Metadata-Version: 2.1
Name: pyhank
Version: 2.4.0
Summary: pyhank - Quasi-discrete Hankel transforms for python
Home-page: https://github.com/etfrogers/pyhank
Author: Edward Rogers
Author-email: etfrogers@hotmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt

PyHank - Quasi-Discrete Hankel Transforms for Python
====================================================

##### Edward Rogers


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PyHank is a python implementation of the quasi-discrete Hankel transform as developed by Manuel Guizar-Sicairos and Julio C. Guitierrez-Vega

> *"Computation of quasi-discrete Hankel transforms of the integer order for propagating optical wave fields"*
  Manuel Guizar-Sicairos and Julio C. Guitierrez-Vega
  J. Opt. Soc. Am. A **21** (1) 53-58 (2004)

It was designed for use primarily in cases where a discrete Hankel transform is required, similar to the FFT for a Fourier transform. It operates on functions stored in NumPy arrays. If you want an Hankel transform that operates on a callable function, you may be interested in [hankel](<https://github.com/steven-murray/hankel>) by Steven Murray.

I have used this code extensively for beam-propagation-method calculations of radially-symmetric beams. In the radially symmetric case, the 2D FFT over x and y that would be used in a non-symmetric system is replaced by a 1D QDHT over r, making the computational load much lighter and allowing bigger simulations.

PyHank was inspired by Adam Wyatt's [Matlab version](https://uk.mathworks.com/matlabcentral/fileexchange/15623-hankel-transform) which I used for many years, before moving to Python and needing my own implementation. It aims to simplify the interface (using Python's object-oriented approach) and utilise existing NumPy/SciPy functions wherever possible.

It has both a simple single-shot interface, and a more advanced approach that speeds up computation significantly if making multiple transforms on the same grid.

Contributions and comments are welcome using Github at:
http://github.com/etfrogers/pyhank


Installation
------------

Installation can simply be done from pip.
PyHank requires numpy and scipy, but these will be installed by pip if necessary.

``pip install pyhank``

For building the docs, the following are required:

- sphinx-gallery >= 0.7
- matplotlib >= 3.2

For development, and running the tests, the following are recommended:

- pytest ~= 5.4.3
- flake8 ~= 3.8.3
- pytest-flake8 ~= 1.0.6
- pytest-cov ~= 2.10.0

Bugs & Contribution
-------------------

Please use Github to report bugs, feature requests and submit your code:
http://github.com/etfrogers/pyhank



Documentation
-------------

The documentation for PyHank can be found at [Read the docs](https://pyhank.readthedocs.io/en/latest/index.html)

Usage
-----

See the [Usage examples at ReadTheDocs](https://pyhank.readthedocs.io/en/latest/auto_examples/index.html)


