scipy.interpolate.RectBivariateSpline

class scipy.interpolate.RectBivariateSpline(x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0)

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) <= s where 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

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.