Metadata-Version: 1.2
Name: chebyfit
Version: 2020.1.1
Summary: Fit exponential and harmonic functions using Chebyshev polynomials
Home-page: https://www.lfd.uci.edu/~gohlke/
Author: Christoph Gohlke
Author-email: cgohlke@uci.edu
License: BSD
Description: 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.lfd.uci.edu/~gohlke/>`_
        
        :Organization:
          Laboratory for Fluorescence Dynamics. University of California, Irvine
        
        :License: BSD 3-Clause
        
        :Version: 2020.1.1
        
        Requirements
        ------------
        * `CPython >= 3.6 <https://www.python.org>`_
        * `Numpy 1.14 <https://www.numpy.org>`_
        
        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
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
