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scipy tutorial - Baustatik-Info-Server

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SciPy Reference Guide, Release 0.8.dev<br />

See also:<br />

splprep, splev, sproot, spalde, splint - evaluation, roots, integral bisplrep, bisplev - bivariate splines UnivariateSpline,<br />

BivariateSpline - an alternative wrapping<br />

Notes:<br />

of the FITPACK functions<br />

Based on algorithms described in:<br />

Dierckx P.<br />

[An algorithm for smoothing, differentiation and integ-] ration of experimental data using spline<br />

functions, J.Comp.Appl.Maths 1 (1975) 165-184.<br />

Dierckx P.<br />

[A fast algorithm for smoothing data on a rectangular] grid while using spline functions, SIAM<br />

J.Numer.Anal. 19 (1982) 1286-1304.<br />

Dierckx P.<br />

[An improved algorithm for curve fitting with spline] functions, report tw54, Dept. Computer Science,K.U.<br />

Leuven, 1981.<br />

Dierckx P.<br />

[Curve and surface fitting with splines, Monographs on] Numerical Analysis, Oxford University<br />

Press, 1993.<br />

splprep(x, w=None, u=None, ub=None, ue=None, k=3, task=0, s=None, t=None, full_output=0, nest=None,<br />

per=0, quiet=1)<br />

Find the B-spline representation of an N-dimensional curve.<br />

Description:<br />

Inputs:<br />

Given a list of N rank-1 arrays, x, which represent a curve in N-dimensional space parametrized<br />

by u, find a smooth approximating spline curve g(u). Uses the FORTRAN routine parcur from<br />

FITPACK<br />

x – A list of sample vector arrays representing the curve. u – An array of parameter values. If not<br />

given, these values are<br />

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

u[i] = v[i] / v[M-1]<br />

ub, ue – The end-points of the parameters interval. Defaults to<br />

u[0] and u[-1].<br />

k – Degree of the spline. Cubic splines are recommended. Even values of<br />

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

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