Fit exponential and harmonic functions using Chebyshev polynomials
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:
2021.6.6
Requirements
Revisions
- 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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for chebyfit-2021.6.6-pp38-pypy38_pp73-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 28e2bc56f1f07aee6a4c77caf01d15ab9929ac9978189385c3132bdcecf997a8 |
|
MD5 | 7a70d8994d9a8cd495a4f39ac49d3a2a |
|
BLAKE2b-256 | b4037925c4c7b4435f7edfe8e92bf2519fe391bd916bfa69542a271e7eadd2af |
Hashes for chebyfit-2021.6.6-pp37-pypy37_pp73-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f88846ce998f4a8ead4efd260d53b9b7b071681d9c9c0a5c2212361f7c2e38e6 |
|
MD5 | 406e7b4ff4890f41939a1c33c3b3ba59 |
|
BLAKE2b-256 | 4353c73b1dd7d528abe071b7eaec701b9354772ca44149eccd6040d2b60ce4e4 |
Hashes for chebyfit-2021.6.6-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2d76b94caa29547f9f53a124531edb5838fb9b7aa4266a7a4bb252c4c9df5e7a |
|
MD5 | 27eceb5bee6cacd0022e750ff4f20677 |
|
BLAKE2b-256 | 4c58dd01b4f819d8044d342c7b4aba22725e924edc3497ede6ef2ac3bc1c4706 |
Hashes for chebyfit-2021.6.6-cp310-cp310-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 56b81e88b9173db7f599cfabc7ab72ed19e27410f8bc1bfc9e33de761d057bf1 |
|
MD5 | 1e37b1e1376a96b0dd284756688a4bc2 |
|
BLAKE2b-256 | b0c20e17224709744d5e3f790d824212696ea5e3cd43b024ecac7a5963fa3edc |
Hashes for chebyfit-2021.6.6-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e1fd97040b99a909050adc944d166a7bcabf36aff0b0d65eb0a2e69b694facc3 |
|
MD5 | 79069269eab06cfdad81fd82b0838650 |
|
BLAKE2b-256 | 67af9c3c902c47119f3d72fc104366c89d8942131f30ed55044f3e984e220b11 |
Hashes for chebyfit-2021.6.6-cp39-cp39-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3b5ada410fd0baeb09d852a59ec98d5d9a83c39ba77ee0bd6a7aba3c2ee761b9 |
|
MD5 | 3ce840caa04c491487685565651912e8 |
|
BLAKE2b-256 | 39ea51e28101257f32b5699cfe84fdf3a5b6a580bff41f67c37d008c53d009ed |
Hashes for chebyfit-2021.6.6-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b9d76ae61367a17e7de46fd89a1d11ec875aeb2109ec9854aa87dcc451ffda15 |
|
MD5 | a81c48df61cd40731bd25d0e2813113d |
|
BLAKE2b-256 | 60b172946b782dabfc614aa7c329397f6f48f6669323d9566557c677551e97bb |
Hashes for chebyfit-2021.6.6-cp38-cp38-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 43f872befed7791fed7d8df0b9855349594d4b4ec7716ff53b8a16eb84189ad1 |
|
MD5 | b60193ef33387d45ad6c1f710f3fe748 |
|
BLAKE2b-256 | 17584553f7da4f3af5e65f76bf434c619a7b60cf318f1fb92c6004026da848f9 |
Hashes for chebyfit-2021.6.6-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f342fce1c711bc12bba79a8db45717367a9e9be0aaf7d98ec28c9cc9a5b2c022 |
|
MD5 | dd77ebab0f2b575f21b8c33c27031681 |
|
BLAKE2b-256 | b2ef287161fdc2a5334f24e31be55430c3879c07806cfb8ebcc1c8828050a9da |
Hashes for chebyfit-2021.6.6-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b0b52cb0bf996a96b64bc4a53835773093b5d57776fcf3137ddf4eb4a17d7182 |
|
MD5 | 59e7f87d728aff29eff899992cdfd5ce |
|
BLAKE2b-256 | 7c24af9a5d4c0a9fe0ec80ca5ecca42616a693a4a969178e89171b7bf25f857c |