python numpy庫(kù)linspace相同間隔采樣的實(shí)現(xiàn)

更新時(shí)間：2020年02月25日 10:46:28 作者：brucewong0516

這篇文章主要介紹了python numpy庫(kù)linspace相同間隔采樣的實(shí)現(xiàn)，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

linspace可以用來(lái)實(shí)現(xiàn)相同間隔的采樣；

numpy.linspace(start,stop,num=50,endpoint=True,retstep=False, dtype=None)

返回num均勻分布的樣本，在[start, stop]。

Parameters(參數(shù)):

start : scalar(標(biāo)量) The starting value of the sequence(序列的起始點(diǎn)).

stop : scalar 序列的結(jié)束點(diǎn)，除非endpoint被設(shè)置為False，在這種情況下, the sequence consists of all but the last of num + 1 evenly spaced samples(該序列包括所有除了最后的num+1上均勻分布的樣本(感覺(jué)這樣翻譯有點(diǎn)坑)), 以致于stop被排除.當(dāng)endpoint is False的時(shí)候注意步長(zhǎng)的大小(下面有例子).

num : int, optional(可選)，生成的樣本數(shù)，默認(rèn)是50。必須是非負(fù)。

endpoint : bool, optional，如果是真，則一定包括stop，如果為False，一定不會(huì)有stop

retstep : bool, optional If True, return (samples, step), where step is the spacing between

samples.(看例子)

dtype : dtype, optional The type of the output array. If dtype is not given, infer the data type from the other input arguments(推斷這個(gè)輸入用例從其他的輸入中). New in version 1.9.0.

Returns:

samples : ndarray
There are num equally spaced samples in the closed
interval [start, stop] or the half-open
interval [start, stop) (depending on whether endpoint is True or False).

step : float(只有當(dāng)retstep設(shè)置為真的時(shí)候才會(huì)存在)
Only returned if retstep is True
Size of spacing between samples.

當(dāng)endpoint被設(shè)置為False的時(shí)候

import numpy as np
np.linspace(1, 10, 10)
array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
np.linspace(1, 10, 10, endpoint = False)
array([ 1. , 1.9, 2.8, 3.7, 4.6, 5.5, 6.4, 7.3, 8.2, 9.1])
In [4]: np.linspace(1, 10, 10, endpoint = False, retstep= True)
Out[4]: (array([ 1. , 1.9, 2.8, 3.7, 4.6, 5.5, 6.4, 7.3, 8.2, 9.1]), 0.9)

官網(wǎng)的例子Examples

>>> np.linspace(2.0, 3.0, num=5)
 array([ 2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
 array([ 2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
 (array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)

Graphical illustration:

>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()