{"id":19671250,"url":"https://github.com/cgohlke/chebyfit","last_synced_at":"2025-04-29T00:32:46.088Z","repository":{"id":62561817,"uuid":"242421677","full_name":"cgohlke/chebyfit","owner":"cgohlke","description":"Fit exponential and harmonic functions using Chebyshev polynomials","archived":false,"fork":false,"pushed_at":"2024-05-25T03:52:57.000Z","size":113,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-06-11T18:32:54.239Z","etag":null,"topics":["chebyshev-polynomials","fluorescence-lifetime-spectroscopy","fluorescence-microscopy-imaging","image-processing","python"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/chebyfit","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cgohlke.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-02-22T22:38:15.000Z","updated_at":"2024-05-25T03:53:01.000Z","dependencies_parsed_at":"2022-11-03T15:01:01.978Z","dependency_job_id":null,"html_url":"https://github.com/cgohlke/chebyfit","commit_stats":null,"previous_names":[],"tags_count":8,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgohlke%2Fchebyfit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgohlke%2Fchebyfit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgohlke%2Fchebyfit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgohlke%2Fchebyfit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cgohlke","download_url":"https://codeload.github.com/cgohlke/chebyfit/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224142765,"owners_count":17262884,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["chebyshev-polynomials","fluorescence-lifetime-spectroscopy","fluorescence-microscopy-imaging","image-processing","python"],"created_at":"2024-11-11T17:08:16.257Z","updated_at":"2025-04-29T00:32:46.082Z","avatar_url":"https://github.com/cgohlke.png","language":"C","funding_links":[],"categories":[],"sub_categories":[],"readme":"..\n  This file is generated by setup.py\n\nFit 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 \u003chttps://www.cgohlke.com\u003e`_\n:License: BSD 3-Clause\n:Version: 2025.1.1\n\nQuickstart\n----------\n\nInstall the chebyfit package and all dependencies from the\n`Python Package Index \u003chttps://pypi.org/project/chebyfit/\u003e`_::\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 \u003chttps://github.com/cgohlke/chebyfit\u003e`_.\n\nRequirements\n------------\n\nThis revision was tested with the following requirements and dependencies\n(other versions may work):\n\n- `CPython \u003chttps://www.python.org\u003e`_ 3.10.11, 3.11.9, 3.12.8, 3.13.1 64-bit\n- `NumPy \u003chttps://pypi.org/project/numpy/\u003e`_ 2.1.3\n\nRevisions\n---------\n\n2025.1.1\n\n- Improve type hints.\n- Drop support for Python 3.9, support Python 3.13.\n\n2024.5.24\n\n- Fix docstring examples not correctly rendered on GitHub.\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 \u003c 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- Drop support for Python 3.7 (NEP 29).\n\n2021.6.6\n\n- Fix compile error on Python 3.10.\n- Drop support for Python 3.6 (NEP 29).\n\n2020.1.1\n\n- Drop 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.. code-block:: python\n\n    \u003e\u003e\u003e deltat = 0.5\n    \u003e\u003e\u003e t = numpy.arange(0, 128, deltat)\n    \u003e\u003e\u003e data = 1.1 + 2.2 * numpy.exp(-t / 33.3) + 4.4 * numpy.exp(-t / 55.5)\n    \u003e\u003e\u003e params, fitted = fit_exponentials(data, numexps=2, deltat=deltat)\n    \u003e\u003e\u003e numpy.allclose(data, fitted)\n    True\n    \u003e\u003e\u003e params['offset']\n    array([1.1])\n    \u003e\u003e\u003e params['amplitude']\n    array([[4.4, 2.2]])\n    \u003e\u003e\u003e params['rate']\n    array([[55.5, 33.3]])\n\nFit harmonic function with exponential decay:\n\n.. code-block:: python\n\n    \u003e\u003e\u003e tt = t * (2 * math.pi / (t[-1] + deltat))\n    \u003e\u003e\u003e data = 1.1 + numpy.exp(-t / 22.2) * (\n    ...     3.3 - 4.4 * numpy.sin(tt) + 5.5 * numpy.cos(tt)\n    ... )\n    \u003e\u003e\u003e params, fitted = fit_harmonic_decay(data, deltat=0.5)\n    \u003e\u003e\u003e numpy.allclose(data, fitted)\n    True\n    \u003e\u003e\u003e params['offset']\n    array([1.1])\n    \u003e\u003e\u003e params['rate']\n    array([22.2])\n    \u003e\u003e\u003e params['amplitude']\n    array([[3.3, 4.4, 5.5]])\n\nFit experimental time-domain image:\n\n.. code-block:: python\n\n    \u003e\u003e\u003e data = numpy.fromfile('test.b\u0026h', dtype='float32').reshape((256, 256, 256))\n    \u003e\u003e\u003e data = data[64 : 64 + 64]\n    \u003e\u003e\u003e params, fitted = fit_exponentials(data, numexps=1, numcoef=16, axis=0)\n    \u003e\u003e\u003e numpy.allclose(data.sum(axis=0), fitted.sum(axis=0))\n    True","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcgohlke%2Fchebyfit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcgohlke%2Fchebyfit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcgohlke%2Fchebyfit/lists"}