Metadata-Version: 2.4
Name: eisyfit-mittag-leffler
Version: 0.1
Summary: Python implementation of the Mifflag-Leffler function
Home-page: https://github.com/jaroslav-herman/mittag-leffler
Author: Jake Huang
Author-email: jdhuang@mines.edu
License: BSD 3-clause
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: description
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# The Mittag-Leffler function in Python

This package contains modules for calculating and approximating the Mittag-Leffler function.

The `mittag_leffler.py` module contains a Python port of
a published
[Matlab implementation](https://se.mathworks.com/matlabcentral/fileexchange/48154-the-mittag-leffler-function) of
the generalized Mittag-Leffler function, written by Konrad Hinsen. The module `ml_internal.py` contains the internal functions for `mittag_leffler.py`. The script `test_ml.py` contains tests for the functions in `mittag_leffler.py`. To use the Python port of the Matlab implementation, simply import `ml` from `mittag_leffler.py`.

The `pade_approx.py` module contains a Python implementation of a global Pade approximation for the Mittag-Leffler function as described by [Sarumi, Furati, and Khaliq](https://arxiv.org/abs/1912.10996). This approximation is valid for `z < 0`, `0 < alpha < 1`, `beta >= alpha`, `(alpha, beta) != (1, 1)`. The approximaton is highly accurate and requires 3 orders of magnitude less computation time than the Matlab algorithm.

# eisyfit-mittag-leffler

This maintained distribution is published as `eisyfit-mittag-leffler` for
use by EIS Fitting. The Python import package remains `mitlef`.

The source code is unchanged from the upstream project; the fork only adds
PyPI distribution metadata and release automation.
