- class scipy.interpolate.LSQBivariateSpline(x, y, z, tx, ty, w=None, bbox=[None, None, None, None], kx=3, ky=3, eps=None)#
Weighted least-squares bivariate spline approximation.
- x, y, zarray_like
1-D sequences of data points (order is not important).
- tx, tyarray_like
Strictly ordered 1-D sequences of knots coordinates.
- warray_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,
- kx, kyints, optional
Degrees of the bivariate spline. Default is 3.
- 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.
a base class for bivariate splines.
a smooth univariate spline to fit a given set of data points.
a smoothing bivariate spline through the given points
a bivariate spline over a rectangular mesh on a sphere
a smoothing bivariate spline in spherical coordinates
a bivariate spline in spherical coordinates using weighted least-squares fitting
a bivariate spline over a rectangular mesh.
a function to find a bivariate B-spline representation of a surface
a function to evaluate a bivariate B-spline and its derivatives
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.
__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].
Construct a new spline representing a partial derivative of this spline.