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# 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,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 is `s=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[, mth]) | Evaluate spline at the grid points defined by the coordinate arrays
  `ev`(xi, yi) | Evaluate spline at points (x[i], y[i]), i=0,...,len(x)-1
  `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
  `integral`(xa, xb, ya, yb) | Evaluate the integral of the spline over area [xa,xb] x [ya,yb].

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