LSQBivariateSpline — SciPy v1.15.2 Manual (original) (raw)
scipy.interpolate.
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.
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 |
| 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]. |
| partial_derivative(dx, dy) | Construct a new spline representing a partial derivative of this spline. |