- class scipy.interpolate.Akima1DInterpolator(x, y, axis=0)#
Fit piecewise cubic polynomials, given vectors x and y. The interpolation method by Akima 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.
- xndarray, shape (npoints, )
1-D array of monotonically increasing real values.
- yndarray, shape (…, npoints, …)
N-D array of real values. The length of
yalong the interpolation axis must be equal to the length of
x. Use the
axisparameter to select the interpolation axis.
- axisint, optional
Axis in the
yarray corresponding to the x-coordinate values. Defaults to
New in version 0.14.
Use only for precise data, as the fitted curve passes through the given points exactly. This routine is useful for plotting a pleasingly smooth curve through a few given points for purposes of plotting.
-  A new method of interpolation and smooth curve fitting based
on local procedures. Hiroshi Akima, J. ACM, October 1970, 17(4), 589-602.
__call__(x[, nu, extrapolate])
Evaluate the piecewise polynomial or its derivative.
Construct a new piecewise polynomial representing the derivative.
Construct a new piecewise polynomial representing the antiderivative.
Find real roots of the piecewise polynomial.