python——linspace函数

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 ``np.linspace(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
    --------
    >>> 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 <= 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 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。

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
--------
>>> 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])