| Name | chebyfit JSON |
| Version |
2024.4.24
JSON |
| download |
| home_page | https://www.cgohlke.com |
| Summary | Fit exponential and harmonic functions using Chebyshev polynomials |
| upload_time | 2024-04-27 00:24:28 |
| maintainer | None |
| docs_url | None |
| author | Christoph Gohlke |
| requires_python | >=3.9 |
| license | BSD |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
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: 2024.4.24
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>`_ 3.9.13, 3.10.11, 3.11.9, 3.12.3
- `NumPy <https://pypi.org/project/numpy/>`_ 1.26.4
Revisions
---------
2024.4.24
- Support NumPy 2.
2024.1.6
- Support Python 3.12.
2023.4.22
- Drop support for Python 3.8 and numpy < 1.21 (NEP29).
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
Raw data
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"description": "Fit exponential and harmonic functions using Chebyshev polynomials\n==================================================================\n\nChebyfit is a Python library that implements the algorithms described in:\n\n Analytic solutions to modelling exponential and harmonic functions using\n Chebyshev polynomials: fitting frequency-domain lifetime images with\n photobleaching. G C Malachowski, R M Clegg, and G I Redford.\n J Microsc. 2007; 228(3): 282-295. doi: 10.1111/j.1365-2818.2007.01846.x\n\n:Author: `Christoph Gohlke <https://www.cgohlke.com>`_\n:License: BSD 3-Clause\n:Version: 2024.4.24\n\nQuickstart\n----------\n\nInstall the chebyfit package and all dependencies from the\n`Python Package Index <https://pypi.org/project/chebyfit/>`_::\n\n python -m pip install -U chebyfit\n\nSee `Examples`_ for using the programming interface.\n\nSource code and support are available on\n`GitHub <https://github.com/cgohlke/chebyfit>`_.\n\nRequirements\n------------\n\nThis revision was tested with the following requirements and dependencies\n(other versions may work):\n\n- `CPython <https://www.python.org>`_ 3.9.13, 3.10.11, 3.11.9, 3.12.3\n- `NumPy <https://pypi.org/project/numpy/>`_ 1.26.4\n\nRevisions\n---------\n\n2024.4.24\n\n- Support NumPy 2.\n\n2024.1.6\n\n- Support Python 3.12.\n\n2023.4.22\n\n- Drop support for Python 3.8 and numpy < 1.21 (NEP29).\n\n2022.9.29\n\n- Add type hints.\n- Convert to Google style docstrings.\n\n2022.8.26\n\n- Update metadata.\n- Remove support for Python 3.7 (NEP 29).\n\n2021.6.6\n\n- Fix compile error on Python 3.10.\n- Remove support for Python 3.6 (NEP 29).\n\n2020.1.1\n\n- Remove support for Python 2.7 and 3.5.\n\n2019.10.14\n\n- Support Python 3.8.\n- Fix numpy 1type FutureWarning.\n\n2019.4.22\n\n- Fix setup requirements.\n\n2019.1.28\n\n- Move modules into chebyfit package.\n- Add Python wrapper for _chebyfit C extension module.\n- Fix static analysis issues in _chebyfit.c.\n\nExamples\n--------\n\nFit two-exponential decay function:\n\n>>> deltat = 0.5\n>>> t = numpy.arange(0, 128, deltat)\n>>> data = 1.1 + 2.2 * numpy.exp(-t / 33.3) + 4.4 * numpy.exp(-t / 55.5)\n>>> params, fitted = fit_exponentials(data, numexps=2, deltat=deltat)\n>>> numpy.allclose(data, fitted)\nTrue\n>>> params['offset']\narray([1.1])\n>>> params['amplitude']\narray([[4.4, 2.2]])\n>>> params['rate']\narray([[55.5, 33.3]])\n\nFit harmonic function with exponential decay:\n\n>>> tt = t * (2 * math.pi / (t[-1] + deltat))\n>>> data = 1.1 + numpy.exp(-t / 22.2) * (3.3 - 4.4 * numpy.sin(tt)\n... + 5.5 * numpy.cos(tt))\n>>> params, fitted = fit_harmonic_decay(data, deltat=0.5)\n>>> numpy.allclose(data, fitted)\nTrue\n>>> params['offset']\narray([1.1])\n>>> params['rate']\narray([22.2])\n>>> params['amplitude']\narray([[3.3, 4.4, 5.5]])\n\nFit experimental time-domain image:\n\n>>> data = numpy.fromfile('test.b&h', dtype='float32').reshape((256, 256, 256))\n>>> data = data[64:64+64]\n>>> params, fitted = fit_exponentials(data, numexps=1, numcoef=16, axis=0)\n>>> numpy.allclose(data.sum(axis=0), fitted.sum(axis=0))\nTrue\n",
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