在scipy.interpolate.interp1d中，kind参数的不同值是什么意思？

Question

The SciPy documentation explains that interp1d 's kind argument can take the values 'linear' , 'nearest' , 'zero' , 'slinear' , 'quadratic' , 'cubic' . The last three are spline orders and 'linear' is self-explanatory. What do 'nearest' and 'zero' do?

Answer 1

• nearest "snaps" to the nearest data point.
• zero is a zero order spline. It's value at any point is the last raw value seen.
• linear performs linear interpolation and slinear uses a first order spline. They use different code and can produce similar but subtly different results .
• quadratic uses second order spline interpolation.
• cubic uses third order spline interpolation.

Note that the k parameter can also accept an integer specifying the order of spline interpolation.

---

import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate as interpolate

np.random.seed(6)
kinds = ('nearest', 'zero', 'linear', 'slinear', 'quadratic', 'cubic')

N = 10
x = np.linspace(0, 1, N)
y = np.random.randint(10, size=(N,))

new_x = np.linspace(0, 1, 28)
fig, axs = plt.subplots(nrows=len(kinds)+1, sharex=True)
axs[0].plot(x, y, 'bo-')
axs[0].set_title('raw')
for ax, kind in zip(axs[1:], kinds):
    new_y = interpolate.interp1d(x, y, kind=kind)(new_x)
    ax.plot(new_x, new_y, 'ro-')
    ax.set_title(kind)

plt.show()

Answer 2

'nearest' returns data point from X nearest to the argument, or interpolates function y=f(x) at the point x using the data point nearest to x

'zero' I would guess is equivalent to truncation of argument and thus using data point closest toward zero