numpy 函数一：linspace

接触 numpy 遇到的第一个函数可能就是 linspace 函数，但是对于我们这种没有学过 matlab 的人来说，根本不知道这是什么。

所以只能自己查资料。

词典显示：

线性等分向量
线性平分矢量
线性平分向量

那么怎么用呢？

linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
    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.

    Parameters
    ----------
    start : scalar
        The starting value of the sequence.
        样本起始值
    stop : scalar
        The end value of the sequence, unless `endpoint` is set to False.
        样本终止值
        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
    	样本个数
        Number of samples to generate. Default is 50.
    endpoint : bool, optional
        If True, `stop` is the last sample. Otherwise, it is not included.
        Default is True.
        如果为真，则最后一个值（stop对应的值）包含在样本中
    retstep : bool, optional
        If True, return (`samples`, `step`), where `step` is the spacing
        between samples.
        如果为真，返回样本及步长
    dtype : dtype, optional
        The type of the output array.  If `dtype` is not given, infer the data
        type from the other input arguments.
        样本的数据类型

        .. versionadded:: 1.9.0

    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
        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).
    logspace : Samples uniformly distributed in log space.

例：

import numpy as np
import scipy as sp
import pylab as pl

# 从0到4pi之间，取100个样点
x = np.linspace(0,4*np.pi,100)

pl.plot(x,np.sin(x))
pl.show()

结果：