scipy.interpolate.

SmoothBivariateSpline#

class scipy.interpolate.SmoothBivariateSpline(x, y, z, w=None, bbox=[None, None, None, None], kx=3, ky=3, s=None, eps=1e-16)[source]#

Smooth bivariate spline approximation.

Parameters:

x, y, zarray_like: 1-D sequences of data points (order is not important).
warray_like, optional: Positive 1-D sequence of weights, of same length as x, y and z.
bboxarray_like, optional: Sequence of length 4 specifying the boundary of the rectangular approximation domain. 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((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s Default s=len(w) which should be a good value if 1/w[i] is an estimate of the standard deviation of z[i].
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.