Classe « RectBivariateSpline »

Informations générales

Héritage

builtins.object
    _BivariateSplineBase
        BivariateSpline
            RectBivariateSpline

Définition

class RectBivariateSpline(BivariateSpline):

help(RectBivariateSpline)

Bivariate spline approximation over a rectangular mesh.

Can be used for both smoothing and interpolating data.

Parameters
----------
x,y : array_like
    1-D arrays of coordinates in strictly ascending order.
    Evaluated points outside the data range will be extrapolated.
z : array_like
    2-D array of data with shape (x.size,y.size).
bbox : array_like, optional
    Sequence of length 4 specifying the boundary of the rectangular
    approximation domain, which means the start and end spline knots of
    each dimension are set by these values. By default,
    ``bbox=[min(x), max(x), min(y), max(y)]``.
kx, ky : ints, optional
    Degrees of the bivariate spline. Default is 3.
s : float, optional
    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.

See Also
--------
BivariateSpline :
    a base class for bivariate splines.
UnivariateSpline :
    a smooth univariate spline to fit a given set of data points.
SmoothBivariateSpline :
    a smoothing bivariate spline through the given points
LSQBivariateSpline :
    a bivariate spline using weighted least-squares fitting
RectSphereBivariateSpline :
    a bivariate spline over a rectangular mesh on a sphere
SmoothSphereBivariateSpline :
    a smoothing bivariate spline in spherical coordinates
LSQSphereBivariateSpline :
    a bivariate spline in spherical coordinates using weighted
    least-squares fitting
bisplrep :
    a function to find a bivariate B-spline representation of a surface
bisplev :
    a function to evaluate a bivariate B-spline and its derivatives

Notes
-----

If the input data is such that input dimensions have incommensurate
units and differ by many orders of magnitude, the interpolant may have
numerical artifacts. Consider rescaling the data before interpolating.