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Parameters
__call__(self, theta, phi, dtheta=0, dphi=0, grid=True)

Parameters

theta, phi : array_like

Input coordinates.

If :None:None:`grid` is False, evaluate the spline at points (theta[i], phi[i]), i=0, ..., len(x)-1 . Standard Numpy broadcasting is obeyed.

If :None:None:`grid` is True: evaluate spline at the grid points defined by the coordinate arrays theta, phi. The arrays must be sorted to increasing order.

dtheta : int, optional

Order of theta-derivative

versionadded
dphi : int

Order of phi-derivative

versionadded
grid : bool

Whether to evaluate the results on a grid spanned by the input arrays, or at points specified by the input arrays.

versionadded

Evaluate the spline or its derivatives at given positions.

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#1402
type: <class 'function'>
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