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Parameters
__call__(self, x, nu=0, ext=None)

Parameters

x : array_like

A 1-D array of points at which to return the value of the smoothed spline or its derivatives. Note: x can be unordered but the evaluation is more efficient if x is (partially) ordered.

nu : int

The order of derivative of the spline to compute.

ext : int

Controls the value returned for elements of x not in the interval defined by the knot sequence.

  • if ext=0 or 'extrapolate', return the extrapolated value.

  • if ext=1 or 'zeros', return 0

  • if ext=2 or 'raise', raise a ValueError

  • if ext=3 or 'const', return the boundary value.

The default value is 0, passed from the initialization of UnivariateSpline.

Evaluate spline (or its nu-th derivative) at positions x.

Examples

See :

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/_fitpack2.py#326
type: <class 'function'>
Commit: