chebyfit 2022.9.29

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

License:

BSD 3-Clause

Version:

2022.9.29

Requirements

This release has been tested with the following requirements and dependencies (other versions may work):

• CPython 3.8.10, 3.9.13, 3.10.7, 3.11.0rc2

• NumPy 1.22.4

Revisions

2022.9.29

• Add type hints.

• Convert to Google style docstrings.

2022.8.26

• Update metadata.

• Remove support for Python 3.7 (NEP 29).

2021.6.6

• Fix compile error on Python 3.10.

• Remove support for Python 3.6 (NEP 29).

2020.1.1

• Remove support for Python 2.7 and 3.5.

2019.10.14

• Support Python 3.8.

• Fix numpy 1type FutureWarning.

2019.4.22

• Fix setup requirements.

2019.1.28

• Move modules into chebyfit package.

• Add Python wrapper for _chebyfit C extension module.

• Fix static analysis issues in _chebyfit.c.

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