chebyfit 2020.1.1
pip install chebyfit==2020.1.1
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Fit exponential and harmonic functions using Chebyshev polynomials
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- License: BSD License (BSD)
- Author: Christoph Gohlke
- Requires: Python >=3.6
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Project description
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:
- Organization:
Laboratory for Fluorescence Dynamics. University of California, Irvine
- License:
BSD 3-Clause
- Version:
2020.1.1
Requirements
Revisions
- 2020.1.1
Remove support for Python 2.7 and 3.5. Update copyright.
- 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
Project details
Unverified details
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- License: BSD License (BSD)
- Author: Christoph Gohlke
- Requires: Python >=3.6
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