Akima Interpolation
Project description
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:
- Organization:
Laboratory for Fluorescence Dynamics. University of California, Irvine
- License:
3-clause BSD
- Version:
2019.2.20
Requirements
Matplotlib 2.2 (optional for plotting)
Notes
The Akima module is no longer being actively developed.
Consider using scipy.interpolate.Akima1DInterpolator instead.
References
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()
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