Can be used for both smoothing and interpolating data.
1-D arrays of coordinates in strictly ascending order.
2-D array of data with shape (x.size,y.size).
Sequence of length 4 specifying the boundary of the rectangular approximation domain. By default, bbox=[min(x), max(x), min(y), max(y)]
.
Degrees of the bivariate spline. Default is 3.
Positive smoothing factor defined for estimation condition: sum((z[i]-f(x[i], y[i]))**2, axis=0) <= s
where f is a spline function. Default is s=0
, which is for interpolation.
Bivariate spline approximation over a rectangular mesh.
BivariateSpline
a base class for bivariate splines.
LSQBivariateSpline
a bivariate spline using weighted least-squares fitting
LSQSphereBivariateSpline
a bivariate spline in spherical coordinates using weighted least-squares fitting
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
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.interpolate._fitpack2.RectSphereBivariateSpline
scipy.interpolate._interpolate.interpn
scipy.interpolate._fitpack2.SmoothBivariateSpline
scipy.interpolate._fitpack2.BivariateSpline
scipy.interpolate._interpolate.interp2d
scipy.interpolate._fitpack2.LSQSphereBivariateSpline
scipy.interpolate._fitpack2.LSQBivariateSpline
scipy.interpolate._fitpack2.SmoothSphereBivariateSpline
scipy.interpolate._fitpack2.UnivariateSpline
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