# 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,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), max(x), min(y), max(y)]`.

    kx, ky ints, optional

      Degrees of the bivariate spline. Default is 3.

    s float, 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].

scipy.interpolate.RegularGridInterpolator.__call__ scipy.interpolate.RectBivariateSpline.__call__