If I have some measured data whose function I don't know (let's say it's either not important, or computationally difficult), such as
x2 = [0, 1, 10, 25, 30]
y2 = [5, 12, 50, 73, 23]
and I use numpy.interp to find the intermediate values, it gives me a linear interpolant between the points and I get a straight line:
xinterp = np.arange(31)
yinterp1 = np.interp(xinterp, x2, y2)
plt.scatter(xinterp, yinterp1)
plt.plot(x2, y2, color = 'red', marker = '.')
The example from the scipy.interpolate docs gives
x = np.linspace(0, 10, num=11, endpoint=True)
y = np.cos(-x**2/9.0)
f = interp1d(x, y)
f2 = interp1d(x, y, kind='cubic')
xnew = np.linspace(0, 10, num=41, endpoint=True)
plt.plot(x, y, 'o', xnew, f(xnew), '-', xnew, f2(xnew), '--')
plt.legend(['data', 'linear', 'cubic'], loc='best')
plt.show()
with different kinds of interpolation for a smoother curve. But what if I want the points in a smooth curve, rather than just the curve? Is there a function in NumPy or SciPy that can give the discrete points along the smoothed curve?