本文介绍了kind参数的不同值在scipy.interpolate.interp1d中意味着什么?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
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 andslinear
uses a firstorder 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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