---
id: peakutils
version: "1.3.5"
license: MIT
license_treatment: permissive
maintenance: dormant
---

# PeakUtils — Peak detection utilities for 1D data

License: permissive · Maintenance: dormant · Downloads: 75.8K/mo

## What it is and 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—spectroscopy, chromatography, time-series analysis, and similar domains.

The package is stable and has been in production use since 2014, though it is no longer actively maintained.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.

Use it for:

- 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

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

PeakUtils detects peaks in 1D data and refines their positions through baseline estimation, Gaussian fitting, and centroid computation.

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.

## Install

pip install peakutils
uv add peakutils
poetry add peakutils

## Installing PeakUtils

Before you install: Low friction install with only numpy and scipy as runtime dependencies. Package is dormant (778 days since last release) but marked Production/Stable; no active maintenance signal.

License in practice: MIT license permits commercial and private use with minimal restrictions; attribution required but no copyleft obligations.

Quickstart:

import numpy as np
import peakutils

data = np.array([0, 1, 3, 2, 1, 0])  # 1D signal
indexes = peakutils.indexes(data, thres=0.3, min_dist=1)
print(indexes)  # Peak indices

Verify before relying:

- Whether the package still works reliably with current numpy/scipy versions despite dormancy
- Whether Gaussian fitting and centroid computation are suitable for your specific peak shape and noise profile

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 75.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

peak detection 1d data, find peaks in signal, gaussian peak fitting, baseline estimation signal, centroid peak detection, peak localization algorithm, signal processing peaks, signal-processing, peak-detection, spectroscopy

[View on SkillFed](https://skillfed.io/packages/peakutils) · [View on PyPI](https://pypi.org/project/peakutils/)