关于np.linspace函数
本帖最后由 Python初学者8号 于 2021-1-18 17:17 编辑正在学习matplotlib包
我遇到了linspace函数，我来研就一下他的作用。
这是函数的源文档：
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
```` 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
--------
>>> np.linspace(2.0, 3.0, num=5)
array()
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array()
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array(), 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()
刚开始不是很懂num参数和endpoint概念，以及说明文档中的这句话
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.
这样理解这个参数，也是引用他们自己写的例子，我加了一点
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array()
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array()
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array(), 0.25)
#我加的：
>>>np.linspace(2.0, 3.0, num=6, endpoint=True)
>>>array()
所以作用就是增加num为num+1，然后删除最后一个数，然后输出num个数字，哈哈 linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)
好了，现在说说retstep参数：
首先看看doc的解释：
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
所以，从名字来说，它是returnstep的缩写，作用来说 ，看下面：
>>> c= np.linspace(1, 3, num=3, endpoint=True, retstep=True, dtype=None, axis=0)
>>> c
(array(), 1.0) 哈哈，通过这个 命令找到了源代码了 哈哈
import numpy as np
import inspect
print(inspect.getsource(np.linspace))
static/image/hrline/line7.png
以下就是源代码
num = operator.index(num)
if num < 0:
raise ValueError("Number of samples, %s, must be non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an __array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop= asanyarray(stop)* 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
delta = stop - start
y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
# In-place multiplication y *= delta/div is faster, but prevents the multiplicant
# from overriding what class is produced, and thus prevents, e.g. use of Quantities,
# see gh-7142. Hence, we multiply in place only for standard scalar types.
_mult_inplace = _nx.isscalar(delta)
if div > 0:
step = delta / div
if _nx.any(step == 0):
# Special handling for denormal numbers, gh-5437
y /= div
if _mult_inplace:
y *= delta
else:
y = y * delta
else:
if _mult_inplace:
y *= step
else:
y = y * step
else:
# sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
# have an undefined step
step = NaN
# Multiply with delta to allow possible override of output class.
y = y * delta
y += start
if endpoint and num > 1:
y[-1] = stop
if axis != 0:
y = _nx.moveaxis(y, 0, axis)
if retstep:
return y.astype(dtype, copy=False), step
else:
return y.astype(dtype, copy=False)
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