为什么numpy.r_使用括号而不是括号? [英] Why does numpy.r_ use brackets instead of parentheses?

问题描述

Numpy.r _ 、. c_和.s_是我遇到的仅有的Python函数，它们使用方括号而不是括号作为参数.为什么会这样呢?这些功能有什么特别之处吗?我可以制作自己的使用方括号的功能(不是我想要的；只是出于好奇)?

Numpy.r_, .c_ and .s_ are the only Python functions I've come across that take arguments in square brackets rather than parentheses. Why is this the case? Is there something special about these functions? Can I make my own functions that use brackets (not that I want to; just curious)?

例如，正确的语法是:

    np.r_['0,2', [1,2,3], [4,5,6]]

我希望它是:

    np.r_('0,2', [1,2,3], [4,5,6])

推荐答案

可以制作任何Python类，使其实例接受一个或两个符号:它将通过实现称为__call__的函数接受括号，并通过实现将括号括起来__getitem__.

Any Python class can be made so that its instances accept either or both notation: it will accept parens by implementing a function called __call__, and brackets by implementing __getitem__.

np.r_恰好是一个实现__getitem__的类，它比平时做的更好.也就是说，r_的类(称为np.lib.index_tricks.RClass)执行以下操作:

np.r_ happens to be of a class that implements __getitem__ to do fancier things than its usual. That is, the class of r_ (called np.lib.index_tricks.RClass) does something like this:

class RClass:
    def __getitem__(self, item):
        # r_ fancyness

这样做是为了使它可以利用切片符号-例如，当您有一个列表(或np数组或实现此协议的任何其他对象)时，您这样做:

Likely, this was done so that it can take advantage of slice notation - eg, when you have a list (or np array or any other object implementing this protocol) l, and you do:

l[:5]

，Python自动创建一个slice对象以传递给__getitem__.

, Python automatically creates a slice object to pass to __getitem__.

此语法不与__call__一起使用-用户必须通过执行l(slice(5))显式创建切片.

This syntax doesn't work with __call__ - a user would have to create the slice explicitly, by doing l(slice(5)).

请注意，__call__可以接受您喜欢的任何参数.而__getitem__始终只使用一个参数:当您执行类似my_array[1:3, 2:5]的操作时，Python会传入一个单独的 tuple 切片.但是，正如您在r_中看到的那样，内容并不仅限于数字和切片-与任何其他函数类似，Python会很乐意传入任何对象并将其留给类以弄清楚其含义.

Note that __call__ can take whatever arguments you like; while __getitem__ always takes exactly one argument: when you do something like my_array[1:3, 2:5], Python passes in a single tuple of slices. But, as you see with r_, the contents aren't restricted to numbers and slices - similarly to any other function, Python will happily pass in any object and leave it to the class to work out what it means.

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