LSQBivariateSpline#
- class scipy.interpolate.LSQBivariateSpline(x, y, z, tx, ty, w=None, bbox=[None, None, None, None], kx=3, ky=3, eps=None)[source]#
- Weighted least-squares bivariate spline approximation. - Parameters:
- 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, - bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)].
- 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.
 
 - 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 
- 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. - 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.