One-dimensional interpolating spline for a given set of data points.
Fits a spline y=s(x) of degree k to the provided x, y data. Spline function passes through all provided points. Equivalent to UnivariateSpline with s=0.
x : array_like
y : array_like
w : array_like, optional
bbox : array_like, optional
k : int, optional
The number of data points must be larger than the spline degree k.
>>> from numpy import linspace,exp >>> from numpy.random import randn >>> from scipy.interpolate import UnivariateSpline >>> x = linspace(-3, 3, 100) >>> y = exp(-x**2) + randn(100)/10 >>> s = UnivariateSpline(x, y, s=1) >>> xs = linspace(-3, 3, 1000) >>> ys = s(xs)
xs,ys is now a smoothed, super-sampled version of the noisy gaussian x,y
|__call__(x[, nu])||Evaluate spline (or its nu-th derivative) at positions x.|
|derivatives(x)||Return all derivatives of the spline at the point x.|
|get_coeffs()||Return spline coefficients.|
|get_knots()||Return positions of (boundary and interior) knots of the spline.|
|get_residual()||Return weighted sum of squared residuals of the spline|
|integral(a, b)||Return definite integral of the spline between two given points.|
|roots()||Return the zeros of the spline.|
|set_smoothing_factor(s)||Continue spline computation with the given smoothing|