One-dimensional smoothing spline fit to a given set of data points.
Fits a spline y=s(x) of degree k to the provided x, y data. s specifies the number of knots by specifying a smoothing condition.
Parameters : | x : array_like
y : array_like
w : array_like, optional
bbox : array_like, optional
k : int, optional
s : float or None, optional
|
---|
See also
Notes
The number of data points must be larger than the spline degree k.
Examples
>>> 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.
Methods
__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 the positions of (boundary and interior) |
get_residual() | Return weighted sum of squared residuals of the spline |
integral(a, b) | Return definite integral of the spline between two |
roots() | Return the zeros of the spline. |
set_smoothing_factor(s) | Continue spline computation with the given smoothing |