Direct spline interpolation of noisy data may result in a curve with
unwanted oscillations. This is particularly bad if the slope of the
curve is important.
A better approach is to reduce the degrees of freedom for the spline
and use the method of least squares to fit the spline to the noisy data.
The deegres of freedom are connected to the number of breaks (knots),
so the smoothing effect is controlled by the selection of breaks.
SPLINEFIT:
 A curve fitting tool based on Bsplines
 Splines on ppform (piecewise polynomial)
 Any spline order (cubic splines by default)
 Periodic boundary conditions
 Linear constraints on function values and derivatives
 Robust fitting scheme
 Operates on ND arrays in the same way as SPLINE
 Nonuniform distributions of breaks
MFILES ALSO INCLUDED:
examples  Examples for splinefit
ppdiff  Differentiate piecewise polynomial
ppint  Integrate piecewise polynomial
