semvec/pypi-packages huggingface dataset:
chebyfit 2026.1.18
Fit exponential and harmonic functions using Chebyshev polynomials
License: BSD-3-Clause
Requires Python: >=3.11

Fit exponential and harmonic functions using Chebyshev polynomials
==================================================================

Chebyfit is a Python library that implements the algorithms described in:

    Analytic solutions to modelling exponential and harmonic functions using
    Chebyshev polynomials: fitting frequency-domain lifetime images with
    photobleaching. G C Malachowski, R M Clegg, and G I Redford.
    J Microsc. 2007; 228(3): 282-295. doi: 10.1111/j.1365-2818.2007.01846.x

:Author: `Christoph Gohlke <https://www.cgohlke.com>`_
:License: BSD 3-Clause
:Version: 2026.1.18

Quickstart
----------

Install the chebyfit package and all dependencies from the
`Python Package Index <https://pypi.org/project/chebyfit/>`_::

    python -m pip install -U chebyfit

See `Examples`_ for using the programming interface.

Source code and support are available on
`GitHub <https://github.com/cgohlke/chebyfit>`_.

Requirements
------------

This revision was tested with the following requirements and dependencies
(other versions may work):

- `CPython <https://www.python.org>`_ 3.11.9, 3.12.10, 3.13.11, 3.14.2 64-bit
- `NumPy <https://pypi.org/project/numpy/>`_ 2.4.1

Revisions
---------

2026.1.18

- Rename chebyshev_invers to chebyshev_inverse (breaking).
- Use multi-phase initialization.
- Improve code quality.

2025.8.1

- Drop support for Python 3.10, support Python 3.14.

2025.1.1

- Improve type hints.
- Drop support for Python 3.9, support Python 3.13.

2024.5.24

- …

Refer to the CHANGES file for older revisions.

Examples
--------

Fit two-exponential decay function:

>>> deltat = 0.5
>>> t = numpy.arange(0, 128, deltat)
>>> data = 1.1 + 2.2 * numpy.exp(-t / 33.3) + 4.4 * numpy.exp(-t / 55.5)
>>> params, fitted = fit_exponentials(data, numexps=2, deltat=deltat)
>>> numpy.allclose(data, fitted)
True
>>> params['offset']
array([1.1])
>>> params['amplitude']
array([[4.4, 2.2]])
>>> params['rate']
array([[55.5, 33.3]])

Fit harmonic function with exponential decay:

>>> tt = t * (2 * math.pi / (t[-1] + deltat))
>>> data = 1.1 + numpy.exp(-t / 22.2) * (
...     3.3 - 4.4 * numpy.sin(tt) + 5.5 * numpy.cos(tt)
... )
>>> params, fitted = fit_harmonic_decay(data, deltat=0.5)
>>> numpy.allclose(data, fitted)
True
>>> params['offset']
array([1.1])
>>> params['rate']
array([22.2])
>>> params['amplitude']
array([[3.3, 4.4, 5.5]])

Fit experimental time-domain image:

>>> data = numpy.fromfile('test.b&h', dtype='float32').reshape((256, 256, 256))
>>> data = data[64 : 64 + 64]
>>> params, fitted = fit_exponentials(data, numexps=1, numcoef=16, axis=0)
>>> numpy.allclose(data.sum(axis=0), fitted.sum(axis=0))
True