help numpy.linspace

版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。

本文链接： https://blog.csdn.net/COCO56/article/details/100515913

Help on function linspace in module numpy:
linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)
  Return evenly spaced numbers over a specified interval.
  Returns num evenly spaced samples, calculated over the interval [start, stop].
The endpoint of the interval can optionally be excluded.
versionchanged:: 1.16.0
Non-scalar start and stop are now supported.

Parameters

• start : array_like
  The starting value of the sequence.
• stop : array_like
  The end value of the sequence, unless endpoint is set to False.
  In that case, the sequence consists of all but the last of num + 1
  evenly spaced samples, so that stop is excluded. Note that the step
  size changes when endpoint is False.
• num : int, optional
  Number of samples to generate. Default is 50. Must be non-negative.
• endpoint : bool, optional
  If True, stop is the last sample. Otherwise, it is not included.
  Default is True.
• retstep : bool, optional
  If True, return (samples, step), where step is the spacing
  between samples.
• dtype : dtype, optional
  The type of the output array. If dtype is not given, infer the data
  type from the other input arguments.

versionadded:: 1.9.0

• axis : int, optional
  The axis in the result to store the samples. Relevant only if start
  or stop are array-like. By default (0), the samples will be along a
  new axis inserted at the beginning. Use -1 to get an axis at the end.

versionadded:: 1.16.0
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, optional
Only returned if retstep is True
Size of spacing between samples.
See Also
--------
arange : Similar to linspace, but uses a step size (instead of the
number of samples).
geomspace : Similar to linspace, but with numbers spaced evenly on a log
scale (a geometric progression).
logspace : Similar to geomspace, but with the end points specified as
logarithms.

Examples

>>> import numpy as np
>>> 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 numpy as np
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')
plt.plot(x2, y + 0.5, 'o')
plt.ylim([-0.5, 1])
plt.show()