scipy.interpolate.RectBivariateSpline¶
-
class
scipy.interpolate.
RectBivariateSpline
(x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0)[source]¶ Bivariate spline approximation over a rectangular mesh.
Can be used for both smoothing and interpolating data.
Parameters: - x,y : array_like
1-D arrays of coordinates in strictly ascending order.
- z : array_like
2-D array of data with shape (x.size,y.size).
- bbox : 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, ky : ints, optional
Degrees of the bivariate spline. Default is 3.
- s : float, optional
Positive smoothing factor defined for estimation condition:
sum((w[i]*(z[i]-s(x[i], y[i])))**2, axis=0) <= s
Default iss=0
, which is for interpolation.
See also
SmoothBivariateSpline
- a smoothing bivariate spline for scattered data
bisplrep
- an older wrapping of FITPACK
bisplev
- an older wrapping of FITPACK
UnivariateSpline
- a similar class for univariate spline interpolation
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].