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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. Evaluated points outside the data range will be extrapolated.

    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, which means the start and end spline knots of each dimension are set by these values. 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

  Notes

  If the input data is such that input dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating.

  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].
  `partial_derivative`(dx, dy) | Construct a new spline representing a partial derivative of this spline.