如何提取python代码文件中使用的函数? [英] How to extract functions used in a python code file?

问题描述

我想创建代码文件中使用的所有功能的列表.例如，如果我们在名为"add_random.py"的文件中包含以下代码

I would like to create a list of all the functions used in a code file. For example if we have following code in a file named 'add_random.py'

`

import numpy as np
from numpy import linalg

def foo():
    print np.random.rand(4) + np.random.randn(4)
    print linalg.norm(np.random.rand(4))

`

我想提取以下列表: [numpy.random.rand, np.random.randn, np.linalg.norm, np.random.rand]

I would like to extract the following list: [numpy.random.rand, np.random.randn, np.linalg.norm, np.random.rand]

该列表包含代码中使用的函数，其实际名称形式为'module.submodule.function'.有使用python语言构建的东西可以帮助我做到这一点吗?

The list contains the functions used in the code with their actual name in the form of 'module.submodule.function'. Is there something built in python language that can help me do this?

推荐答案

您可以使用以下方法提取所有调用表达式:

You can extract all call expressions with:

import ast

class CallCollector(ast.NodeVisitor):
    def __init__(self):
        self.calls = []
        self.current = None

    def visit_Call(self, node):
        # new call, trace the function expression
        self.current = ''
        self.visit(node.func)
        self.calls.append(self.current)
        self.current = None

    def generic_visit(self, node):
        if self.current is not None:
            print "warning: {} node in function expression not supported".format(
                node.__class__.__name__)
        super(CallCollector, self).generic_visit(node)

    # record the func expression 
    def visit_Name(self, node):
        if self.current is None:
            return
        self.current += node.id

    def visit_Attribute(self, node):
        if self.current is None:
            self.generic_visit(node)
        self.visit(node.value)  
        self.current += '.' + node.attr

将其与ast解析树一起使用:

Use this with a ast parse tree:

tree = ast.parse(yoursource)
cc = CallCollector()
cc.visit(tree)
print cc.calls

演示:

>>> tree = ast.parse('''\
... def foo():
...     print np.random.rand(4) + np.random.randn(4)
...     print linalg.norm(np.random.rand(4))
... ''')
>>> cc = CallCollector()
>>> cc.visit(tree)
>>> cc.calls
['np.random.rand', 'np.random.randn', 'linalg.norm']

上述助行器仅处理名称和属性；如果您需要更复杂的表达式支持，则必须扩展它.

The above walker only handles names and attributes; if you need more complex expression support, you'll have to extend this.

请注意，收集这样的名称并非易事.任何间接都不会被处理.您可以在函数代码中构建一个字典来调用和动态交换函数对象，而上面的静态分析将无法跟踪它.

Note that collecting names like this is not a trivial task. Any indirection would not be handled. You could build a dictionary in your code of functions to call and dynamically swap out function objects, and static analysis like the above won't be able to track it.

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