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 ``-inf`` instead of ``0`` when an
integer ``dtype`` is specified. The old behavior can
still be obtained with ``(start, stop, num).astype(int)``
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, 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
``[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
--------
>>> (2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> (2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> (2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import as plt
>>> N = 8
>>> y = (N)
>>> x1 = (0, 10, N, endpoint=True)
>>> x2 = (0, 10, N, endpoint=False)
>>> (x1, y, 'o')
[<.Line2D object at 0x...>]
>>> (x2, y + 0.5, 'o')
[<.Line2D object at 0x...>]
>>> ([-0.5, 1])
(-0.5, 1)
>>> ()
"""
num = (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, . 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 (. 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 _nx.issubdtype(dtype, _nx.integer):
_nx.floor(y, out=y)
if retstep:
return (dtype, copy=False), step
else:
return (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。
可以选择排除间隔的端点。
参数
----------
start：array_like
序列的起始值。
stop：array_like
序列的结束值，除非“endpoint”设置为False。在这种情况下，序列由除最后一个以外的所有``num+1组成``均匀分布的样本，以便排除“停止”。请注意，步骤当“endpoint”为False时，大小会发生变化。
num:int，可选
要生成的样本数。默认值为50。必须是非负的。
端点：bool，可选
如果为True，“stop”是最后一个示例。否则不包括在内。默认是真的。
retstep:bool，可选
如果为True，则返回（'samples'，'step'），其中'step'是间距
在样本之间。
dtype:dtype，可选
输出数组的类型。如果未给出'dtype'，则为数据类型由“开始”和“停止”推断。推断出的数据类型永远不会被删除整数`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]``
（取决于“endpoint”是真是假）。
step : float, optional
仅当'retstep'为真时返回
样本之间的间距大小。
另见
--------
arange：与“linspace”类似，但使用步长（而不是样本数量）。
geomspace：类似于“linspace”，但数字在一根圆木上均匀分布比例（几何级数）。
logspace：类似于“geomspace”，但端点指定为对数。
Examples -------- >>> (2.0, 3.0, num=5) array([2. , 2.25, 2.5 , 2.75, 3. ]) >>> (2.0, 3.0, num=5, endpoint=False) array([2. , 2.2, 2.4, 2.6, 2.8]) >>> (2.0, 3.0, num=5, retstep=True) (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import as plt >>> N = 8 >>> y = (N) >>> x1 = (0, 10, N, endpoint=True) >>> x2 = (0, 10, N, endpoint=False) >>> (x1, y, 'o') [<.Line2D object at 0x...>] >>> (x2, y + 0.5, 'o') [<.Line2D object at 0x...>] >>> ([-0.5, 1])