What is the difference of use cases between scipy.interpolate.splrep and scipy.interpolate.UnivariateSpline?

Question

• scipy.interpolate.splrep(x, y, w=None, xb=None, xe=None, k=3, task=0, s=None, t=None, full_output=0, per=0, quiet=1): Find the B-spline representation of 1-D curve. Given the set of data points (x[i], y[i]) determine a smooth spline approximation of degree k on the interval xb <= x <= xe.
• class scipy.interpolate.UnivariateSpline(x, y, w=None, bbox=[None, None], k=3, s=None, ext=0, check_finite=False): One-dimensional smoothing spline fit to a given set of data points. Fits a spline y = spl(x) of degree k to the provided x, y data. s specifies the number of knots by specifying a smoothing condition.

It looks like quite the same, except this note in the UnivariateSpline manual:

splrep: An older, non object-oriented wrapping of FITPACK

Return values are not described for UnivariateSpline.

So, is UnivariateSpline a replacement for splrep, or are use cases not totally the same?

Answer 1

That comment in the docs is outdated. Both interfaces wrap the same FITPACK implementation. So the difference us only that one interface is object-oriented, and the other one uses functions. (and is therefore easier to use in conjunction with the BSpline object)