# scipy.interpolate.Akima1DInterpolator#

class scipy.interpolate.Akima1DInterpolator(x, y, axis=0)[source]#

Akima interpolator

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.

Parameters:
xndarray, shape (npoints, )

1-D array of monotonically increasing real values.

yndarray, shape (…, npoints, …)

N-D array of real values. The length of `y` along the interpolation axis must be equal to the length of `x`. Use the `axis` parameter to select the interpolation axis.

axisint, optional

Axis in the `y` array corresponding to the x-coordinate values. Defaults to `axis=0`.

`PchipInterpolator`

PCHIP 1-D monotonic cubic interpolator.

`CubicSpline`

Cubic spline data interpolator.

`PPoly`

Piecewise polynomial in terms of coefficients and breakpoints

Notes

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.

References

 A new method of interpolation and smooth curve fitting based

on local procedures. Hiroshi Akima, J. ACM, October 1970, 17(4), 589-602.

Attributes:
axis
c
extrapolate
x

Methods

 `__call__`(x[, nu, extrapolate]) Evaluate the piecewise polynomial or its derivative. `derivative`([nu]) Construct a new piecewise polynomial representing the derivative. `antiderivative`([nu]) Construct a new piecewise polynomial representing the antiderivative. `roots`([discontinuity, extrapolate]) Find real roots of the piecewise polynomial.