Classe « SmoothBivariateSpline »

Informations générales

Héritage

builtins.object
    _BivariateSplineBase
        BivariateSpline
            SmoothBivariateSpline

Définition

class SmoothBivariateSpline(BivariateSpline):

help(SmoothBivariateSpline)

Smooth bivariate spline approximation.

Parameters
----------
x, y, z : array_like
    1-D sequences of data points (order is not important).
w : array_like, optional
    Positive 1-D sequence of weights, of same length as `x`, `y` and `z`.
bbox : array_like, optional
    Sequence of length 4 specifying the boundary of the rectangular
    approximation domain.  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((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s``
    Default ``s=len(w)`` which should be a good value if ``1/w[i]`` is an
    estimate of the standard deviation of ``z[i]``.
eps : float, optional
    A threshold for determining the effective rank of an over-determined
    linear system of equations. `eps` should have a value within the open
    interval ``(0, 1)``, the default is 1e-16.

See Also
--------
BivariateSpline :
    a base class for bivariate splines.
UnivariateSpline :
    a smooth univariate spline to fit a given set of data 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
RectBivariateSpline :
    a bivariate spline over a rectangular mesh
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
-----
The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.

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.

This routine constructs spline knot vectors automatically via the FITPACK
algorithm. The spline knots may be placed away from the data points. For
some data sets, this routine may fail to construct an interpolating spline,
even if one is requested via ``s=0`` parameter. In such situations, it is
recommended to use `bisplrep` / `bisplev` directly instead of this routine
and, if needed, increase the values of ``nxest`` and ``nyest`` parameters
of `bisplrep`.

For linear interpolation, prefer `LinearNDInterpolator`.
See ``https://gist.github.com/ev-br/8544371b40f414b7eaf3fe6217209bff``
for discussion.