Akima Interpolation
===================
Akima is a Python library that implements Akima's interpolation method
described in:
A new method of interpolation and smooth curve fitting based on local
procedures. Hiroshi Akima, J. ACM, October 1970, 17(4), 589-602.
A continuously differentiable sub-spline is built from piecewise cubic
polynomials. It passes through the given data points and will appear smooth
and natural.
This module is no longer being actively developed. Consider using
`scipy.interpolate.Akima1DInterpolator
<http://docs.scipy.org/doc/scipy/reference/interpolate.html>`_ instead.
:Author: `Christoph Gohlke <https://www.cgohlke.com>`_
:License: BSD 3-Clause
:Version: 2024.1.6
Quickstart
----------
Install the akima package and all dependencies from the
`Python Package Index <https://pypi.org/project/akima/>`_::
python -m pip install -U akima
See `Examples`_ for using the programming interface.
Source code, examples, and support are available on
`GitHub <https://github.com/cgohlke/akima>`_.
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, 3.11.7, 3.12.1
- `NumPy <https://pypi.org/project/numpy/>`_ 1.26.3
Revisions
---------
2024.1.6
- Add type hints.
- Remove support for Python 3.8 and 1.22 (NEP 29).
2022.9.12
- Remove support for Python 3.7 (NEP 29).
- Update metadata.
Examples
--------
>>> import numpy
>>> from matplotlib import pyplot
>>> 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)
... 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()
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