RectBivariateSpline#
- class scipy.interpolate.RectBivariateSpline(x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0)[source]#
- Bivariate spline approximation over a rectangular mesh. - Can be used for both smoothing and interpolating data. - Parameters:
- x,yarray_like
- 1-D arrays of coordinates in strictly ascending order. Evaluated points outside the data range will be extrapolated. 
- zarray_like
- 2-D array of data with shape (x.size,y.size). 
- bboxarray_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, kyints, optional
- Degrees of the bivariate spline. Default is 3. 
- sfloat, optional
- Positive smoothing factor defined for estimation condition: - sum((z[i]-f(x[i], y[i]))**2, axis=0) <= swhere 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. - 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.