kind参数的不同值在scipy.interpolate.interp1d中意味着什么? [英] What do the different values of the kind argument mean in scipy.interpolate.interp1d?

问题描述

SciPy文档解释了interp1d的kind参数可以采用值‘linear’，‘nearest’，‘zero’，‘slinear’，‘quadratic’，‘cubic’.最后三个是样条顺序，'linear'是不言自明的. 'nearest'和'zero'做什么?

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?

推荐答案

• nearest快照"到最近的数据点.
• zero是零阶样条.它的值在任何时候都是最后看到的原始值.
• linear执行线性插值，而slinear使用第一个 订单样条.他们使用不同的代码，并且可以产生相似但略有不同的结果.
• quadratic使用二阶样条插值.
• cubic使用三阶样条插值.
• 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.

请注意，k参数还可以接受指定样条插值顺序的整数.

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()

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