scipy.interpolate.splprep

scipy.interpolate.splprep(x, w=None, u=None, ub=None, ue=None, k=3, task=0, s=None, t=None, full_output=0, nest=None, per=0, quiet=1)

Find the B-spline representation of an N-dimensional curve.

Description:

Given a list of N rank-1 arrays, x, which represent a curve in N-dimensional space parametrized by u, find a smooth approximating spline curve g(u). Uses the FORTRAN routine parcur from FITPACK

Inputs:

x – A list of sample vector arrays representing the curve. u – An array of parameter values. If not given, these values are

calculated automatically as (M = len(x[0])): v[0] = 0 v[i] = v[i-1] + distance(x[i],x[i-1]) u[i] = v[i] / v[M-1]

ub, ue – The end-points of the parameters interval. Defaults to

u[0] and u[-1].

k – Degree of the spline. Cubic splines are recommended. Even values of

k should be avoided especially with a small s-value. 1 <= k <= 5.

task – If task==0 find t and c for a given smoothing factor, s.

If task==1 find t and c for another value of the smoothing factor,

s. There must have been a previous call with task=0 or task=1 for the same set of data.

If task=-1 find the weighted least square spline for a given set of

knots, t.

s – A smoothing condition. The amount of smoothness is determined by

satisfying the conditions: sum((w * (y - g))**2,axis=0) <= s where g(x) is the smoothed interpolation of (x,y). The user can use s to control the tradeoff between closeness and smoothness of fit. Larger s means more smoothing while smaller values of s indicate less smoothing. Recommended values of s depend on the weights, w. If the weights represent the inverse of the standard-deviation of y, then a good s value should be found in the range (m-sqrt(2*m),m+sqrt(2*m)) where m is the number of datapoints in x, y, and w.

t – The knots needed for task=-1. full_output – If non-zero, then return optional outputs. nest – An over-estimate of the total number of knots of the spline to

help in determining the storage space. By default nest=m/2. Always large enough is nest=m+k+1.

per – If non-zero, data points are considered periodic with period

x[m-1] - x[0] and a smooth periodic spline approximation is returned. Values of y[m-1] and w[m-1] are not used.

quiet – Non-zero to suppress messages.

Outputs: (tck, u, {fp, ier, msg})

tck – (t,c,k) a tuple containing the vector of knots, the B-spline

coefficients, and the degree of the spline.

u – An array of the values of the parameter.

fp – The weighted sum of squared residuals of the spline approximation. ier – 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 – A message corresponding to the integer flag, ier.

Remarks:

SEE splev for evaluation of the spline and its derivatives.

See also:

splrep, splev, sproot, spalde, splint - evaluation, roots, integral bisplrep, bisplev - bivariate splines UnivariateSpline, BivariateSpline - an alternative wrapping

of the FITPACK functions

Notes:

Dierckx P. : Algorithms for smoothing data with periodic and

parametric splines, Computer Graphics and Image Processing 20 (1982) 171-184.

Dierckx P. : Algorithms for smoothing data with periodic and param-

etric splines, report tw55, Dept. Computer Science, K.U.Leuven, 1981.

Dierckx P. : Curve and surface fitting with splines, Monographs on

Numerical Analysis, Oxford University Press, 1993.