scipy.interpolate.SmoothBivariateSpline#
- class scipy.interpolate.SmoothBivariateSpline(x, y, z, w=None, bbox=[None, None, None, None], kx=3, ky=3, s=None, eps=1e-16)[source]#
Smooth bivariate spline approximation.
- Parameters
- x, y, zarray_like
1-D sequences of data points (order is not important).
- warray_like, optional
Positive 1-D sequence of weights, of same length as x, y and z.
- bboxarray_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, kyints, optional
Degrees of the bivariate spline. Default is 3.
- sfloat, optional
Positive smoothing factor defined for estimation condition:
sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s
Defaults=len(w)
which should be a good value if1/w[i]
is an estimate of the standard deviation ofz[i]
.- epsfloat, 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)
.Methods
__call__
(x, y[, dx, dy, grid])Evaluate the spline or its derivatives at given positions.
ev
(xi, yi[, dx, dy])Evaluate the spline at points
Return spline coefficients.
Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively.
Return weighted sum of squared residuals of the spline approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0)
integral
(xa, xb, ya, yb)Evaluate the integral of the spline over area [xa,xb] x [ya,yb].
partial_derivative
(dx, dy)Construct a new spline representing a partial derivative of this spline.