def 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.
.. versionchanged:: 1.20.0
Values are rounded towards endpoint is set to False.
In that case, the sequence consists of all but the last of 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, the data type
is inferred from start and stop. The inferred dtype will never be
an integer; float is chosen even if the arguments would produce an
array of integers.
.. 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
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([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')
[]
>>> plt.plot(x2, y + 0.5, 'o')
[]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
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 1:
y[-1] = stop
if axis != 0:
y = _nx.moveaxis(y, 0, axis)
if _nx.issubdtype(dtype, _nx.integer):
_nx.floor(y, out=y)
if retstep:
return y.astype(dtype, copy=False), step
else:
return y.astype(dtype, copy=False)
返回指定间隔内的等距数字。
返回”num”均匀分布的样本，在 间隔[‘start’，’stop]。返回指定间隔内的等距数字。</strong></p>
<p><strong>可以选择排除间隔的端点。</strong></p>
<p><strong>----------</strong></p>
<p><strong>start：</strong> <strong>array_like</strong></p>
<p><strong>序列的起始值。</strong></p>
<p><strong>stop：</strong> <strong>array_like</strong></p>
<p><strong>序列的结束值，除非"endpoint"设置为False。</strong> <strong>在这种情况下，序列由除最后一个以外的所有<code>num+1组成</code></strong> <strong>均匀分布的样本，以便排除"停止"。请注意，步骤</strong> <strong>当"endpoint"为False时，大小会发生变化。</strong></p>
<p><strong>num:int，可选</strong></p>
<p><strong>要生成的样本数。默认值为50。必须是非负的。</strong></p>
<p><strong>端点：bool，可选</strong></p>
<p><strong>如果为True，"stop"是最后一个示例。否则不包括在内。</strong> <strong>默认是真的。</strong></p>
<p><strong>retstep:bool，可选</strong></p>
<p><strong>如果为True，则返回（'samples'，'step'），其中'step'是间距</strong></p>
<p><strong>在样本之间。</strong></p>
<p><strong>dtype:dtype，可选</strong></p>
<p><strong>输出数组的类型。如果未给出'dtype'，则为数据类型</strong> <strong>由"开始"和"停止"推断。推断出的数据类型永远不会被删除</strong> <strong>整数即使参数会产生 整数数组。
轴：int，可选
结果中用于存储样本的轴。只有在启动时才相关或停止是阵列式的。默认情况下（0），样本将沿着开始处插入新轴。使用-1在末尾获得一个轴。
Returns
——-
samples ：Ndaray
在闭合间隔中有’num’等间距的样本
[start，stop]或半开区间或[start，stop]</code><br> （取决于"endpoint"是真是假）。</strong></p>
<p><strong>step : float, optional<br> 仅当'retstep'为真时返回<br> 样本之间的间距大小。<br> 另见<br>--------<br>arange：与"linspace"类似，但使用步长（而不是样本数量）。<br>geomspace：类似于"linspace"，但数字在一根圆木上均匀分布比例（几何级数）。<br>logspace：类似于"geomspace"，但端点指定为对数。</strong></p>
<p>
Examples
Original: https://blog.csdn.net/weixin_53660567/article/details/123160542
Author: 长沙有肥鱼
Title: python——linspace函数
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