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bisplrep(x, y, z, w=None, xb=None, xe=None, yb=None, ye=None, kx=3, ky=3, task=0, s=None, eps=1e-16, tx=None, ty=None, full_output=0, nxest=None, nyest=None, quiet=1)

Given a set of data points (x[i], y[i], z[i]) representing a surface z=f(x,y), compute a B-spline representation of the surface. Based on the routine SURFIT from FITPACK.

Notes

See bisplev to evaluate the value of the B-spline given its tck representation.

Parameters

x, y, z : ndarray: Rank-1 arrays of data points.
w : ndarray, optional: Rank-1 array of weights. By default w=np.ones(len(x)) .
xb, xe : float, optional: End points of approximation interval in x. By default xb = x.min(), xe=x.max() .
yb, ye : float, optional: End points of approximation interval in y. By default yb=y.min(), ye = y.max() .
kx, ky : int, optional: The degrees of the spline (1 <= kx, ky <= 5). Third order (kx=ky=3) is recommended.
task : int, optional: If task=0, find knots in x and y and coefficients for a given smoothing factor, s. If task=1, find knots and coefficients for another value of the smoothing factor, s. bisplrep must have been previously called with task=0 or task=1. If task=-1, find coefficients for a given set of knots tx, ty.
s : float, optional: A non-negative smoothing factor. If weights correspond to the inverse of the standard-deviation of the errors in z, then a good s-value should be found in the range (m-sqrt(2*m),m+sqrt(2*m)) where m=len(x).
eps : float, optional: A threshold for determining the effective rank of an over-determined linear system of equations (0 < eps < 1). :None:None:`eps` is not likely to need changing.
tx, ty : ndarray, optional: Rank-1 arrays of the knots of the spline for task=-1
full_output : int, optional: Non-zero to return optional outputs.
nxest, nyest : int, optional: Over-estimates of the total number of knots. If None then nxest = max(kx+sqrt(m/2),2*kx+3) , nyest = max(ky+sqrt(m/2),2*ky+3) .
quiet : int, optional: Non-zero to suppress printing of messages. This parameter is deprecated; use standard Python warning filters instead.

Returns

tck : array_like: A list [tx, ty, c, kx, ky] containing the knots (tx, ty) and coefficients (c) of the bivariate B-spline representation of the surface along with the degree of the spline.
fp : ndarray: The weighted sum of squared residuals of the spline approximation.
ier : int: An integer flag about splrep success. Success is indicated if ier<=0. If ier in [1,2,3] an error occurred but was not raised. Otherwise an error is raised.
msg : str: A message corresponding to the integer flag, ier.

Find a bivariate B-spline representation of a surface.

Examples

Examples are given in the tutorial <tutorial-interpolate_2d_spline> .

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.interpolate._fitpack2.BivariateSpline scipy.interpolate._fitpack2.RectBivariateSpline scipy.interpolate._fitpack2.SphereBivariateSpline scipy.interpolate._fitpack2.SmoothBivariateSpline scipy.interpolate._fitpack_impl.splint scipy.interpolate._fitpack_impl.sproot scipy.interpolate._fitpack_py.splev scipy.interpolate._fitpack_impl.splev scipy.interpolate._fitpack2.UnivariateSpline scipy.interpolate._fitpack2.RectSphereBivariateSpline scipy.interpolate._fitpack2._BivariateSplineBase scipy.interpolate._fitpack_impl.splrep scipy.interpolate._fitpack2.LSQSphereBivariateSpline scipy.interpolate._fitpack2.LSQBivariateSpline scipy.interpolate._fitpack2.SmoothSphereBivariateSpline scipy.interpolate._fitpack_impl.splprep scipy.interpolate._fitpack_py.splrep scipy.interpolate._interpolate.interp2d scipy.interpolate._fitpack_py.sproot scipy.interpolate._fitpack_py.splint

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