numpy.linspace使用详解

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

在指定的间隔内返回均匀间隔的数字。

返回num均匀分布的样本，在[start, stop]。

这个区间的端点可以任意的被排除在外。

Parameters(参数):

start : scalar(标量) The starting value of the sequence(序列的起始点). stop : scalar 序列的结束点，除非endpoint被设置为False，在这种情况下, the sequence consists of all but the last of num + 1 evenly spaced samples(该序列包括所有除了最后的num+1上均匀分布的样本(感觉这样翻译有点坑)), 以致于stop被排除.当endpoint is False的时候注意步长的大小(下面有例子). num : int, optional(可选) 生成的样本数，默认是50。必须是非负。 endpoint : bool, optional 如果是真，则一定包括stop，如果为False，一定不会有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(推断这个输入用例从其他的输入中). 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(只有当retstep设置为真的时候才会存在) Only returned if retstep is True Size of spacing between samples.

See also

arangeSimilar to linspace, but uses a step size (instead of the number of samples).arange使用的是步长，而不是样本的数量 logspaceSamples uniformly distributed in log space.

当endpoint被设置为False的时候
>>> 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)

官网的例子

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()