numpy摘抄

小鱼儿 2022-03-08 14:20 240阅读 0赞

为了创建数字序列，NumPy提供了一个类似于`range`返回数组而不是列表的函数 。

>>> np.arange( 10, 30, 5 )
    array([10, 15, 20, 25])

当`arange`与浮点参数一起使用时，由于有限的浮点精度，通常不可能预测所获得的元素的数量。出于这个原因，通常最好使用`linspace`作为参数接收我们想要的元素数量的函数，而不是步骤：

>>>

>>> from numpy import pi
    >>> np.linspace( 0, 2, 9 )                 # 9 numbers from 0 to 2
    array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ,  1.25,  1.5 ,  1.75,  2.  ])

**一维**数组可以被索引，切片和迭代，就像 [列表][Link 1] 和其他Python序列一样。

>>> a = np.arange(12).reshape(3,4)
    >>> b = a > 4
    >>> b                                          # b is a boolean with a's shape
    array([[False, False, False, False],
           [False,  True,  True,  True],
           [ True,  True,  True,  True]])

>>> a[b] = 0                                   # All elements of 'a' higher than 4 become 0
    >>> a
    array([[0, 1, 2, 3],
           [4, 0, 0, 0],
           [0, 0, 0, 0]])

You can look at the following example to see how to use boolean indexing to generate an image of the [Mandelbrot set][]:

>>>

>>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> def mandelbrot( h,w, maxit=20 ):
    ...     """Returns an image of the Mandelbrot fractal of size (h,w)."""
    ...     y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ]
    ...     c = x+y*1j
    ...     z = c
    ...     divtime = maxit + np.zeros(z.shape, dtype=int)
    ...
    ...     for i in range(maxit):
    ...         z = z**2 + c
    ...         diverge = z*np.conj(z) > 2**2            # who is diverging
    ...         div_now = diverge & (divtime==maxit)  # who is diverging now
    ...         divtime[div_now] = i                  # note when
    ...         z[diverge] = 2                        # avoid diverging too much
    ...
    ...     return divtime
    >>> plt.imshow(mandelbrot(400,400))
    >>> plt.show()

### 直方图 ###

`histogram`应用于数组的NumPy 函数返回一对向量：数组的直方图和bin的向量。注意： `matplotlib`还有一个构建直方图的功能（`hist`在Matlab中称为），与NumPy中的直方图不同。主要区别在于`pylab.hist`自动绘制直方图，而 `numpy.histogram`只生成数据。

>>>

>>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2
    >>> mu, sigma = 2, 0.5
    >>> v = np.random.normal(mu,sigma,10000)
    >>> # Plot a normalized histogram with 50 bins
    >>> plt.hist(v, bins=50, density=1)       # matplotlib version (plot)
    >>> plt.show()

![../\_images/quickstart-2\_00\_00.png][.._images_quickstart-2_00_00.png]

>>>

>>> # Compute the histogram with numpy and then plot it
    >>> (n, bins) = np.histogram(v, bins=50, density=True)  # NumPy version (no plot)
    >>> plt.plot(.5*(bins[1:]+bins[:-1]), n)
    >>> plt.show()

[Link 1]: https://docs.python.org/tutorial/introduction.html#lists
[Mandelbrot set]: https://en.wikipedia.org/wiki/Mandelbrot_set
[.._images_quickstart-2_00_00.png]: https://docs.scipy.org/doc/numpy/_images/quickstart-2_00_00.png