Python可以打印函数定义吗？

Question

在JavaScript中，可以打印出函数的定义。有没有办法在Python中实现这一点？ （只是在交互模式中玩耍，我想读一个没有打开（）的模块，我只是好奇而已）。

Answer 1

如果要导入的功能，你可以使用inspect.getsource：

>>> import re 
>>> import inspect 
>>> print inspect.getsource(re.compile) 
def compile(pattern, flags=0): 
    "Compile a regular expression pattern, returning a pattern object." 
    return _compile(pattern, flags) 

这将在交互提示工作，但显然只对（交互式提示中定义的不是对象），这些导入的对象。当然，它只会工作，如果Python可以找到源代码（所以没有内置对象，C库，pyc文件等）

Answer 2

如果您使用iPython，您可以使用function_name?得到帮助，并function_name??将打印出的来源，如果可以。

Answer 3

可以使用__doc__关键字：

#print the class description 
print string.__doc__ 
#print function description 
print open.__doc__ 

Answer 4

虽然我通常会同意inspect是一个很好的答案，但我不同意你无法获得解释器中定义的对象的源代码。如果您使用中的dill.source.getsource，则可以获得函数和lambda表达式的源，即使它们是交互式定义的。 它也可以从curries中定义的绑定或未绑定的类方法和函数获取代码...但是，如果没有包含对象的代码，您可能无法编译该代码。

>>> from dill.source import getsource 
>>> 
>>> def add(x,y): 
... return x+y 
... 
>>> squared = lambda x:x**2 
>>> 
>>> print getsource(add) 
def add(x,y): 
    return x+y 

>>> print getsource(squared) 
squared = lambda x:x**2 

>>> 
>>> class Foo(object): 
... def bar(self, x): 
...  return x*x+x 
... 
>>> f = Foo() 
>>> 
>>> print getsource(f.bar) 
def bar(self, x): 
    return x*x+x 

>>> 

Answer 5

可以在函数中使用__doc__，采取hog()功能例如： 你可以看到hog()这样的用法：

from skimage.feature import hog 

print hog.__doc__ 

输出将是：

Extract Histogram of Oriented Gradients (HOG) for a given image. 
Compute a Histogram of Oriented Gradients (HOG) by 

    1. (optional) global image normalisation 
    2. computing the gradient image in x and y 
    3. computing gradient histograms 
    4. normalising across blocks 
    5. flattening into a feature vector 

Parameters 
---------- 
image : (M, N) ndarray 
    Input image (greyscale). 
orientations : int 
    Number of orientation bins. 
pixels_per_cell : 2 tuple (int, int) 
    Size (in pixels) of a cell. 
cells_per_block : 2 tuple (int,int) 
    Number of cells in each block. 
visualise : bool, optional 
    Also return an image of the HOG. 
transform_sqrt : bool, optional 
    Apply power law compression to normalise the image before 
    processing. DO NOT use this if the image contains negative 
    values. Also see `notes` section below. 
feature_vector : bool, optional 
    Return the data as a feature vector by calling .ravel() on the result 
    just before returning. 
normalise : bool, deprecated 
    The parameter is deprecated. Use `transform_sqrt` for power law 
    compression. `normalise` has been deprecated. 

Returns 
------- 
newarr : ndarray 
    HOG for the image as a 1D (flattened) array. 
hog_image : ndarray (if visualise=True) 
    A visualisation of the HOG image. 

References 
---------- 
* http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients 

* Dalal, N and Triggs, B, Histograms of Oriented Gradients for 
    Human Detection, IEEE Computer Society Conference on Computer 
    Vision and Pattern Recognition 2005 San Diego, CA, USA 

Notes 
----- 
Power law compression, also known as Gamma correction, is used to reduce 
the effects of shadowing and illumination variations. The compression makes 
the dark regions lighter. When the kwarg `transform_sqrt` is set to 
``True``, the function computes the square root of each color channel 
and then applies the hog algorithm to the image. 

Answer 6

这是我想出如何去做的方式：

import inspect as i 
    import sys 
    sys.stdout.write(inspect.getsource(MyFunction)) 

这将取出新行字符并很好地打印出该功能