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, 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.

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.