# scipy.interpolate.LSQBivariateSpline

class scipy.interpolate.LSQBivariateSpline(x, y, z, tx, ty, w=None, bbox=[None, None, None, None], kx=3, ky=3, eps=None)

  Weighted least-squares bivariate spline approximation.

  Parameters
    x, y, z array_like

      1-D sequences of data points (order is not important).

    tx, ty array_like

      Strictly ordered 1-D sequences of knots coordinates.

    w array_like, optional

      Positive 1-D array of weights, of the same length as x, y and z.

    bbox(4,) 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.

    eps float, optional

      A threshold for determining the effective rank of an over-determined linear system of equations. eps should have a value within the open interval `(0, 1)`, the default is 1e-16.

  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

  `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

  `RectBivariateSpline`

    a bivariate spline over a rectangular mesh.

  `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

  Notes

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

  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.SmoothSphereBivariateSpline.get_residual scipy.interpolate.LSQBivariateSpline.__call__