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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.

Methods

Notes

versionadded

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.

Parameters

x : ndarray, shape (m, )

1-D array of monotonically increasing real values.

y : ndarray, shape (m, ...)

N-D array of real values. The length of y along the first axis must be equal to the length of x .

axis : int, optional

Specifies the axis of y along which to interpolate. Interpolation defaults to the first axis of y .

Akima interpolator

See Also

CubicSpline

Cubic spline data interpolator.

PPoly

Piecewise polynomial in terms of coefficients and breakpoints

PchipInterpolator

PCHIP 1-D monotonic cubic interpolator.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.interpolate._cubic.PchipInterpolator scipy.interpolate._cubic.CubicSpline scipy.interpolate._cubic.CubicHermiteSpline

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/interpolate/_cubic.py#364
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