hankel
Hankel Transformations using method of Ogata 2005
Decision gist · record as of 2026-08-14
Yes. The package solves a specific, well-defined numerical problem (Hankel transforms via Ogata's method) with low install friction, permissive licensing, no known vulnerabilities, active maintenance, and production-stable status. Install it if you need accurate Hankel transforms or radially symmetric Fourier analysis.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6 or later.
- Low friction: pure Python wheel with three stable scientific dependencies (numpy, scipy, mpmath).
- Repository is active with recent commits and production-stable status.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
last release 2023-07-27 (1114 days) · last repo commit 2026-08-10 · 50 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 132,031 downloads/mo, #11,567 on PyPI
Alternatives
Verify before relying
pip install hankel
import hankel
import numpy as np
# Define a radial function and compute its Hankel transform
h = hankel.HankelTransform(nu=0)
result = h.transform(lambda r: np.exp(-r), k=1.0)- Accuracy benchmarks or error bounds for typical use cases beyond the Ogata 2005 reference.
- Performance characteristics (speed, memory) for large-scale or high-order transforms.
- Specific API and class names available in the hankel module for constructing transforms.
What it is and what it does
Hankel is a Python library that solves Hankel transforms and integrals—a class of integral transforms needed whenever Fourier analysis must be applied to radially symmetric fields. The core problem it addresses is that standard numerical integration schemes fail on these transforms because Bessel functions oscillate wildly, making quadrature unreliable. The package implements Ogata's method, which locates the zeros of the Bessel function to construct an accurate and fast quadrature scheme.
The library is built on numpy, scipy, and mpmath, and is designed for researchers and engineers working in fields where radial symmetry and Fourier analysis intersect. It supports arbitrary-order transforms, includes built-in support for radially symmetric Fourier transforms, and is thoroughly tested. The package is production-stable, actively maintained, and requires Python 3.6 or later.
Use it for
- Computing Hankel transforms for wave propagation problems in cylindrically symmetric media.
- Evaluating radially symmetric Fourier transforms in image processing or diffraction analysis.
- Solving integral equations involving Bessel functions in mathematical physics.
- Performing accurate numerical integration of oscillatory functions weighted by Bessel functions.
- Analyzing correlation functions with radial symmetry in statistical applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package solves a specific, well-defined numerical problem (Hankel transforms via Ogata's method) with low install friction, permissive licensing, no known vulnerabilities, active maintenance, and production-stable status. Install it if you need accurate Hankel transforms or radially symmetric Fourier analysis.
Install
hankel on PyPI
Before you install
Low friction: pure Python wheel with three stable scientific dependencies (numpy, scipy, mpmath). Repository is active with recent commits and production-stable status.
Requires Python 3.6 or later.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
Quickstart
pip install hankel
import hankel
import numpy as np
# Define a radial function and compute its Hankel transform
h = hankel.HankelTransform(nu=0)
result = h.transform(lambda r: np.exp(-r), k=1.0)
Verify before relying
- Accuracy benchmarks or error bounds for typical use cases beyond the Ogata 2005 reference.
- Performance characteristics (speed, memory) for large-scale or high-order transforms.
- Specific API and class names available in the hankel module for constructing transforms.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesmpmathnumpyscipy |
| Maintenance | Actively maintained 1,114 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 132,031 / month, #11,567 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: End Users/DesktopIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyTopic :: Scientific/EngineeringTopic :: Utilities |
Evidence: hankel-1.2.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “hankel transform”
- hankelComputes Hankel transforms and integrals using Ogata's quadrature…
- asdf-transform-schemasProvides ASDF schemas for validating transform tags used in the ASDF…
- edtComputes Euclidean distance transforms for 1D, 2D, or 3D labeled…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also PyWavelets · jaxellip · nvidia-cufft · nvidia-cufft-cu11 · transformations · nvidia-cufft-cu12 · humming-kernels · nvidia-mathdx · gram-newton-schulz