Performing static analysis with Pyflakes

We will perform static analysis of a part of the NumPy codebase. In order to do this, we will check out the code using Git. We will then run static analysis on part of the code using pyflakes.

How to do it...

  1. Check out the code.

    To check out the NumPy code, we will need Git. Installing Git is outside the scope of this book. The Git command to retrieve the code is as follows:

    git clone git://github.com/numpy/numpy.git numpy

    Alternatively, we can download a zip archive from https://github.com/numpy/numpy .

  2. Analyze the code.

    The previous step should have created a numpy directory with all the NumPy code. Go to this directory, and within it run the following command:

    $ pyflakes *.py
pavement.py:71: redefinition of unused 'md5' from line 69
pavement.py:88: redefinition of unused 'GIT_REVISION' from line 86
pavement.py:314: 'virtualenv' imported but unused
pavement.py:315: local variable 'e' is assigned to but never used
pavement.py:380: local variable 'sdir' is assigned to but never used
pavement.py:381: local variable 'bdir' is assigned to but never used
pavement.py:536: local variable 'st' is assigned to but never used
setup.py:21: 're' imported but unused
setup.py:27: redefinition of unused 'builtins' from line 25
setup.py:124: redefinition of unused 'GIT_REVISION' from line 118
setupegg.py:17: 'setup' imported but unused
setupscons.py:61: 'numpy' imported but unused
setupscons.py:64: 'numscons' imported but unused
setupsconsegg.py:6: 'setup' imported but unused

    This will run analysis on the code style, and check for PEP-8 violations in all the Python scripts within the current directory. You can also analyze a single file if you prefer.

How it works...

As you can see, it is pretty simple to analyze code style and look for PEP-8 violations with Pyflakes. The other advantage is speed; however, the number of error types that Pyflakes reports is pretty limited.

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