scipy.interpolate.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, z : array_like
1-D sequences of data points (order is not important).
- tx, ty : array_like
Strictly ordered 1-D sequences of knots coordinates.
- w : array_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, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
- eps : float, optional
A threshold for determining the effective rank of an over-determined linear system of equations. eps should have a value between 0 and 1, the default is 1e-16.
See also
bisplrep
- an older wrapping of FITPACK
bisplev
- an older wrapping of FITPACK
UnivariateSpline
- a similar class for univariate spline interpolation
SmoothBivariateSpline
- create a smoothing BivariateSpline
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 get_coeffs
()Return spline coefficients. get_knots
()Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively. get_residual
()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].