Finding derivative of a spline in Python using SciPy
In this tutorial, we will learn how to find derivative of a spline in Python using SciPy.
Here we have used:
- SciPy Module
First of all, we have to be familiar with the word spline. The spline is a piecewise polynomial function and this function is used in interpolating problems, specifically spline interpolation is mostly preferred as a method of estimating values between known data points.
The derivative of a spline – SciPy
here, we are focusing on the cubic spline. we can easily get cubic spline of any data by using the following library
from scipy.interpolate import CubicSpline
here, for the x-axis, we are considering an array of nine elements
and for the y-axis, we are considering the array of sine values of nine elements.
from scipy.interpolate import CubicSpline import matplotlib.pyplot as plt import numpy as np x = np.arange(10) y = np.sin(x) cs = CubicSpline(x, y) s = np.arange(-1, 10, 0.1) fig, p = plt.subplots(figsize=(8, 4)) p.plot(x, y, 'o', label='value') p.plot(s, np.sin(s), label='original') p.plot(s, cs(s), label="C") p.plot(s, cs(s, 1), label="C1") p.plot(s, cs(s, 2), label="linear") p.set_xlim(-0.5, 14) p.legend(loc='upper right', ncol=3) plt.show()
Changes in values can be observed in the graph.
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