# scipy.interpolate.splrep¶

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.

Description:

Given the set of data points (x[i], y[i]) determine a smooth spline approximation of degree k on the interval xb <= x <= xe. The coefficients, c, and the knot points, t, are returned. Uses the FORTRAN routine curfit from FITPACK.

Inputs:

x, y – The data points defining a curve y = f(x). w – Strictly positive rank-1 array of weights the same length as x and y.

The weights are used in computing the weighted least-squares spline fit. If the errors in the y values have standard-deviation given by the vector d, then w should be 1/d. Default is ones(len(x)).
xb, xe – The interval to fit. If None, these default to x and x[-1]
respectively.
k – The order of the spline fit. It is recommended to use cubic splines.
Even order splines should be avoided especially with small s values. 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 (t will be stored an used internally)
If task=-1 find the weighted least square spline for
a given set of knots, t. These should be interior knots as knots on the ends will be added automatically.
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. default : s=m-sqrt(2*m) if weights are supplied.

s = 0.0 (interpolating) if no weights are supplied.
t – The knots needed for task=-1. If given then task is automatically
set to -1.

full_output – If non-zero, then return optional outputs. per – If non-zero, data points are considered periodic with period

x[m-1] - x 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, {fp, ier, msg})

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

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.

Example:

x = linspace(0, 10, 10) y = sin(x) tck = splrep(x, y) x2 = linspace(0, 10, 200) y2 = splev(x2, tck) plot(x, y, ‘o’, x2, y2)

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

of the FITPACK functions

Notes:

Based on algorithms described in:
Dierckx P. : An algorithm for smoothing, differentiation and integ-
ration of experimental data using spline functions, J.Comp.Appl.Maths 1 (1975) 165-184.
Dierckx P. : A fast algorithm for smoothing data on a rectangular
grid while using spline functions, SIAM J.Numer.Anal. 19 (1982) 1286-1304.
Dierckx P. : An improved algorithm for curve fitting with spline
functions, report tw54, Dept. Computer Science,K.U. Leuven, 1981.
Dierckx P. : Curve and surface fitting with splines, Monographs on
Numerical Analysis, Oxford University Press, 1993.

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