scipy.interpolate.UnivariateSpline.
derivative#
- UnivariateSpline.derivative(n=1)[source]#
- Construct a new spline representing the derivative of this spline. - Parameters:
- nint, optional
- Order of derivative to evaluate. Default: 1 
 
- Returns:
- splineUnivariateSpline
- Spline of order k2=k-n representing the derivative of this spline. 
 
 - See also - Notes - Added in version 0.13.0. - Examples - This can be used for finding maxima of a curve: - >>> import numpy as np >>> from scipy.interpolate import UnivariateSpline >>> x = np.linspace(0, 10, 70) >>> y = np.sin(x) >>> spl = UnivariateSpline(x, y, k=4, s=0) - Now, differentiate the spline and find the zeros of the derivative. (NB: - sprootonly works for order 3 splines, so we fit an order 4 spline):- >>> spl.derivative().roots() / np.pi array([ 0.50000001, 1.5 , 2.49999998]) - This agrees well with roots \(\pi/2 + n\pi\) of \(\cos(x) = \sin'(x)\).