知识问答

Python可以打印函数定义吗？

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问题：Python可以打印函数定义吗？
回答 0
回答 1
回答 2
回答 3
回答 4
回答 5
回答 6

问题：Python可以打印函数定义吗？

在JavaScript中，可以打印出函数的定义。有没有办法在Python中完成此任务？

（只是在交互模式下玩，所以我想在没有open（）的情况下读取模块。我只是很好奇。）

点击查看英文原文

In JavaScript, one can print out the definition of a function. Is there a way to accomplish this in Python?

(Just playing around in interactive mode, and I wanted to read a module without open(). I was just curious).

回答 0

如果要导入功能，则可以使用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)

这将在交互式提示中起作用，但显然仅适用于导入的对象（不适用于交互式提示中定义的对象）。当然，只有在Python可以找到源代码的情况下它才会起作用（因此不能在内置对象，C库，.pyc文件等上找到）

点击查看英文原文

If you are importing the function, you can use 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)

This will work in the interactive prompt, but apparently only on objects that are imported (not objects defined within the interactive prompt). And of course it will only work if Python can find the source code (so not on built-in objects, C libs, .pyc files, etc)

回答 1

如果您使用的是iPython，则可以使用function_name?获得帮助，并且function_name??可以打印出源代码。

点击查看英文原文

If you’re using iPython, you can use function_name? to get help, and function_name?? will print out the source, if it can.

回答 2

这是我想出的方法：

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

这样可以取出换行符并很好地打印功能

点击查看英文原文

This is the way I figured out how to do it:

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

This takes out the new line characters and prints the function out nicely

回答 3

尽管我通常会认为这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。如果使用dill.source.getsourcefrom dill，即使它们是交互式定义的，也可以获取函数和lambda的来源。它也可以从咖喱中定义的绑定或未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，则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

>>>
点击查看英文原文

While I’d generally agree that inspect is a good answer, I’d disagree that you can’t get the source code of objects defined in the interpreter. If you use dill.source.getsource from dill, you can get the source of functions and lambdas, even if they are defined interactively. It also can get the code for from bound or unbound class methods and functions defined in curries… however, you might not be able to compile that code without the enclosing object’s code.

>>> 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

>>>

回答 4

用help(function)得到的功能说明。

您可以help() 在此处了解更多信息。

点击查看英文原文

Use help(function) to get the function description.

You can read more about help() here.

回答 5

您可以使用__doc__关键字：

#print the class description
print string.__doc__
#print function description
print open.__doc__
点击查看英文原文

You can use the __doc__ keyword:

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

回答 6

您可以__doc__在函数中使用，以hog()函数为例：您可以看到这样的用法hog()：

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.
点击查看英文原文

You can use the __doc__ in the function, take hog() function as example: You can see the usage of hog() like this:

from skimage.feature import hog

print hog.__doc__

The output will be:

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.