scipy.interpolate.

RectBivariateSpline#

class scipy.interpolate.RectBivariateSpline(x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0, maxit=20)[source]#

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.
maxitint, optional: The maximal number of iterations maxit allowed for finding a smoothing spline with fp=s. Default is maxit=20.

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.