Metadata-Version: 1.2
Name: akima
Version: 2019.2.20
Summary: Akima Interpolation
Home-page: https://www.lfd.uci.edu/~gohlke/
Author: Christoph Gohlke
Author-email: cgohlke@uci.edu
License: BSD
Description: Akima Interpolation
        ===================
        
        Akima is a Python library that implements Akima's interpolation method
        using a continuously differentiable sub-spline built from piecewise cubic
        polynomials [1]. The resultant curve passes through the given data points
        and will appear smooth and natural.
        
        :Author:
          `Christoph Gohlke <https://www.lfd.uci.edu/~gohlke/>`_
        
        :Organization:
          Laboratory for Fluorescence Dynamics. University of California, Irvine
        
        :License: 3-clause BSD
        
        :Version: 2019.2.20
        
        Requirements
        ------------
        * `CPython 2.7 or 3.5+ <https://www.python.org>`_
        * `Numpy 1.14 <https://www.numpy.org>`_
        * `Matplotlib 2.2 <https://www.matplotlib.org>`_  (optional for plotting)
        
        Notes
        -----
        The Akima module is no longer being actively developed.
        
        Consider using `scipy.interpolate.Akima1DInterpolator
        <http://docs.scipy.org/doc/scipy/reference/interpolate.html>`_ instead.
        
        References
        ----------
        (1) A new method of interpolation and smooth curve fitting based
            on local procedures. Hiroshi Akima, J. ACM, October 1970, 17(4), 589-602.
        
        Examples
        --------
        >>> from scipy.interpolate import Akima1DInterpolator
        >>> def example():
        ...     '''Plot interpolated Gaussian noise.'''
        ...     x = numpy.sort(numpy.random.random(10) * 100)
        ...     y = numpy.random.normal(0.0, 0.1, size=len(x))
        ...     x2 = numpy.arange(x[0], x[-1], 0.05)
        ...     y2 = interpolate(x, y, x2)
        ...     y3 = Akima1DInterpolator(x, y)(x2)
        ...     from matplotlib import pyplot
        ...     pyplot.title('Akima interpolation of Gaussian noise')
        ...     pyplot.plot(x2, y2, 'r-', label='akima')
        ...     pyplot.plot(x2, y3, 'b:', label='scipy', linewidth=2.5)
        ...     pyplot.plot(x, y, 'go', label='data')
        ...     pyplot.legend()
        ...     pyplot.show()
        >>> example()
        
Platform: any
Classifier: Development Status :: 7 - Inactive
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 :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=2.7
