barak.interp.interp_Akima¶

barak.interp.interp_Akima(x_new, x, y)[source]¶

Return interpolated data using Akima’s method.

Akima’s interpolation method uses a continuously differentiable sub-spline built from piecewise cubic polynomials. The resultant curve passes through the given data points and will appear smooth and natural.

Parameters :	x_new : array_like, shape (M,) Values at which to interpolate. x, y : array_like, shape (N,) Reference values. x cannot contain duplicates.
Returns :	vals : ndarray, shape (M,) Interpolated values.

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

Plot interpolated Gaussian noise:

>>> x = np.sort(np.random.random(10) * 100)
>>> y = np.random.normal(0.0, 0.1, size=len(x))
>>> x2 = np.arange(x[0], x[-1], 0.02)
>>> y2 = interp_Akima(x2, x, y)
>>> y3 = interp_spline(x2, x, y)
>>> from matplotlib import pyplot as plt
>>> plt.plot(x2, y2, 'b-', label='Akima')
>>> plt.plot(x2, y3, 'r-', label='Cubic')
>>> plt.plot(x, y, 'co')
>>> plt.legend()
>>> plt.show()