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# scipy.interpolate.SmoothBivariateSpline

class `scipy.interpolate.``SmoothBivariateSpline`(x, y, z, w=None, bbox=[, None, None, None, None], kx=3, ky=3, s=None, eps=None)

  Smooth bivariate spline approximation.

  Parameters : | x, y, z : array_like 1-D sequences of data points (order is not important). w : array_lie, optional Positive 1-D sequence of weights. 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 `s=len(w)` which should be a good value if 1/w[i] is an estimate of the standard deviation of z[i]. eps : float, optional A threshold for determining the effective rank of an over-determined linear system of equations. eps should have a value between 0 and 1, the default is 1e-16.

  See also

  `bisplrep`, `bisplev`

  `UnivariateSpline`
    a similar class for univariate spline interpolation
  `LSQUnivariateSpline`
    to create a BivariateSpline using weighted

  Notes

  The length of x, y and z should be at least `(kx+1) * (ky+1)`.

  Methods

  `ev` |
  `get_coeffs` |
  `get_knots` |
  `get_residual` |
  `integral` |

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