniques, with a unified API for testing and comparing methods.","skillfed_tags":["spectroscopy","signal-processing","chemistry"],"use_cases":["Remove background noise from Raman spectroscopy measurements to isolate peak signals for chemical identification.","Correct baseline drift in FTIR spectra before performing quantitative analysis or peak fitting.","Compare multiple baseline correction algorithms on the same dataset to determine which produces the best result for your material.","Preprocess XRD or XRF data to improve signal-to-noise ratio before downstream analysis.","Integrate baseline correction into automated spectroscopy data pipelines for batch processing.","Develop custom baseline correction workflows by combining different algorithms and parameters."],"what_it_does":"pybaselines is a Python library for baseline correction\u2014the removal of background signal from experimental measurements in spectroscopy and analytical chemistry. It implements over 50 algorithms including well-known methods like AsLS, airPLS, ModPoly, and SNIP, as well as specialized variants unique to the library. The library targets practitioners working with data from techniques such as Raman, FTIR, NMR, XRD, and similar analytical methods.\n\nThe main interface is the Baseline class for 1D data and Baseline2D for 2D data. Each algorithm returns both the calculated baseline and a dictionary of parameters, allowing users to quickly test multiple approaches on the same dataset to find the best fit. The library depends only on numpy and scipy, making it lightweight and easy to integrate into existing scientific Python workflows.","worth_installing":"Yes. pybaselines is a mature, actively maintained library with no known vulnerabilities, low installation friction, and a permissive license. Install it if you work with spectroscopy or analytical data requiring baseline correction\u2014the breadth of algorithms and unified API make it the natural choice for this task."},"id":"pybaselines","links":{"html":"https://skillfed.io/packages/pybaselines","md":"https://skillfed.io/packages/pybaselines.md","pypi":"https://pypi.org/project/pybaselines/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-08-10","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"pybaselines","python_support":"supports_current","summary":"A library of algorithms for the baseline correction of experimental data."},"popularity":{"monthly_downloads":128730,"position":11697,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.1"}