Notes

The length of x, y and z should be at least (kx+1) * (ky+1) .

Parameters

x, y, z : array_like

1-D sequences of data points (order is not important).

tx, ty : array_like

Strictly ordered 1-D sequences of knots coordinates.

w : array_like, optional

Positive 1-D array of weights, of the same length as x, y and z.

bbox : (4,) array_like, optional

Sequence of length 4 specifying the boundary of the rectangular approximation domain. By default, bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)] .

kx, ky : ints, optional

Degrees of the bivariate spline. Default is 3.

eps : float, optional

A threshold for determining the effective rank of an over-determined linear system of equations. :None:None:`eps` should have a value within the open interval (0, 1) , the default is 1e-16.

Weighted least-squares bivariate spline approximation.

See Also

BivariateSpline

a base class for bivariate splines.

LSQSphereBivariateSpline

a bivariate spline in spherical coordinates using weighted least-squares fitting

RectBivariateSpline

a bivariate spline over a rectangular mesh.

RectSphereBivariateSpline

a bivariate spline over a rectangular mesh on a sphere

SmoothBivariateSpline

a smoothing bivariate spline through the given points

SmoothSphereBivariateSpline

a smoothing bivariate spline in spherical coordinates

UnivariateSpline

a smooth univariate spline to fit a given set of data points.

bisplev

a function to evaluate a bivariate B-spline and its derivatives

bisplrep

a function to find a bivariate B-spline representation of a surface

Examples

See :

Back References

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

scipy.interpolate._fitpack2.RectSphereBivariateSpline scipy.interpolate._fitpack2.SmoothBivariateSpline scipy.interpolate._fitpack2.BivariateSpline scipy.interpolate._fitpack2.InterpolatedUnivariateSpline scipy.interpolate._fitpack2.LSQSphereBivariateSpline scipy.interpolate._fitpack2.RectBivariateSpline scipy.interpolate._fitpack2.SmoothSphereBivariateSpline scipy.interpolate._fitpack2.UnivariateSpline

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

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