在JavaScript中,可以打印出函数的定义。有没有办法在Python中实现这一目标?
(只是在交互模式下玩游戏,我想在没有打开的情况下读取模块()。我只是很好奇)。
答案 0 :(得分:122)
如果要导入该功能,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)
此将在交互式提示中工作,但显然仅适用于导入的对象(不是交互式提示中定义的对象)。当然,只有Python可以找到源代码(因此不能在内置对象,C库,.pyc文件等上),它才会起作用。
答案 1 :(得分:83)
如果您使用的是iPython,则可以使用 function_name?
获取帮助, function_name??
将打印出来来源,如果可以的话。
答案 2 :(得分:8)
虽然我普遍认为inspect
是一个很好的答案,但我不同意您无法获得解释器中定义的对象的源代码。如果您使用dill
中的dill.source.getsource
,则可以获得函数和lambdas的来源,即使它们是以交互方式定义的。
它还可以从curries中定义的绑定或非绑定类方法和函数中获取代码...但是,如果没有封闭对象的代码,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
>>>
答案 3 :(得分:6)
这是我弄清楚如何做的方式:
import inspect as i
import sys
sys.stdout.write(i.getsource(MyFunction))
这将取出新的行字符并很好地打印出该函数
答案 4 :(得分:0)
使用help(function)
获取功能说明。
答案 5 :(得分:-5)
您可以使用__doc__关键字:
#print the class description
print string.__doc__
#print function description
print open.__doc__
答案 6 :(得分:-5)
您可以在函数中使用__doc__
,以hog()
函数为例:
你可以看到hog()
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.