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scipy.interpolate.SmoothBivariateSpline.__call__

# 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_like, optional

  > Positive 1-D sequence of weights, of same length as x, y and z.

  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`
    an older wrapping of FITPACK
  `bisplev`
    an older wrapping of FITPACK
  `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

  `__call__`(x, y[, mth, 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
  `integral`(xa, xb, ya, yb) | Evaluate the integral of the spline over area [xa,xb] x [ya,yb].