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)[source]¶ Find the Bspline representation of a 1D curve.
Given the set of data points
(x[i], y[i])
determine a smooth spline approximation of degree k on the intervalxb <= x <= xe
. Parameters
 x, yarray_like
The data points defining a curve y = f(x).
 warray_like, optional
Strictly positive rank1 array of weights the same length as x and y. The weights are used in computing the weighted leastsquares spline fit. If the errors in the y values have standarddeviation given by the vector d, then w should be 1/d. Default is ones(len(x)).
 xb, xefloat, optional
The interval to fit. If None, these default to x[0] and x[1] respectively.
 kint, optional
The degree of the spline fit. It is recommended to use cubic splines. Even values of k should be avoided especially with small s values. 1 <= k <= 5
 task{1, 0, 1}, optional
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.
 sfloat, optional
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 standarddeviation of y, then a good s value should be found in the range (msqrt(2*m),m+sqrt(2*m)) where m is the number of datapoints in x, y, and w. default : s=msqrt(2*m) if weights are supplied. s = 0.0 (interpolating) if no weights are supplied.
 tarray_like, optional
The knots needed for task=1. If given then task is automatically set to 1.
 full_outputbool, optional
If nonzero, then return optional outputs.
 perbool, optional
If nonzero, data points are considered periodic with period x[m1]  x[0] and a smooth periodic spline approximation is returned. Values of y[m1] and w[m1] are not used.
 quietbool, optional
Nonzero to suppress messages. This parameter is deprecated; use standard Python warning filters instead.
 Returns
 tcktuple
A tuple (t,c,k) containing the vector of knots, the Bspline coefficients, and the degree of the spline.
 fparray, optional
The weighted sum of squared residuals of the spline approximation.
 ierint, optional
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.
 msgstr, optional
A message corresponding to the integer flag, ier.
See also
Notes
See
splev
for evaluation of the spline and its derivatives. Uses the FORTRAN routinecurfit
from FITPACK.The user is responsible for assuring that the values of x are unique. Otherwise,
splrep
will not return sensible results.If provided, knots t must satisfy the SchoenbergWhitney conditions, i.e., there must be a subset of data points
x[j]
such thatt[j] < x[j] < t[j+k+1]
, forj=0, 1,...,nk2
.This routine zeropads the coefficients array
c
to have the same length as the array of knotst
(the trailingk + 1
coefficients are ignored by the evaluation routines,splev
andBSpline
.) This is in contrast withsplprep
, which does not zeropad the coefficients.References
Based on algorithms described in [1], [2], [3], and [4]:
 1
P. Dierckx, “An algorithm for smoothing, differentiation and integration of experimental data using spline functions”, J.Comp.Appl.Maths 1 (1975) 165184.
 2
P. Dierckx, “A fast algorithm for smoothing data on a rectangular grid while using spline functions”, SIAM J.Numer.Anal. 19 (1982) 12861304.
 3
P. Dierckx, “An improved algorithm for curve fitting with spline functions”, report tw54, Dept. Computer Science,K.U. Leuven, 1981.
 4
P. Dierckx, “Curve and surface fitting with splines”, Monographs on Numerical Analysis, Oxford University Press, 1993.
Examples
>>> import matplotlib.pyplot as plt >>> from scipy.interpolate import splev, splrep >>> x = np.linspace(0, 10, 10) >>> y = np.sin(x) >>> spl = splrep(x, y) >>> x2 = np.linspace(0, 10, 200) >>> y2 = splev(x2, spl) >>> plt.plot(x, y, 'o', x2, y2) >>> plt.show()