blocks|key|3003876|text|为了测量圈复杂度，traceback.org提供了一个很好的工具。该页面还很好地概述了如何解释结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3003877|pylint为%2B1。它在验证是否遵守编码标准(无论是PEP8还是您自己组织的变体)方面做得很好，这最终有助于降低圈复杂度。|3003878|entityMap|0|LINK|mutability|MUTABLE|url|http://www.traceback.org/2008/03/31/measuring-cyclomatic-complexity-of-python-code/|1|http://www.logilab.org/project/pylint|2|http://www.python.org/dev/peps/pep-0008/^0|9|D|0|0|0|6|1|Q|4|2|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]]|C|$]]|$1|D|3|E|5|6|7|V|8|@]|9|@$A|W|B|X|1|Y]|$A|Z|B|10|1|11]]|C|$]]|$1|F|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]]]

For measuring cyclomatic complexity, there's a nice tool available at <a href="http://www.traceback.org/2008/03/31/measuring-cyclomatic-complexity-of-python-code/" rel="noreferrer">traceback.org</a>. The page also gives a good overview of how to interpret the results.

+1 for <a href="http://www.logilab.org/project/pylint" rel="noreferrer">pylint</a>. It is great at verifying adherence to coding standards (be it <a href="http://www.python.org/dev/peps/pep-0008/" rel="noreferrer">PEP8</a> or your own organization's variant), which can in the end help to reduce cyclomatic complexity.

blocks|key|3848322|text|对于圈复杂度，您可以使用radon：https://github.com/rubik/radon|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3848323|(使用pip安装：pip+install+radon)|3848324|此外，它还具有以下功能：|3848325|3848326|原始指标(包括SLOC、注释行、空行等)|unordered-list-item|3848327|Halstead指标(所有索引(在Visual+Studio中使用的索引)|3848328|3848329|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/rubik/radon^0|C|5|I|U|0|0|3|3|9|H|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]]|D|@$9|11|A|12|1|13]]|E|$]]|$1|F|3|G|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|19|8|@]|D|@]|E|$]]|$1|J|3|-4|5|6|7|1A|8|@]|D|@]|E|$]]|$1|K|3|L|5|M|7|1B|8|@]|D|@]|E|$]]|$1|N|3|O|5|M|7|1C|8|@]|D|@]|E|$]]|$1|P|3|-4|5|6|7|1D|8|@]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1E|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X]]]]

For cyclomatic complexity you can use <code>radon</code>: <a href="https://github.com/rubik/radon" rel="noreferrer">https://github.com/rubik/radon</a>

(Use <code>pip</code> to install it: <code>pip install radon</code>)

Additionally it also has these features:

<ul>
<li>raw metrics (these include SLOC, comment lines, blank lines, &amp;c.)</li>
<li>Halstead metrics (all of them)</li>
<li>Maintainability Index (the one used in Visual Studio)</li>
</ul>

blocks|key|3848042|text|对于静态分析，有pylint和pychecker。就我个人而言，我使用pylint，因为它似乎比pychecker更全面。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3848043|对于圈复杂度，您可以尝试this+perl+program，或者这个article，它引入了一个python程序来做同样的事情|3848044|entityMap|0|LINK|mutability|MUTABLE|url|http://www.logilab.org/857|1|http://pychecker.sourceforge.net/|2|http://www.journyx.com/curt/complexity.html|3|http://www.traceback.org/2008/03/31/measuring-cyclomatic-complexity-of-python-code/^0|8|6|0|F|9|1|0|C|H|2|Y|7|3|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]|$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|E|5|6|7|10|8|@]|9|@$A|11|B|12|1|13]|$A|14|B|15|1|16]]|C|$]]|$1|F|3|-4|5|6|7|17|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]|R|$5|I|J|K|C|$L|S]]]]

For static analysis there is <a href="http://www.logilab.org/857" rel="noreferrer">pylint</a> and <a href="http://pychecker.sourceforge.net/" rel="noreferrer">pychecker</a>. Personally I use pylint as it seems to be more comprehensive than pychecker. 

For cyclomatic complexity you can try <a href="http://www.journyx.com/curt/complexity.html" rel="noreferrer">this perl program</a>, or this <a href="http://www.traceback.org/2008/03/31/measuring-cyclomatic-complexity-of-python-code/" rel="noreferrer">article</a> which introduces a python program to do the same

blocks|key|757991|text|当你需要理解一个新项目时，Pycana就像是一种魅力！|type|unstyled|depth|inlineStyleRanges|entityRanges|data|757992|python+PyCAna+(Python+code+Analyzer)是一个用于python的简单代码分析器的别致名称，它可以在执行代码后创建一个类图。|offset|length|757993|757994|看看它是如何工作的：http://pycana.sourceforge.net/|757995|输出：|757996|​|757997|📷|atomic|757998|757999|entityMap|0|LINK|mutability|MUTABLE|url|https://sourceforge.net/projects/pycana/|1|http://pycana.sourceforge.net/|2|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/0e8b99af803ad6f479ae2d1dfad2a3d3.png|imageAlt^0|0|7|6|0|0|0|A|U|1|0|0|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|17|8|@]|9|@$D|18|E|19|1|1A]]|A|$]]|$1|F|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|1C|8|@]|9|@$D|1D|E|1E|1|1F]]|A|$]]|$1|I|3|J|5|6|7|1G|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1H|8|@]|9|@]|A|$]]|$1|M|3|N|5|O|7|1I|8|@]|9|@$D|1J|E|1K|1|1L]]|A|$]]|$1|P|3|L|5|6|7|1M|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|1N|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X]]|Y|$5|T|U|V|A|$W|Z]]|10|$5|11|U|12|A|$13|14|15|-4]]]]

Pycana works like charm when you need to understand a new project!

<blockquote>
 <a href="https://sourceforge.net/projects/pycana/" rel="nofollow noreferrer">PyCAna</a> (Python Code Analyzer) is
 a fancy name for a simple code
 analyzer for python that creates a
 class diagram after executing your
 code.
</blockquote>

See how it works:
<a href="http://pycana.sourceforge.net/" rel="nofollow noreferrer">http://pycana.sourceforge.net/</a>

output:

<a href="https://i.stack.imgur.com/mhEwK.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/mhEwK.png" alt="alt text"></a>

blocks|key|3003839|text|多亏了Pydev，您可以非常轻松地在Eclipse+IDE中使用integrate+pylint，并在每次保存修改后的文件时获得代码报告。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3003840|entityMap|0|LINK|mutability|MUTABLE|url|http://pydev.org/|1|http://www.eclipse.org/|2|http://pydev.org/manual_adv_pylint.html^0|3|5|0|I|B|1|W|G|2|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|-4|5|6|7|Z|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]|N|$5|G|H|I|C|$J|O]]]]

Thanks to <a href="http://pydev.org/" rel="nofollow noreferrer">Pydev</a>, you can <a href="http://pydev.org/manual_adv_pylint.html" rel="nofollow noreferrer">integrate pylint</a> in the <a href="http://www.eclipse.org/" rel="nofollow noreferrer">Eclipse IDE</a> really easily and get a code report each time you save a modified file.

blocks|key|755114|text|使用flake8，它在一个工具中提供了pep8、pyflakes和圈复杂度分析|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|755115|entityMap|0|LINK|mutability|MUTABLE|url|https://pypi.python.org/pypi/flake8^0|2|6|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Use <a href="https://pypi.python.org/pypi/flake8" rel="noreferrer">flake8</a>, which provides pep8, pyflakes, and cyclomatic complexity analysis in one tool

blocks|key|5259540|text|有一个名为CloneDigger的工具可以帮助您查找类似的代码片段。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|5259541|entityMap|0|LINK|mutability|MUTABLE|url|http://clonedigger.sourceforge.net/^0|5|B|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

There is a tool called 
<a href="http://clonedigger.sourceforge.net/" rel="nofollow noreferrer">CloneDigger</a> that helps you find similar code snippets.

blocks|key|5259825|text|为了检查圈复杂度，当然有包。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5259826|安装：|5259827|$+pip+install+--upgrade+mccabe|code-block|syntax|javascript|5259828|用法：|5259829|$+python+-m+mccabe+--min=6+path/to/myfile.py|5259830|注意上面的阈值6。根据this+answer，分数>5可能应该简化。|offset|length|5259831|使用--min=3的示例输出|style|CODE|5259832|68:1:+'Fetcher.fetch'+3
48:1:+'Fetcher._read_dom_tag'+3
103:1:+'main'+3|5259833|它也可以通过pylint-mccabe或pytest-mccabe等方式使用。|5259834|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/1365185/832230|1|https://pypi.python.org/pypi/pylint-mccabe/|2|https://pypi.python.org/pypi/pytest-mccabe/^0|0|0|0|0|0|B|B|0|0|2|7|0|0|6|D|1|K|D|2|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1B|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|1C|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|1D|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|1E|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|1F|8|@]|9|@$O|1G|P|1H|1|1I]]|A|$]]|$1|Q|3|R|5|6|7|1J|8|@$O|1K|P|1L|S|T]]|9|@]|A|$]]|$1|U|3|V|5|F|7|1M|8|@]|9|@]|A|$G|H]]|$1|W|3|X|5|6|7|1N|8|@]|9|@$O|1O|P|1P|1|1Q]|$O|1R|P|1S|1|1T]]|A|$]]|$1|Y|3|-4|5|6|7|1U|8|@]|9|@]|A|$]]]|Z|$10|$5|11|12|13|A|$14|15]]|16|$5|11|12|13|A|$14|17]]|18|$5|11|12|13|A|$14|19]]]]

For checking cyclomatic complexity, there is of course the <a href="https://pypi.python.org/pypi/mccabe/" rel="nofollow noreferrer"><code>mccabe</code></a> package.

Installation:

<pre><code>$ pip install --upgrade mccabe
</code></pre>

Usage:

<pre><code>$ python -m mccabe --min=6 path/to/myfile.py
</code></pre>

Note the threshold of 6 above. Per <a href="https://stackoverflow.com/a/1365185/832230">this answer</a>, scores >5 probably should be simplified.

Sample output with <code>--min=3</code>:

<pre><code>68:1: 'Fetcher.fetch' 3
48:1: 'Fetcher._read_dom_tag' 3
103:1: 'main' 3
</code></pre>

It can optionally also be used via <a href="https://pypi.python.org/pypi/pylint-mccabe/" rel="nofollow noreferrer">pylint-mccabe</a> or <a href="https://pypi.python.org/pypi/pytest-mccabe/" rel="nofollow noreferrer">pytest-mccabe</a>, etc.

I have a large source repository split across multiple projects. I would like to produce a report about the health of the source code, identifying problem areas that need to be addressed.

Specifically, I'd like to call out routines with a high cyclomatic complexity, identify repetition, and perhaps run some lint-like static analysis to spot suspicious (and thus likely erroneous) constructs.

How might I go about constructing such a report?

How can I analyze Python code to identify problematic areas?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个跨多个项目的大型源代码库。我想写一份关于源代码健康状况的报告，找出需要解决的问题区域。具体地说，我想调用具有高度圈复杂度的例程，识别重复，也许还会运行一些类似于lint的静态分析来发现可疑的(因此可能是错误的)构造。我该如何着手构建这样的报告呢？

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